Posts | Green Deal Data Observatory

New Data Curators Wanted

Wed, 09 Nov 2022 11:46:00 +0100

A data curator is a contributor in our open collaboration who will be named as a co-creator of tidy, standardized, reusable, FAIR, datasets in his/her field of expertise. Our curators help us vocalize the needs of their domain, be it data-driven beekeeping, or detecting algorithmic biases of recommender systems, and evaluates if the data that we come up with is directly usable and actionable. A data curator is a similar co-author as a “contributor” to open source software or a co-author of a journal article.

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Boost your career without a conflict of interest

Being a data curator does not mean a commercial affiliation with any observatory partners, it is an affiliation to jointly create intellectual property. All our data curators are identified by their ORCiD ideas and named as co-creators in the open science repositories where we make our data available.

We create CC0 data that can be used for commercial, academic, and policy purposes. However, we want to honor the intellectual investment into a shared intellectual property by

delaying the release (for remaining competitive in academic publishing, if our curator is using the data in new articles; or NGOs for their campaign)
creating hybrid assets for commercial users where some elements, particularly the ones that use their proprietary data, may not become open data.

FAIR: Findable, Accessible, Interoperable, and Reusable Digital Assets

Our observatories do not only work with open data.

We want to make data findable, interoperable (to be used in many applications), accessible, and eventually reusable (FAIR), but that does not mean that all data used must be free. Creating and especially regularly updating high-quality data assets requires plenty of intellectual and monetary investment.

We gladly add commercially available data to our observatory if we can share a large enough subset that our peer-reviewers can attest to the data’s high quality, usability, and actionability.

How to become a data curator?

Our handbook for curators a bit of a work in progress, but the onboarding process is clear. Do not worry if you do not use GitHub, it is not necessary, but we story and co-create our assets, including the curator’s handbook on this digital co-working place.

This is an open book that we co-create on GitHub, and if you find any roadblocks, you do not understand something, or have a better idea on how to illustrate or explain things, just make a for to this repo, improve it, add new photos, and send us a pull request. (You need an invite first for editing!)
Here is a starter repository on GitHub. Not mandatory, but if you use GitHub, start here.

In a nutshell:

Please read the entire covenant here.
We need a very brief biography. Name, affiliation, education details, one-line and short biography. Please, send back this bio_template.txt text file. If you know markdown, use this version. The files are identical, but your word processor may not know how to open an .md file.
Your ORCiD to resolve ambiguity with similarly named people. You may use different library or publication service IDs, such as Google Scholar, Publeon, etc, you may provide them, too, but we do need an ORCiD ID, because most of the EU open science infrastructure and the R ecosystem uses this one. If you do not have it, please create one—it only takes a few minutes. Please add it to the bio_template.txt.
Your LinkedIn ID, add it to the bio_template.txt.
You should follow our file naming conventions, and avoid the use of special characters in any file names at all times: , $, :,;,,,., ", ' tick or backtick.
You must send a ile picture that is at least 500px wide (jpg or png format.) It can be bigger, and preferably not a very “narrow” cut, as all avatars will be behind a circular mask (see other curators.)

Find inspiration from other contributors

Why data observatories?

Our data observatories (platform products) cover our R&D and platform costs while giving us access to an expanding range of prime clients. We use 21-st century open-source data engineering solutions, a decentralized data governance method, and web 3.0 technologies to avoid conflicts of interest and prevent the data Sisyphus of error-prone human data wrangling. There is little competition on this service level (there are about 60 UN/EU/OECD recognized data observatories, and almost all of them are managed by a different operator.) This layer is already monetized, and we have proven success. Our unique advantage is a combination of legal and technological skills: understanding legally open data, web 3.0, and data modeling, and the ability to participate in the open-source statistical /scientific software creator community.
We create open-source software applications that fuel our data observatories with unprocessed, open, linked data. We create software for the R statistical environment, which is used in both official statistics and in many business and academic organizations. The production of R software components is a competitive field, but we believe that our position is strong: the vast majority of R packages are lightly or not at all serviced because of the lack of financing.

We provide bespoke analytics solutions to our institutional partners in our data observatories. Such bespoke solutions iterate over our existing software components, helping us design better applications within an ever-expanding ecosystem. Providing tailored data-science services would require a large organization without a clear focus. We provide these services on an ad-hoc basis only among institutional partners and users of our data observatories. In these circles, which are often prime clients, we face little or no competition because we are trusted partners and data and solution providers. This is a key to our revenue and market growth.
We develop high-value software-as-service applications that leverage our data observatory assets and our software solution into a novel, commercially valuable uses. Our applications are built around our family of open-source software and generalize our bespoke analytics solutions. We are in a late prototype phase where we already have some revenue and are trying to prepare for scaling up at the correct price with three of our applications. All of our applications are entering into highly competitive market segments. We are building on our ‘unfair’ advantage that we are bundling our solutions with data that is not accessible to competitors, and we can test them in the protected ecosystems of our observatories.

Good to know

FAIR Principles: improve the Findability, Accessibility, Interoperability, and Reuse of digital assets.
DataCite: A persistent, standardized approach to access, identification, sharing, and re-use of datasets—this is our favored way of describing data for future use according to the FAIR principles. Many EU open science repositories will ask your publications with this documentation.
Biblatex is a standard text file used by citation engines, bibliography management tool, and in scientific publication templates. (See for example the Overleaf Biblatex tutorial.
Dublin Core is an older international standard than DataCite, but the two standards greatly overlap. Dublin Core was originally developed by libraries. You often may need to fill out Dublin Core properties for publication.

Watch Our 2-min Introduction

⚙️/ Subtitles/ 🇳🇱 🇬🇧 🇧🇦 🇨🇿 🇭🇺 🇩🇪 🇱🇹 🇫🇷 🇸🇰 🇪🇸 🇹🇷 + Catalan.

Vote Reprex :)

Go to Cast your vote for The Hague Innovators challenge 2022! and choose Reprex :)

Big Data for All: Building Collaborative Data Observatories

Thu, 03 Nov 2022 17:30:00 +0000

Reprex’s co-founder, Daniel Antal talked in the Eindhoven Innovation Café about these issues. You can watch the recorded version of the the livestream that starts at 5 minutes and 22 seconds:

This is a past event. Check out our forthcoming events or write to Daniel Antal or to antaldaniel. Or send an email.

The event invitation text and links

Big data and AI creates inequalities. It puts historically marginalized people, like ethnic minorities, and womxn, at a disadvantage. Because AI and checking on AI require plenty of data, usually only giant corporations, the wealthiest governments, and university entities can make it work for them. Reprex is a Hague-based, international startup that wants to impact various sustainable development goals by enabling smaller organizations to join their smaller datasets, use open data, create linked available data, and collaboratively make a change.

Reprex is a finalist for the Hague Innovation Award for impact startup (please 🙏, vote for us!). Daniel Antal, one of the co-founders, will talk about their approach to building an international coalition of music organizations to pool data and challenge data monopolies using organizational techniques, a collaboration ethos, and data from the open-source developer world.

Using the example of independent music creators, who often find themselves in a position where it is more expensive to claim their money from global platforms, he will talk about how to reduce inequalities in the world of big data and AI with collaboration on web 3.0. In the Q&A he will take questions on how to apply their know-how, and generally linked open data to other art+tech or creative segments or problems for which everybody is too small, like meeting the Paris Accord greenhouse gas targets bit by bit, small company by small company.

In the Q&A, we can discuss many things

How can Reprex help an individual creator in music, or in fashion and design, or any other area?
What sort of help it can give to researchers, research institutes, specialist consultancies, law firms, and other knowledge-based actors?

What sort of partners is Reprex looking for in Eindhoven?

Check out our projects

Digital Music Observatory and Listen Local
Cultural & Creative Sectors and Industries Observatory and short call for potential partners.
Green Deal Data Observatory and simple, connected, financial and sustainability reporting for creative enterprises and others

Reprex: the impact startup

Check out our accomplishments since the foundation in 2020

BeeSage: Data-driven Beekeeping for Productivity and Sustainability

Mon, 31 Oct 2022 12:00:00 +0000

BeeSage is an early stage startup which is contesting the The Hague Innovators Award in the pre-startup category. They are evangelizing data-driven beekeeping for productivity and sustainability, Reprex is an impact scale-up in the other category of this competition for more mature startups that is building collaborative, open scholarly data infrastructure, so-called data observatories to support the needs of various data-driven policy, business, or scientific innovation.

We met in the ImpactCity initiative of The Hague, and we’d like to build on The Hague impact startup ecosystem by combining our respective strengths so we decided to join forces! We both want to win in The Hague Innovators’ Challenge in 2022, but we only compete for the votes of the audience. Through our cooperation, we would like to increase the viability of BeeSage in the pre-startup category and increase the value proposition of Reprex in the startup category to the jury in ImpactFest.

Remote team of engineers in the Netherlands, Latvia and Portugal.

BeeSage modular beehive monitoring system boosts productivity and honey yield to benefit the Earth through data-driven pollination. Their software and hardware such as Smart Beehive Scales help beekeepers mitigate risks and enable informed decisions, while turning every apiary into a weather station.

They are also building HiveMap as a data analytics software, which enables beekeeper associations, environmental organizations and other stakeholders to turn beehive and remove sensing data into valuable insights. This product can be greatly enhanced through the latest data from Europe’s Copernicus satellites, from meteorological and air pollution sources. This is where Reprex’s Green Deal Data Observatory comes into the picture.

BeeSage is building HiveMap as a data analytics software.

Beekeepers associations, small research groups, or Ecotech startups cannot afford to build a full team of data engineers, data scientists, and statisticians to tap into components of raw inflation data (to find honey price or food value chain data), to create validated and continuously maintained data pipelines from environmental satellites, the data warehouses of Eurostat and the European Environmental Agency. They cannot hire small-area statisticians and ecological regression experts to create ecological and key business indicators for small tracts of land that are directly relevant to the health of a honeybee colony.

Reprex produces open-source scientific software, and various collaborative data engineering infrastructures to get legally open governmental data and open science data in a timely, usable format to ecological researchers, beekeeper associations, and Ecotech startups like BeeSage. The European Union legally opened up vast arrays of scientific and governmental data sources for commercial and scientific reuse, but investment into re-processing and validating data that was originally collected for a different primary cause requires plenty of private investment.

Reprex’s data observatories, particularly the Green Deal Data Observatory are public-private partnerships that foster the collective collection, processing, peer-review, and reuse of novel big data, like BeeSage’s beehive data, and reusable statistical and environmental data. We hope to place the permanent institution of this PPP in the Hague, which is already the World’s Smartest City, and which wants to remain a global centre of excellence of peace, justice, and sustainability in the era of big data and AI.

Reprex: Big Data For All

Mon, 31 Oct 2022 12:00:00 +0000

Reprex is the Hague-based impact startup developing decentralized, modern, web 3.0-compatible data observatories. Our mission is to fulfill parts of the SDG 16 and 17 goals: based on the open collaboration method of open-source software development and open knowledge management, we would like to enable impact makers to contribute to other SDG goals by making AI and big data work for them.

Table of Contents

Watch Our 2-min Introduction

⚙️/ Subtitles/ 🇳🇱 🇬🇧 🇧🇦 🇨🇿 🇭🇺 🇩🇪 🇱🇹 🇫🇷 🇸🇰 🇪🇸 🇹🇷 + Catalan.

How To Vote?

Go to Cast your vote for The Hague Innovators challenge 2022! and choose Reprex :)

Make sure you select Reprex and write in your email (it is safe here.) You need to tick `✅ I’m not a robot’ to be able to select companies. Further instructions 🇬🇧 here 🇭🇺 magyarul itt.

What do we do in the European Green Deal?

The Green Deal Data Observatory was our first test to bring our information management, data access, and processing know-how to new data sources (such as environmental satellite data, hydrological data, etc.) after music.
Due to the mainstreaming of SDG and ESG reporting, many features are developed with our music partners and overlap with the Competition Data Observatory.
We are looking for reliable data that can be processed into computational antitrust and policy evidence in relationship with SDG 11 to enhance inclusive and sustainable urbanization, SDG 12 to ensure sustainable consumption and production patterns, SDG 13 to take urgent action to combat climate change and its impacts within the partnership approach of SDG 17.

Product

Our data observatories (platform products) cover our R&D and platform costs while giving us access to an expanding range of prime clients. We use 21-st century open-source data engineering solutions, a decentralized data governance method, and web 3.0 technologies to avoid conflicts of interest and prevent the data Sisyphus of error-prone human data wrangling. There is little competition on this service level (there are about 60 UN/EU/OECD recognized data observatories, and almost all of them are managed by a different operator.) This layer is already monetized, and we have proven success. Our unique advantage is a combination of legal and technological skills: understanding legally open data, web 3.0, and data modeling, and the ability to participate in the open-source statistical /scientific software creator community.
We create open-source software applications that fuel our data observatories with unprocessed, open, linked data. We create software for the R statistical environment, which is used in both official statistics and in many business and academic organizations. The production of R software components is a competitive field, but we believe that our position is strong: the vast majority of R packages are lightly or not at all serviced because of the lack of financing.

We provide bespoke analytics solutions to our institutional partners in our data observatories. Such bespoke solutions iterate over our existing software components, helping us design better applications within an ever-expanding ecosystem. Providing tailored data-science services would require a large organization without a clear focus. We provide these services on an ad-hoc basis only among institutional partners and users of our data observatories. In these circles, which are often prime clients, we face little or no competition because we are trusted partners and data and solution providers. This is a key to our revenue and market growth.
We develop high-value software-as-service applications that leverage our data observatory assets and our software solution into a novel, commercially valuable uses. Our applications are built around our family of open-source software and generalize our bespoke analytics solutions. We are in a late prototype phase where we already have some revenue and are trying to prepare for scaling up at the correct price with three of our applications. All of our applications are entering into highly competitive market segments. We are building on our ‘unfair’ advantage that we are bundling our solutions with data that is not accessible to competitors, and we can test them in the protected ecosystems of our observatories.

Plans in The Hague?

Our message is simple: doing business and doing good for the city of the Hague means a vote for Reprex. We would like to win the Hague Innovators Challenge in 2022 because we believe we could multiply our growth in partnership with the Hague. We have a significant budget to develop our observatories, and our company is already located in the Hague, in Apollo 14—but most of our team members, not to mention the observatory’s non-data personnel are not based in our beautiful and smart city. The observatories are important platforms for our growth, and they could create a lot more jobs and impact in the city than in our startup company. Should we win the prize, we would spend the 25,000 euros on one thing: to develop our observatories into a real public-private partnership in the Hague, with a permanent office in Apollo 14 or the Hague Humanity Hub.

Please Choose Reprex in The Hague Innovators Award Online Vote

Sat, 29 Oct 2022 16:17:00 +0200

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🇭🇺 magyarul

How to vote?

Go to Cast your vote for The Hague Innovators challenge 2022!

Make sure you select Reprex and write in your email (it is safe here.) You need to tick `✅ I’m not a robot’ to be able to select companies.

Your vote is not final yet, you must click on a confirmation link to prove that it was you who voted. Go to your email. (Your email is only recorded to avoid double voting, they will not add your address to any marketing databases.)

Please make sure you chose Reprex and click on link to the confirmation your vote.

You receive a text that your code is recorded and you have nothing else to do. (Your address is safe with the municipality of The Hague.)

Thank you very much!

Please share our video message on YouTube among your colleagues and friends.

By pressing the ⚙️ and choosing subtitles you can choose your language. If you are there, please leave a 👍, too :)

The message is the message! We are an ethical data and AI company, and one of our topics is detecting if algorithms are biased towards the English language speakers. We want to teach the computer to understand small languages, and of course, everyone, who is under-represented in data: womxn, former colonial nations.

🇳🇱 🇬🇧 🇧🇦 🇨🇿 🇭🇺 🇩🇪 🇱🇹 🇫🇷 🇸🇰 🇪🇸 🇹🇷 + Catalan.

2 Retweet our appeal from one of our observatory Twitter accounts. For music audiences:

💜 We ask you, humbly, to support us with a vote or by sharing our appeal. We're part of the #opensource, #opendata, and #openscience movement that depends on your support to stay online and thriving, but many of our users or simply look the other way🤦🏻‍♀️https://t.co/Qcdh7saPpW
— Competition Data Observatory (@CompDataObs) November 6, 2022

The same message forgeneral cultural audiences: @CultDataObs; for music audiences: @DigitalMusicObs, for green audeinces @GreenDealObs

Like our LinkedIn page and share our appeal.
Or just send the link to this post from the browser your colleagues and friends.

Why vote for us?

We are finalists in The Hague Innovation Awards with a product offering and a message that big data and AI should work for all: ethnic minorities, small nations, small languages, womxn. We are measuring why certain artists are not getting recommended and paid on global streaming platforms, or why NGOs do not find the right data about fighting greenwashing. We want to help small businesses, civil society organizations, and NGOs who cannot hire a data engineer and a data scientist to fight data monopolies. Who cannot defend themselves from the dark patterns of greedy algorithms?

Get in touch

Check out our events or write to Daniel Antal or to antaldaniel. Or send an email. Thank you!

How to Find Locations for Things That Save Waste Heat?

Mon, 24 Oct 2022 16:18:00 +0200

Peter Rosbjerg: The Loss of Winter….

Europe is preparing for the coldest winter since the second world war. We must conserve energy, and use every particle of gas, sunshine, and wind to heat our homes, schools, and hospitals. This is a great moment to give new inventors who want to save wasted energy a chance. Our Green Deal Data Observatory would like to help Thermowatt in identifying the best possible city locations to implement their ground breaking technology that turns the heat stranded in our sewage networks into city heating. Thermowatt is addressing one of the most mind blowing sources of wastes that we cause and don’t utilize in a city context.

Washing machines, showers, and even floor-wiping buckets are full of water that is significantly and constantly warmer than the cold European winter. We know well from our studies in elementary school physics that it is at least theoretically possible to utilize the heat going down the sink from our houses as heat. Maybe it is even possible to conserve it for future use. This is how geothermal energy works for heat and power generation. This is exactly what heat pumps do in many houses. But how could we install heat pumps into an 8-story-high residential building in the Hague? How could we save this wasted heat?

Converting the inner energy of lukewarm or warm water into hot water is theoretically possible, the question is, what would be necessary to make this economic in everyday life? Saving the energy wasted from a washing machine or a shower would be most likely to succeed if we would not need to convert to electricity (the conversion always leads to a loss of much energy due to the inefficiency of the conversion) and use the energy of the warm water for heating. We need to find places where there is an abundant use of lukewarm water in the sewage and there is a stable need for heat nearby. It also helps if the potential buyer has long-term contracting credibility. To install a pump that will, drop by drop, save energy from lukewarm water will need years of operation to turn economically profitable.

The inventor of Thermowatt says that his invention will pump back hot water from the sewage if the sewage mainline is not far from the location of the waste (the sewage is not yet cold) and the buyer of the energy is nearby. Searching for such locations is exactly what our Green Deal Data Observatory wants to facilitate. We want to find house complexes in Europe that need heating on many days (for example, a hospital in a relatively Nordic country) close to a sewage system that is close enough to industry or residential zones with a large quantity of lukewarm, low-heat water. This is not a simple Google search!

Thermowatt in the Budapest Sewage Works. See image gallery. Where can we find places for these things?

To find ideal sites for Thermowatt, we would need to search for buildings and pipelines, not maps or documents. The Internet of Things is for connecting buildings and pipes that have sensors and chips. The semantic search on web 3.0 is finding buildings and pipelines even if they do not have chips, or sensors. The idea of the semantic web, or the web 3.0 is to connect any well-described “thing”, from heat day statistical tables to building documentation to urban plans of sewage systems into a single, searchable web.

In the web 3.0, a thing can be anything that is properly documented: a table, an e-book or a printed book, photographs or a building, or the description of a building in a city cadastre. The web 1.0 way would be to google for building databases, heating day data, and sewage pipeline data all over Europe, by accessing those databases, placing them into the Thermowatt’s database, then making a SQL query for the locations of certain buildings matching the environmental profile. The web 3.0 is to search for things, such as hospital buildings, whose coordinates match a certain environmental profile, in many databases.

Searching for buildings in labs.kadaster.nl.

The web 3.0 way is to link together existing databases, including databases that fall under the EU Open Data Directive, because such databases can be re-used for commercial purposes for free. The European Union wants to boost the efficiency of business innovation by making all data assets that were originally financed by the taxpayers—including companies, like Thermowatt itself–, available for commercial use after the government has used them for its own purposes. If the government has placed all hospitals, and sewage pipeline data into databases, why not open it up for Thermowatt? And why not in a way that avoids database building costs for Thermowatt, which is, itself, a small, innovative eco-tech company without a data science or large IT team. The web 3.0 makes links to databases, just like you would link to the websites of each and every hospital building where you would like to pitch this installation.

SThe Semantic Web Compared To The Traditional Web by Arbeck.

The Semantic Web Compared To The Traditional Web

“The semantic web is the future of the internet and always will be,”, joked, Peter Norvig, director of research at Google said almost 20 years after the invention of the world wide web, which took only 5 years to become from concept a global hit.. While the connection of webpages with the hypertext url link, or the http(s), was an instant success in 1994, connecting databases with the RDF standards has proven to be a much more difficult task. But it is happening. The European Union releases more and more datasets in this format, and researchers and startups like Reprex are offering cheaper and easier open-source tools to build RDF-compatible ‘dataset resources’.

Linking together datasets in a way that they can be searched by meaning (‘give me a building close enough to a sewage main pipe in the Hague’, ‘now find me similar buildings in relatively cool cities with many heating days in the Netherlands… in the Benelux…. In Europe’). Google, an early evangelist of the web 3.0 as much as the web 1.0 15 years earlier, started to commercially release it under the name ‘Google knowledge graph’, allowing users to search for a thing instead of a thing. Between 1997 and 2004 it took about 7 years to make Google search engine the global leader in finding strings in the web 1.0. The commercialization of the web 3.0 is taking a slower pace, but around 2019-2020 it became a mainstream technology for large organizations. The mission of Reprex is to make knowledge graphs available for small companies, even civil society actors with cooperation in the data observatories.

The aim of the Green Deal Data Observatory is to create such a knowledge graph that connects datasets from Eurostat, the European Environmental Agency, national and city cadastres, Wikipedia, Open Street Data, and all sorts of places in a way that ecotech companies like Thermowatt can search for their next site and start saving energy loss in Europe.

Creating a knowledge graph is always an open collaboration: we never know what new datasets, blueprints, photos, and heatmaps will be available on web 3.0 in the future. The Green Deal Data Observatory is creating a knowledge graph that serves business and policy purposes related to the European Green Deal. Reprex, Thermowatt, Concorde MB Partners, and Blue Door Consulting are inviting district heating companies, facility operators, sewage utilities,s and cities to join our knowledge graph, and start looking for new locations where we can stop the energy loss in the cold winter of 2022/2023.

stacodelists: use standard, language-independent variable codes to help international data interoperability and machine reuse in R

Wed, 29 Jun 2022 08:12:00 +0100

Visit the documentation website of statcodelists on statcodelists.dataobservatory.eu/.

The goal of statcodelists is to promote the reuse and exchange of statistical information and related metadata with making the internationally standardized SDMX code lists available for the R user. SDMX – the Statistical Data and Metadata eXchange has been published as an ISO International Standard (ISO 17369). The metadata definitions, including the codelists are updated regularly according to the standard. The authoritative version of the code lists made available in this package is https://sdmx.org/?page_id=3215/.

Click to expand table of contents of the post

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Purpose

Cross-domain concepts in the SDMX framework describe concepts relevant to many, if not all, statistical domains. SDMX recommends using these concepts whenever feasible in SDMX structures and messages to promote the reuse and exchange of statistical information and related metadata between organisations.

Code lists are predefined sets of terms from which some statistical coded concepts take their values. SDMX cross-domain code lists are used to support cross-domain concepts. What are these cross-domain coded concepts?

Geographical codes, like NL: the Netherlands in the CL_AREA code list.
Standard industry codes J631 for Data processing, hosting and related activities in Europe. (NACE Rev 2 in Europe, beware, it is J592in Australia and New Zealand, see CL_ACTIVITY_ANZSIC06.)
Occupations, like OC2521 for Database designers and administrators in CL_OCCUPATIONS
Time fomatting standards, like CCYY for annual data series in CL_TIME_FORMAT.

Check out the available codlists on the package homepage.

The use of common code lists will help users to work even more efficiently, easing the maintenance of and reducing the need for mapping systems and interfaces delivering data and metadata to them. A very obvious advantage of using the code systems is that you can retrieve data from national sources indifferent of the natural language used in North Macedonia, Japan, the U.S. or the Netherlands. While the data labels may change to be locally human-readable, computers and geeks can read the codes and understand them immediately. Provided that they use the standard codes.

Our data observatories are rolling out SDMX coding across all datasets to help data ingestion and interoperability, data findability and data reuse. statcodelists can help the use of standard SDMX codes in your R workflow–both for downloading data from statistical agencies and to produce publication-ready datasets that the rest of the world (and even APIs) will understand.

Installation

You can install statcodelists from CRAN:

install.packages("statcodelists")

Further recommended code values for expressing general statistical concepts like not applicable, etc., can be found in section Generic codes of the Guidelines for the creation and management of SDMX Cross-Domain Code Lists.

For further codelists used by reliable statistical agency but not harmonized on SDMX level please consult the SDMX Global Registry Codelists page.

The creator of this package is not affiliated with SDMX, and this package was has not been endorsed by SDMX.

Code of Conduct

Please note that the statcodelists project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

100,000 Opinions on the Most Pressing Global Problem

Thu, 25 Nov 2021 09:41:00 +0100

A reprezentative sample of n=100793 from 5 years on the most serious global problem. Get the tidy dataset from our repository or API.

Imagine if you could compare data easily from surveys taken about climate change from all European countries, maybe even from other continents, from different years? If you could work with a sample of not only n=1000, n=4000, or n=10,000 but n=100,000? What type of granularity it would give you about the perception of climate change or supported policy measures? That is exactly what our survey harmonization software allows for you to do.

You can use and verify our software: it is a perfectly documented, open source, peer-reviewed scientific software. But for most users, a bit too difficult to handle. This is why we are building the Green Deal Data Observatory as a user-centered API around the software. The Green Deal Data Observatory is processing climate-change related data from variuos survey, sensory, satellite data sources, and places them into tidy, easy-to-import datasets and visualizations.

Survey harmonization means various social science, statistical and data processing steps to make data comparable and joinable from various questionnaire answers taken in different countries, languages, and years. To demonstrate the power of retrospective survey harmonization, we have made an indicator, visualizations and a data animation from more than a hundred nationally representative surveys, which asked more than 137,000 Europeans about what they considered to be the single most serious problem facing the world as a whole?

Survey data harmonization refers to procedures that improve the data comparability or the possibility to make policy or scientific comparisons between data from surveys conducted in different countries or in different years. Our retroharmonize software helps this tedious, laborous, difficult data processing task.

The result is stunning compared to a survey of 1000, 4000 or even 10,000 people. In this video we have harmonized the answers from more than 137,000 Europeans surveyed in more than 20 languages. As you can see in the data animation, people got more and more concerned about climate change… until Covid struck.

Our data shows that more urban and higher educated people tend to be more and more concerned about climate change. Concern is higher and higher as younger and younger people are asked. (Our data source, the Eurobaromter survey is asking Europeans from the age of 15.)

There are huge national differences in Europe: people in the countries that we defined as Nordic (Scandinavia and Finland) are much more serious about climate change than the rest of the continent. It also matters when was the question asked: between 2013-2019 anxiety over the climate has been growing rapidly, but it peaked in 2019. In 2020, the Covid pandemic has altered the problem map of the European population, with ‘infectious diseases’ other important global problems. But apart from the time of asking the question, and the place of asking, there are important patterns emerging all over Europe which are shared regardless of the time and place.

Our classification tree model shows what factors play an important role in determining if somebody believes that climate change is the most important global problem.

People with no formal education rarely think that climate change is the most important global problem. People with secondary school education care less than people with tertiary education, and people with tertiary education or a bachelor’s degree care less than people who have a university degree or who are committed to life-long learning. This effect is further emphasized by level of urbanization: the more urbanized are the respondents, the more likely they think that climate change is the single most important problem facing humanity. (Urban people tend to have higher education levels, too.)

Another important factor is age: the younger the respondent, the more likely to believe that climate change is the single most important problem.

One takeaway is that generally, people’s climate awareness is rising: Europeans tend to be more urbanized and more educated, and this works in favor of recognizing this problem. The coming younger generations are also more aware of climate change. Yet, as Covid-19 shows, a global trauma can alter the picture quickly.

Using the implemented machine learning R software package of Christoph Molnar, we calculated the importance of various socio-demography variables in predicting who will think that climate change is the most important problem facing us.

Out of the variables we investigated, time spent in education is the most important factor contributing to climate awareness, closely followed by the time when the question was asked.

The importance of age, time, and even the time spent in education (age of leaving formal education) show that there is very significant change over time. Unfortunately, this change is not monotonous, until 2019 climate awareness was growing by this indicator, then it declined due to Covid.

If you would ask a European citizen about the most important global problem today, the following decision tree would help guessing if she or he would reply “climate change”.

Our classification tree model shows what factors play an important role in determining if somebody believes that climate change is the most important global problem.

The education level, the age, and the question of asking are very important variables, and so is the fact if the respondent has at least one child. Interestingly, parents are less likely to be concerned about climate change then other people. In other words, the children are more concerned than their parents.

Get our data

You can always rely on our API to import directly the latest, best data, but if you want to be sure, you can use our regular backups on Zenodo. Zenodo is an open science repository managed by CERN and supported by the European Union. On Zenodo, you can find an authoritative copy of our indicator (and its previous versions) with a digital object identifier, in this case, 10.5281/zenodo.5711962. These datasets will be preserved for decades, and nobody can manipulate them. You cannot accidentally overwrite them, and we have no backdoor to modify them.

Are you a data user? Give us some feedback! Shall we do some further automatic data enhancements with our datasets? Document with different metadata? Link more information for business, policy, or academic use? Please give us any feedback!

How We Add Value to Public Data With Imputation and Forecasting?

Mon, 08 Nov 2021 10:00:00 +0100

Public data sources are often plagued by missng values. Naively you may think that you can ignore them, but think twice: in most cases, missing data in a table is not missing information, but rather malformatted information. This approach of ignoring or dropping missing values will not be feasible or robust when you want to make a beautiful visualization, or use data in a business forecasting model, a machine learning (AI) applicaton, or a more complex scientific model. All of the above require complete datasets, and naively discarding missing data points amounts to an excessive waste of information. In this example we are continuing the example a not-so-easy to find public dataset.

In the previous blogpost we explained how we added value with documenting the data following the FAIR principle and with the professional curatorial work of placing the data in context, and linking it to other information sources that are not depending on the English language, and can connect our radio dataset to other data, books, publications, regardless if they are described in English, or in German, or Slovak. Photo: Atmospheric Research Observatory, South Pole, Antarctica Photo: NOAA.

Completing missing datapoints requires statistical production information (why might the data be missing?) and data science knowhow (how to impute the missing value.) If you do not have a good statistician or data scientist in your team, you will need high-quality, complete datasets. This is what our automated data observatories provide.

Why is data missing?

International organizations offer many statistical products, but usually they are on an ‘as-is’ basis. For example, Eurostat is the world’s premiere statistical agency, but it has no right to overrule whatever data the member states of the European Union, and some other cooperating European countries give to them. And they cannot force these countries to hand over data if they fail to do so. As a result, there will be many data points that are missing, and often data points that have wrong (obsolete) descriptions or geographical dimensions. We will show the geographical aspect of the problem in a separate blogpost; for now, we only focus on missing data.

Some countries have only recently started providing data to the Eurostat umbrella organization, and it is likely that you will find few datapoints for North Macedonia or Bosnia-Herzegovina. Other countries provide data with some delay, and the last one or two years are missing. And there are gaps in some countries’ data, too.

See the authoritative copy of the dataset.

This is a headache if you want to use the data in some machine learning application or in a multiple or panel regression model. You can, of course, discard countries or years where you do not have full data coverage, but this approach usually wastes too much information–if you work with 12 years, and only one data point is available, you would be discarding an entire country’s 11-years’ worth of data. Another option is to estimate the values, or otherwise impute the missing data, when this is possible with reasonable precision. This is where things get tricky, and you will likely need a statistician or a data scientist onboard.

What can we improve?

Consider that the data is only missing from one year for a particular country, 2015. The naive solution would be to omit 2015 or the country at hand from the dataset. This is pretty destructive, because we know a lot about the R&D allocations in this country and in this year! But leaving 2015 blank will not look good on a chart, and will make your machine learning application or your regression model stop.

A statistician or an innovation expert will tell you that you know more-or-less the missing information: the total allocation was most likely not zero in that year. With some statistical or innovation, or public finance specific knowledge you will use the 2014, or 2016 value, or a combination of the two and keep the country and year in the dataset.

Our improved dataset added backcasted (using the best time series model fitting the country’s actually present data), forecasted (again, using the best time series model), and approximated data (using linear approximation.) In a few cases, we add the last or next known value. To give a few quantiative indicators about our work:

Increased number of observations: 29.2%
Reduced missing values: -26.4%
Increased non-missing subset for regression or AI: +64.7%

If your organization is working with panel (longitudional multiple) regressions or various machine learning applications, then your team knows that not havint the +66.67% gain would be a deal-breaker in the choice of models and punctuality of estimates or KPIs or other quantiative products. And that they would spent about 90% of their data resources on achieving this +66.67% gain in usability.

If you happen to work in an NGO, a business unit or a research institute that does not employ data scientists, then it is likely that you can never achieve this improvement, and you have to give up on a number of quantitative tools or visualizations. If you have a data scientist onboard, that professional can use our work as a starting point.

Can you trust our data?

We believe that you can trust our data better than the original public source. We use statistical expertise to find out why data may be missing. Often, it is present in a wrong location (for example, the name of a region changed.)

If you are reluctant to use estimates, think about discarding known actual data from your forecast or visualization, because one data point is missing. How do you provide more accurate information? By hiding known actual data, because one point is missing, or by using all known data and an estimate?

Our codebooks and our API uses the Statistical Data and Metadata eXchange documentation standards to clearly indicate which data is observed, which is missing, which is estimated, and of course, also how it is estimated. This example highlights another important aspect of data trustworthiness. If you have a better idea, you can replace them with a better estimate.

Our indicators come with standardized codebooks that do not only contain the descriptive metadata, but administrative metadata about the history of the indicator values. You will find very important information about the statistical method we used the fill in the data gaps, and even link the reliable, the peer-reviewed scientific, statistical software that made the calculations. For data scientists, we record the plenty of information about the computing environment, too-–this can come handy if your estimates need external authentication, or you suspect a bug.

Avoid the data Sisyphus

If you work in an academic institution, in an NGO or a consultancy, you can never be sure who downloaded the GBARD by socioeconomic objectives (NABS 2007) Eurostat folder from Eurostat. Did they modify the dataset? Did they already make corrections with the missing data? What method did they use? To prevent many potential problems, you will likely download it again, and again, and again…

See our The Data Sisyphus blogpost.

We have a better solution. You can always rely on our API to import directly the latest, best data, but if you want to be sure, you can use our regular backups on Zenodo. Zenodo is an open science repository managed by CERN and supported by the European Union. On Zenodo, you can find an authoritative copy of our indicator (and its previous versions) with a digital object identifier, in this case, 10.5281/zenodo.5661169. These datasets will be preserved for decades, and nobody can manipulate them. You cannot accidentally overwrite them, and we have no backdoor to modify them.

How We Add Value to Public Data With Better Curation And Documentation?

Mon, 08 Nov 2021 09:00:00 +0100

In this example, we show a simple indicator: the Government Budget Allocations for R&D in Environment in many European countries. (In our Digital Music Observatory we give a more relevant example about the turnover of the radio industry in Europe.)

This dataset comes from a public datasource, the data warehouse of the European statistical agency, Eurostat. Yet it is not trivial to use: unless you are familiar with the nomenclature for the analysis and comparison of scientific programmes and budgets or the Frascati Manual, you will probably not find this dataset on the Eurostat website.

The raw data can be retrieved GBARD by socioeconomic objectives (NABS 2007)[gba_nabsfin07] Eurostat folder (if you find it.)

Our version of this statistical indicator is documented following the FAIR principles: our data assets are findable, accessible, interoperable, and reusable. While the Eurostat data warehouse partly fulfills these important data quality expectations, we can improve them significantly. And we can also improve the dataset, too, as we will show in the next blogpost.

Findable Data

Our data observatories add value by curating the data–we bring this indicator to light with a more descriptive name, and we place it in context with our Green Deal Data Observatory. While many people may need this dataset in the environmental policy organizations, NGOs, scientific journalists, or researchers, most of them has no training in the nomenclatures of scientific and R&D spending or public budget accounts. Our curated data observatories bring together many available data around important domains. Our Green Deal Data Observatory, for example, aims to form an ecosystem of climate policy and climate change mitigation data users and producers.

We added descriptive metadata that help you find our data and match it with other relevant data sources.

We added descriptive metadata that help you find our data and match it with other relevant data sources. For example, we add keywords and standardized metadata identifiers from the Library of Congress Linked Data Services, probably the world’s largest standardized knowledge library description. This makes sure that you can find relevant data about the same concept (environmental protection) besides our turnover data. This help unambigously connect our dataset with other information source that use the same concept, but maybe different keywords, such as Protection of environment, or maybe Umweltschutz in German, or Ochrana životného prostredia in Slovak. Or avoid confusion with Human environment.

Accessible Data

Our data is accessible in two forms: in csv tabular format (which can be read with Excel, OpenOffice, Numbers, SPSS and many similar spreadsheet or statistical applications) and in JSON for automated importing into your databases. We can also provide our users with SQLite databases, which are fully functional, single user relational databases.

Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This makes the data easier to clean, and far more easier to use in a much wider range of applications than the original data we used. In theory, this is a simple objective, yet we find that even governmental statistical agencies–and even scientific publications–often publish untidy data. This poses a significant problem that implies productivity loses: tidying data will require long hours of investment, and if a reproducible workflow is not used, data integrity can also be compromised: chances are that the process of tidying will overwrite, delete, or omit a data or a label.

Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table.

While the original data source, the Eurostat data warehouse is accessible, too, we added value with bringing the data into a tidy format. Tidy data can immediately be imported into a statistical application like SPSS or STATA, or into your own database. It is immediately available for plotting in Excel, OpenOffice or Numbers.

Interoperability

Our data can be easily imported with, or joined with data from other internal or external sources.

All our indicators come with standardized descriptive metadata, and statistical (processing) metadata. See our API

All our indicators come with standardized descriptive metadata, following two important standards, the Dublin Core and DataCite–implementing not only the mandatory, but the recommended descriptions, too. This will make it far easier to connect the data with other data sources, e.g. turnover with the number of radio broadcasting enterprises or radio stations within specific territories.

Our passion for documentation standards and best practices goes much further: our data uses Statistical Data and Metadata eXchange standardized codebooks, unit descriptions and other statistical and administrative metadata.

Reuse

All our datasets come with standardized information about reusabililty. We add citation, attribution data, and licensing terms. Most of our datasets can be used without commercial restriction after acknowledging the source, but we sometimes work with less permissible data licenses.

In the case presented here, we added further value to encourage re-use. In addition to tidying, we significantly increased the usability of public data by handling missing cases. This is the subject of our next blogpost.

The Data Sisyphus

Thu, 08 Jul 2021 09:00:00 +0000

Sisyphus was punished by being forced to roll an immense boulder up a hill only for it to roll down every time it neared the top, repeating this action for eternity. This is the price that project managers and analysts pay for the inadequate documentation of their data assets.

When was a file downloaded from the internet? What happened with it sense? Are their updates? Did the bibliographical reference was made for quotations? Missing values imputed? Currency translated? Who knows about it – who created a dataset, who contributed to it? Which is an intermediate format of a spreadsheet file, and which is the final, checked, approved by a senior manager?

Big data creates inequality and injustice. On aspect of this inequality is the cost of data processing and documentation – a greatly underestimated, and usually not reported cost item. In small organizations, where there are no separate data science and data engineering roles, data is usually supposed to be processed and documented by (junior) analysts or researchers. This a very important source of the gap between Big Tech and them: the data usually ends up very expensive, ill-formatted, not readable by computers that use machine learning and AI. Usually the documentation steps are completely omitted.

“Data is potential information, analogous to potential energy: work is required to release it.” – Jeffrey Pomerantz

Metadata, which is information about the history of the data, and information how it can be technically and legally reused, has a hidden cost. Cheap or low-quality external data comes with poor or no metadata, and small organizations lack the resources to add high-quality metadata to their datasets. However, this only perpetuates the problem.

The hidden cost item behind the unbillable hours

As we have shown with our research partners, such metadata problems are not unique to data analysis. Independent artists and small labels are suffering on music or book sales platforms, because their copyrighted content is not well documented. If you automatically document tens of thousands of songs or datasets, the documentation cost is very small per item. If you, do it manually, the cost may be higher than the expected revenue from the song, or the total cost of the dataset itself. (See our research consortiums’ preprint paper: Ensuring the Visibility and Accessibility of European Creative Content on the World Market: The Need for Copyright Data Improvement in the Light of New Technologies)

In the short run, small consultancies, NGOs, or as a matter of fact, musicians, seem to logically give up on high-quality documentation and logging. In the long run, this has two devastating consequences: computers, such as machine learning algorithms cannot read their documents, data, songs. And as memory fades, the ill-documented resources need to be re-created, re-checked, reformatted. Often, they are even hard to find on your internal server or laptop archive.

Metadata is a hidden destroyer of the competitiveness of corporate or academic research, or independent content management. It never quoted on external data vendor invoices, it is not planned as a cost item, because metadata, the description of a dataset, a document, a presentation, or song, is meaningless without the resource that it describes. You never buy metadata. But if your dataset comes without proper metadata documentation, you are bound, like Sisyphus, to search for it, to re-arrange it, to check its currency units, its digits, its formatting. Data analysts are reported to spend about 80% of their working hours on data processing and not data analysis – partly, because data processing is a very laborious task that can be done by computers at a scale far cheaper, and partly because they do not know if the person who sat before them at the same desk has already performed these tasks, or if the person responsible for quality control checked for errors.

Uncut diamonds need to be cut, polished, and you have to make sure that they come from a legal source. Data is similar: it needs to be tidied up, checked and documented before use. Photo: Dave Fischer.

Undocumented data is hardly informative – it may be a page in a book, a file in an obsolete file format on a governmental server, an Excel sheet that you do not remember to have checked for updates. Most data are useless, because we do not know how it can inform us, or we do not know if we can trust it. The processing can be a daunting task, not to mention the most boring and often neglected documentation duties after the dataset is final and pronounced error-free by the person in charge of quality control.

Our observatory automatically processes and documents the data

The good news about documentation and data validation costs is that they can be shared. If many users need GDP/capita data from all over the world in euros, then it is enough if only one entity, a data observatory, collects all GDP and population data expresed in dollars, korunas, and euros, and makes sure that the latest data is correctly translated to euros, and then correctly divided by the latest population figures. These task are error-prone,and should not be repeaeted by every data journalist, NGO employee, PhD student or junior analyst. This is one of the services of our data observatory.

The tidy data format means that the data has a uniform and clear data structure and semantics, therefore it can be automatically validated for many common errors and can be automatically documented by either our software or any other professional data science application. It is not as strict as the schema for a relational database, but it is strict enough to make, among other things, importing into a database easy.
The descriptive metadata contains information on how to find the data, access the data, join it with other data (interoperability) and use it, and reuse it, even years from now. Among others, it contains file format information and intellectual property rights information.
The processing metadata makes the data usable in strictly regulated professional environments, such as in public administration, law firms, investment consultancies, or in scientific research. We give you the entire processing history of the data, which makes peer-review or external audit much easier and cheaper.
The authoritative copy is held at an independent repository, it has a globally unique identifier that protects you from accidental data loss, mixing up with unfinished an untested version.

Cutting the dataset to a format with clear semantics and documenting it with the FAIR metadata concep exponentially increases the value of data. It can be publisehd or sold at a premium. Photo: Andere Andre.

While humans are much better at analysing the information and human agency is required for trustworthy AI, computers are much better at processing and documenting data. We apply to important concepts to our data service: we always process the data to the tidy format, we create an authoritative copy, and we always automatically add descriptive and processing metadata.

The value of metadata

Metadata is often more valuable and more costly to make than the data itself, yet it remains an elusive concept for senior or financial management. Metadata is information about how to correctly use the data and has no value without the data itself. Data acquisition, such as buying from a data vendor, or paying an opinion polling company, or external data consultants appears among the material costs, but metadata is never sold alone, and you do not see its cost.

In most cases, the reason why there is no gold rush for open data is that fact that while the EU member states release billions of euros’ worth data for free, or at very low cost, annually, it comes without proper metadata.

Data-as-Service

Reusable, legal, easy-to-import, interoperable, always fresh data in tidy formats with a modern API. Photo: Edgar Soto.

If the data source is cheap or has a low quality, you do not even get it. If you do not have it, it will show up as a human resource cost in research (when your analysist or junior researcher are spending countless hours to find out the missing metadata information on the correct use of the data) or in sales costs (when you try to reuse a research, consulting or legal product and you have comb through your archive and retest elements again and again.)

The data, together with the descriptive and administrative metadata, and links to the use license and the authoritative copy can be found in our API. Try it out!

Including Indicators from Arab Barometer in Our Observatory

Mon, 28 Jun 2021 09:00:00 +0000

A new version of the retroharmonize R package – which is working with retrospective, ex post harmonization of survey data – was released yesterday after peer-review on CRAN. It allows us to compare opinion polling data from the Arab Barometer with the Eurobarometer and Afrorbarometer. This is the first version that is released in the rOpenGov community, a community of R package developers on open government data analytics and related topics.

Surveys are the most important data sources in social and economic statistics – they ask people about their lives, their attitudes and self-reported actions, or record data from companies and NGOs. Survey harmonization makes survey data comparable across time and countries. It is very important, because often we do not know without comparison if an indicator value is low or high. If 40% of the people think that climate change is a very serious problem, it does not really tell us much without knowing what percentage of the people answered this question similarly a year ago, or in other parts of the world.

With the help of Ahmed Shabani and Yousef Ibrahim, we created a third case study after the Eurobarometer, and Afrobarometer, about working with the Arab Barometer harmonized survey data files.

Ex ante survey harmonization means that researchers design questionnaires that are asking the same questions with the same survey methodology in repeated, distinct times (waves), or across different countries with carefully harmonized question translations. Ex post harmonizations means that the resulting data has the same variable names, same variable coding, and can be joined into a tidy data frame for joint statistical analysis. While seemingly a simple task, it involves plenty of metadata adjustments, because established survey programs like Eurobarometer, Afrobarometer or Arab Barometer have several decades of history, and several decades of coding practices and file formatting legacy.

Variable harmonization means that if the same question is called in one microdata source Q108 and the other eval-parl-elections then we make sure that they get a harmonize and machine readable name without spaces and special characters.
Variable label harmonization means that the same questionnaire items get the same numeric coding and same categorical labels.
Missing case harmonization means that various forms of missingness are treated the same way.

For the climate awareness dataset get the country averages and aggregates from Zenodo, and the plot in jpg or png from figshare.

In our new Arab Barometer case study, the evaulation of parliamentary elections has the following labels. We code them consistently 1: free_and_fair, 2: some_minor_problems, 3: some_major_problems and 4: not_free.

“0. missing”	“1. they were completely free and fair”
“2. they were free and fair, with some minor problems”	“3. they were free and fair, with some major problems”
“4. they were not free and fair”	“8. i don’t know”
“9. declined to answer”	“Missing”
“They were completely free and fair”	“They were free and fair, with some minor breaches”
“They were free and fair, with some major breaches”	“They were not free and fair”
“Don’t know”	“Refuse”
“Completely free and fair”	“Free and fair, but with minor problems”
“Free and fair, with major problems”	“Not free or fair”
“Don’t know (Do not read)”	“Decline to answer (Do not read)”

Of course, this harmonization is essential to get clean results like this:

For evaluation or reuse of parliamentary elections dataset get the replication data and the code from the Zenodo open repository.

In our case study, we had three forms of missingness: the respondent did not know the answer, the respondent did not want to answer, and at last, in some cases the respondent was not asked, because the country held no parliamentary elections. While in numerical processing, all these answers must be left out from calculating averages, for example, in a more detailed, categorical analysis they represent very different cases. A high level of refusal to answer may be an indicator of surpressing democratic opinion forming in itself.

Survey harmonization with many countries entails tens of thousands of small data management task, which, unless automatically documented, logged, and created with a reproducible code, is a helplessly error-prone process. We believe that our open-source software will bring many new statistical information to the light, which, while legally open, was never processed due to the large investment needed.

We also started building experimental APIs data is running retroharmonize regularly. We will place cultural access and participation data in the Digital Music Observatory, climate awareness, policy support and self-reported mitigation strategies into the Green Deal Data Observatory, and economy and well-being data into our Economy Data Observatory.

Further plans

Retrospective survey harmonization is a far more complex task than this blogpost suggest. Retrospective survey harmonization is a far more complex task than this blogpost suggest, because established survey programs have gathered decades of legacy data in legacy coding schemes and legacy file formats. Putting the data right, and especially putting the invaluable descriptive and administrative (processing) metadata right is a huge undertaking. We are releasing example codes, datasets and charts for researchers to comapre our harmonized results with theirs, and improve our software. We are releasing example codes, datasets and charts for researchers to comapre our harmonized results with theirs, and improve our software.

Use our software

The retroharmonize R package can be freely used, modified and distributed under the GPL-3 license. For the main developer and contributors, see the package homepage. If you use it for your work, please kindly cite it as:

Daniel Antal (2021). retroharmonize: Ex Post Survey Data Harmonization. R package version 0.1.17. https://doi.org/10.5281/zenodo.5034752

Download the BibLaTeX entry.

Tutorial to work with the Arab Barometer survey data

Daniel Antal, & Ahmed Shaibani. (2021, June 26). Case Study: Working With Arab Barometer Surveys for the retroharmonize R package (Version 0.1.6). Zenodo. https://doi.org/10.5281/zenodo.5034759

For the replication data to report potential issues and improvement suggestions with the code:

Daniel Antal, & Ahmed Shaibani. (2021). Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys (Version 0.1.6) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5034741

Experimental API

We are also experimenting with the automated placement of authoritative and citeable figures and datasets in open repositories. For the climate awareness dataset get the country averages and aggregates from Zenodo, and the plot in jpg or png from figshare. Our plan is to release open data in a modern API with rich descriptive metadata meeting the Dublin Core and DataCite standards, and further administrative metadata for correct coding, joining and further manipulating or data, or for easy import into your database.

Join our open source effort

Want to help us improve our open data service? Include Lationbarómetro and the Caucasus Barometer in our offering? Join the rOpenGov community of R package developers, an our open collaboration to create the automated data observatories. We are not only looking for developers, but data curators and service design associates, too.

Open Data - The New Gold Without the Rush

Fri, 18 Jun 2021 17:00:00 +0000

If open data is the new gold, why even those who release fail to reuse it? We created an open collaboration of data curators and open-source developers to dig into novel open data sources and/or increase the usability of existing ones. We transform reproducible research software into research- as-service.

Every year, the EU announces that billions and billions of data are now “open” again, but this is not gold. At least not in the form of nicely minted gold coins, but in gold dust and nuggets found in the muddy banks of chilly rivers. There is no rush for it, because panning out its value requires a lot of hours of hard work. Our goal is to automate this work to make open data usable at scale, even in trustworthy AI solutions.

There is no rush for it, because panning out its value requires a lot of hours of hard work. Our goal is to automate this work to make open data usable at scale, even in trustworthy AI solutions.

Most open data is not public, it is not downloadable from the Internet – in the EU parlance, “open” only means a legal entitlement to get access to it. And even in the rare cases when data is open and public, often it is mired by data quality issues. We are working on the prototypes of a data-as-service and research-as-service built with open-source statistical software that taps into various and often neglected open data sources.

We are in the prototype phase in June and our intentions are to have a well-functioning service by the time of the conference, because we are working only with open-source software elements; our technological readiness level is already very high. The novelty of our process is that we are trying to further develop and integrate a few open-source technology items into technologically and financially sustainable data-as-service and even research-as-service solutions.

Our review of about 80 EU, UN and OECD data observatories reveals that most of them do not use these organizations’s open data - instead they use various, and often not well processed proprietary sources.

We are taking a new and modern approach to the data observatory concept, and modernizing it with the application of 21st century data and metadata standards, the new results of reproducible research and data science. Various UN and OECD bodies, and particularly the European Union support or maintain more than 60 data observatories, or permanent data collection and dissemination points, but even these do not use these organizations and their members open data. We are building open-source data observatories, which run open-source statistical software that automatically processes and documents reusable public sector data (from public transport, meteorology, tax offices, taxpayer funded satellite systems, etc.) and reusable scientific data (from EU taxpayer funded research) into new, high quality statistical indicators.

We are taking a new and modern approach to the ‘data observatory’ concept, and modernizing it with the application of 21st century data and metadata standards, the new results of reproducible research and data science

We are building various open-source data collection tools in R and Python to bring up data from big data APIs and legally open, but not public, and not well served data sources. For example, we are working on capturing representative data from the Spotify API or creating harmonized datasets from the Eurobarometer and Afrobarometer survey programs.
Open data is usually not public; whatever is legally accessible is usually not ready to use for commercial or scientific purposes. In Europe, almost all taxpayer funded data is legally open for reuse, but it is usually stored in heterogeneous formats, processed into an original government or scientific need, and with various and low documentation standards. Our expert data curators are looking for new data sources that should be (re-) processed and re-documented to be usable for a wider community. We would like to introduce our service flow, which touches upon many important aspects of data scientist, data engineer and data curatorial work.
We believe that even such generally trusted data sources as Eurostat often need to be reprocessed, because various legal and political constraints do not allow the common European statistical services to provide optimal quality data – for example, on the regional and city levels.
With rOpenGov and other partners, we are creating open-source statistical software in R to re-process these heterogenous and low-quality data into tidy statistical indicators to automatically validate and document it.
We are carefully documenting and releasing administrative, processing, and descriptive metadata, following international metadata standards, to make our data easy to find and easy to use for data analysts.
We are automatically creating depositions and authoritative copies marked with an individual digital object identifier (DOI) to maintain data integrity.
We are building simple databases and supporting APIs that release the data without restrictions, in a tidy format that is easy to join with other data, or easy to join into databases, together with standardized metadata.
We maintain observatory websites (see: Digital Music Observatory, Green Deal Data Observatory, Economy Data Observatory) where not only the data is available, but we provide tutorials and use cases to make it easier to use them. Our mission is to show a modern, 21st century reimagination of the data observatory concept developed and supported by the UN, EU and OECD, and we want to show that modern reproducible research and open data could make the existing 60 data observatories and the planned new ones grow faster into data ecosystems.

We are working around the open collaboration concept, which is well-known in open source software development and reproducible science, but we try to make this agile project management methodology more inclusive, and include data curators, and various institutional partners into this approach. Based around our early-stage startup, Reprex, and the open-source developer community rOpenGov, we are working together with other developers, data scientists, and domain specific data experts in climate change and mitigation, antitrust and innovation policies, and various aspects of the music and film industry.

Our open collaboration is truly open: new data curators,developers and service designers, even volunteers and citizen scientists are welcome to join.

Our open collaboration is truly open: new data curators, data scientists and data engineers are welcome to join. We develop open-source software in an agile way, so you can join in with an intermediate programming skill to build unit tests or add new functionality, and if you are a beginner, you can start with documentation and testing our tutorials. For business, policy, and scientific data analysts, we provide unexploited, exciting new datasets. Advanced developers can join our development team: the statistical data creation is mainly made in the R language, and the service infrastructure in Python and Go components.

Analyze Locally, Act Globally: New regions R Package Release

Wed, 16 Jun 2021 12:00:00 +0000

The new version of our rOpenGov R package regions was released today on CRAN. This package is one of the engines of our experimental open data-as-service Green Deal Data Observatory, Economy Data Observatory, Digital Music Observatory prototypes, which aim to place open data packages into open-source applications.

Click to expand table of contents of the post

Table of Contents

In international comparison the use of nationally aggregated indicators often have many disadvantages: they inhibit very different levels of homogeneity, and data is often very limited in number of observations for a cross-sectional analysis. When comparing European countries, a few missing cases can limit the cross-section of countries to around 20 cases which disallows the use of many analytical methods. Working with sub-national statistics has many advantages: the similarity of the aggregation level and high number of observations can allow more precise control of model parameters and errors, and the number of observations grows from 20 to 200-300.

The change from national to sub-national level comes with a huge data processing price: internal administrative boundaries, their names, codes codes change very frequently.

Yet the change from national to sub-national level comes with a huge data processing price. While national boundaries are relatively stable, with only a handful of changes in each recent decade. The change of national boundaries requires a more-or-less global consensus. But states are free to change their internal administrative boundaries, and they do it with large frequency. This means that the names, identification codes and boundary definitions of sub-national regions change very frequently. Joining data from different sources and different years can be very difficult.

Our regions R package helps the data processing, validation and imputation of sub-national, regional datasets and their coding.

There are numerous advantages of switching from a national level of the analysis to a sub-national level comes with a huge price in data processing, validation and imputation, and the regions package aims to help this process.

You can review the problem, and the code that created the two map comparisons, in the Maping Regional Data, Maping Metadata Problems vignette article of the package. A more detailed problem description can be found in Working With Regional, Sub-National Statistical Products.

This package is an offspring of the eurostat package on rOpenGov. It started as a tool to validate and re-code regional Eurostat statistics, but it aims to be a general solution for all sub-national statistics. It will be developed parallel with other rOpenGov packages.

Get the Package

You can install the development version from GitHub with:

devtools::install_github("rOpenGov/regions")

or the released version from CRAN:

install.packages("regions")

You can review the complete package documentation on regions.dataobservaotry.eu. If you find any problems with the code, please raise an issue on Github. Pull requests are welcome if you agree with the Contributor Code of Conduct

If you use regions in your work, please cite the package as: Daniel Antal. (2021, June 16). regions (Version 0.1.7). CRAN. http://doi.org/10.5281/zenodo.4965909

Download the BibLaTeX entry.

Join us

Join our Green Deal Data Observatory collaboration!

Join our open collaboration Green Deal Data Observatory team as a data curator, developer or business developer. More interested in economic policies, particularly computation antitrust, innovation and small enterprises? Check out our Economy Music Observatory team! Or your interest lies more in data governance, trustworthy AI and other digital market problems? Check out our Digital Music Observatory team!

Open Data is Like Gold in the Mud Below the Chilly Waves of Mountain Rivers

Thu, 10 Jun 2021 07:00:00 +0000

Open data is like gold in the mud below the chilly waves of mountain rivers. Panning it out requires a lot of patience, or a good machine.

As the founder of the automated data observatories that are part of Reprex’s core activities, what type of data do you usually use in your day-to-day work?

The automated data observatories are results of syndicated research, data pooling, and other creative solutions to the problem of missing or hard-to-find data. The music industry is a very fragmented industry, where market research budgets and data are scattered in tens of thousands of small organizations in Europe. Working for the music and film industry as a data analyst and economist was always a pain because most of the efforts went into trying to find any data that can be analyzed. I spent most of the last 7-8 years trying to find any sort of information—from satellites to government archives—that could be formed into actionable data. I see three big sources of information: textual,numeric, and continuous recordings for on-site, offsite, and satellite sensors. I am much better with numbers than with natural language processing, and I am improving with sensory sources. But technically, I can mint any systematic information—the text of an old book, a satellite image, or an opinion poll—into datasets.

For you, what would be the ultimate dataset, or datasets that you would like to see in the Green Deal Data Observatory?

Our retroharmonize and regions packages can create regional statistics from Eurobarometer and Afrobarometer surveys on how people think locally about climate change. I would like to combine this with local information on observable climate change, such as drought, urban heat, and extreme weather conditions. Do people have to feel the pain of climate change to believe in the phenomenon? How do self-reported mitigation steps correlate with what people already feel in their local environment? Suzan is talking about measuring mitigation and damage control, because she’s aware of the already present health risks in overheating urban environments. I am more interested in what people think.

See our case study on connecting local tax revenues, climate awareness poll data and drought data in Belgium - we want to extend this to Europe and then to Africa. We also published the code how to do it with tutorials 1, 2 for our International Open Data Day 2021 Event.

Is there a number or piece of information that recently surprised you? If so, what was it?

There were a few numbers that surprised me, and some of them were brought up by our observatory teams. Karel is talking about the fact that not all green energy is green at all: many hydropower stations contribute to the greenhouse effect and not reduce it. Annette brought up the growing interest in the Dalmatian breed after the Disney 101 Dalmatians movies, and it reminded me of the astonishing growth in interest for chess sets, chess tutorials, and platform subscriptions after the success of Netflix’s The Queen’s Gambit.

The Queen’s Gambit’ Chess Boom Moves Online By Rachael Dottle on bloomberg.com

Annette is talking about the importance of cultural influencers, and on that theme, what could be more exciting that Netflix’s biggest success so far is not a detective series or a soap opera but a coming-of-age story of a female chess prodigy. Intelligence is sexy, and we are in the intelligence business.

But to tell a more serious and more sobering number, I recently read with surprise that there are more people smoking cigarettes on Earth in 2021 than in 1990. Population growth in developing countries replaced the shrinking number of developed country smokers. While I live in Europe, where smoking is strongly declining, it reminds me that Europe’s population is a small part of the world. We cannot take for granted that our home-grown experiences about the world are globally valid.

Do you have a good example of really good, or really bad use of data?

FiveThirtyEight.com had a wonderful podcast series, produced by Jody Avirgan, called What’s the Point. It is exactly about good and bad uses of data, and each episode is super interesting. Maybe the most memorable is Why the Bronx Really Burned. New York City tried to measure fire response times, identify redundancies in service, and close or re-allocate fire stations accordingly. What resulted, though, was a perfect storm of bad data: The methodology was flawed, the analysis was rife with biases, and the results were interpreted in a way that stacked the deck against poorer neighborhoods. It is similar to many stories told in a very compelling argument by Catherine D’Ignazio and Lauren F. Klein in their much celebrated book, Data Feminism. Usually, the bad use of data starts with a bad data collection practice. Data analysts in corporations, NGOs, public policy organizations and even in science usually analyze the data that is available.

You can find these examples, together with many more that our contributors recommend, in the motivating examples of Create New Datasets and the Remain Critical parts of our onboarding material. We hope that more and more professionals and citizen scientist will help us to create high-quality and open data.

The real power lies in designing a data collection program. A consistent data collection program usually requires an investment that only powerful organizations, such as government agencies, very large corporations, or the richest universities can afford. You cannot really analyze the data that is not collected and recorded; and usually what is not recorded is more interesting than what is. Our observatories want to democratize the data collection process and make it more available, more shared with research automation and pooling.

You cannot really analyze the data that is not collected and recorded; and usually what is not recorded is more interesting than what is. Our observatories want to democratize the data collection process and make it more available, more shared with research automation and pooling.

From your perspective, what do you see being the greatest problem with open data in 2021?

I have been involved with open data policies since 2004. The problem has not changed much: more and more data are available from governmental and scientific sources, but in a form that makes them useless. Data without clear description and clear processing information is useless for analytical purposes: it cannot be integrated with other data, and it cannot be trusted and verified. If researchers or government entities that fall under the Open Data Directive release data for reuse in a way that does not have descriptive or processing metadata, it is almost as if they did not release anything. You need this additional information to make valid analyses of the data, and to reverse-engineer them may cost more than to recollect the data in a properly documented process. Our developers, particularly Leo and Pyry are talking eloquently about why you have to be careful even with governmental statistical products, and constantly be on the watch out for data quality.

Our API is not only publishing descriptive and processing metadata alongside with our data, but we also make all critical elements of our processing code available for peer-review on rOpenGov

What do you think the Green Deal Data Observatory, and our other automated observatories do, to make open data more credible in the European economic policy community and be accepted as verified information?

Most of our work is in research automation, and a very large part of our efforts are aiming to reverse engineer missing descriptive and processing metadata. In a way, I like to compare ourselves to the working method of the open-source intelligence platform Bellingcat. They were able to use publicly available, scattered information from satellites and social media to identify each member of the Russian military company that illegally entered the territory of Ukraine and shot down the Malaysian Airways MH17 with 297, mainly Dutch, civilians on board.

How we create value for research-oriented consultancies, public policy institutes, university research teams, journalists or NGOs.

We do not do such investigations but work very similarly to them in how we are filtering through many data sources and attempting to verify them when their descriptions and processing history is unknown. In the last years, we were able to estore the metadata of many European and African open data surveys, economic impact, and environmental impact data, or many other open data that was lying around for many years without users.

Open data is like gold in the mud below the chilly waves of mountain rivers. Panning it out requires a lot of patience, or a good machine. I think we will come to as surprising and strong findings as Bellingcat, but we are not focusing on individual events and stories, but on social and environmental processes and changes.

Join us

Join our open collaboration Green Deal Data Observatory team as a data curator, developer or business developer. More interested in antitrust, innovation policy or economic impact analysis? Try our Economy Data Observatory team! Or your interest lies more in data governance, trustworthy AI and other digital market problems? Check out our Digital Music Observatory team!

Educate and Train Data Admirers that Data is not Scary

Wed, 09 Jun 2021 12:00:00 +0000

Annette Wong is helping our service development from a digital strategy and marketing point of view.

Why is data important to the work that you do as a digital strategist at an agency?

As a marketing and digital agency, we work with clients to produce and develop marketing campaigns that impact the bottom line. One of the ways to determine the Return-On-Investment (ROI) is through data. By analyzing the data, our team is able to help our clients predict audience behavior and ideally convert them into taking action ($$$).

Currently, I’m working on a music livestreaming platform and everyday we’re always looking at how our campaigns are performing (and measuring their effectiveness). For example, if we’re running a paid campaign through Facebook and if it’s not converting at the expected % that we want, it indicates to us that we need to change our approach. Data gives us the power and freedom to experiment (with minimal risk) and empowers us to make informed decisions quickly.

Why are you excited about the Digital Music Observatory and is there a reason you decided to participate in this initiative?

Seeing how the pandemic decimated the music industry, specifically in-person events, made me feel a lot of empathy for musicians and the economics of their situation, especially with how musicians generate a living income through their music. The importance of data and having open access promotes transparency, fairer wages (ideally), and levels the playing field for musicians of all sizes and popularity.

Our retroharmonization software helps the creation of objective and comparable indicators about how musicians make a living, or how people think about climate challenges.

I decided to participate in this challenge because I love how data is a secret weapon that anyone can use to re-balance the interests of creators, distributors, and consumers.

Is there a number that recently surprised you? What was it?

This is a little silly but very recently I watched the 101 Dalmatians movie. After watching the movie, I was curious to see if there was a correlation between the release of the movie and the number of Dalmations adopted afterwards. 101 Dalmatians was released in 1985 and 1991 which made thousands of families (in the U.S.) want to adopt one. The American Kennel Club reported that the annual number of Dalmatian puppies registered skyrocketed from 8,170 animals to 42,816.

Photo: John o’ Groats, Unsplash license.

This information is interesting because it validates the idea of how culture influences consumer behavior. I think it’s really cool that we can measure cultural collisions and how it impacts the way we act, think, and respond.

What can our automated data observatories do to make open data more credible in the European economic policy community, or in the music business community more accepted?

I believe that people, in general, appreciate and understand the importance of data. But, it can be overwhelming, sometimes scary, and intimidating to deal with (esp. in large quantities).

However, I feel more people are open to the idea of using data and understand the value of leveraging data to share objective truths. Something that our automated data observatories can do is to provide more opportunities to educate and train data admirers that data is not scary, that it is accessible, and it is here to help uncover insights that can’t be immediately seen.

Join us

Credibility is Enhanced Through Cross Links Between Different Data from Different Domains

Tue, 08 Jun 2021 18:50:00 +0000

As a consultant, what type of data do you usually work with?

I work at the intersection between strategy, finance and organisation. My usual dataset is quite broad - and sometimes unstructured. Oftentimes, the most decisive data are ones that cross domains: economic data coupled with environmental measurements, sociodemographic characteristics linked with online analytics.

If you were able to pick, what would be the ultimate dataset, or datasets that you would like to see in the Green Deal Data Observatory? And the Economy Data Observatory?

If I may venture that far, the interesting point is where these two data observatories meet. But high on my wishlist would be anything related to geospatial dispersion of environmental and climate data: land erosion, aerosols, solar incidence. From an economic perspective, my interest would go especially to - again - dispersion across regions or other geographical domains of, say, number of new enterprises, disposable income, tax incidence…

See our case study on connecting local tax revenues, climate awareness poll data and drought data in Belgium.

Why did you decide to join the challenge and why do you think that this would be a game changer for policymakers and for business leaders?

There is, both from an ecological and a societal point of view, an urgent need for open-access, real-time, trustworthy data to base decisions on. Ever since Kydland & Prescott’s analyses of “rules rather than discretion” and even earlier analyses of investment under uncertainty, the dynamic rules for optimal decision-making (including investment) require fast-response reliable data.

Do you have a favorite, or most used open governmental or open science data source? What do you think about it? Could it be improved?

Let me give one example: the AMECO annual macro-economic database is great for long-term historical analyses but its components ought to be real-time available. As an anecdote, as a fund manager in emerging markets we needed to anticipate macro-economic evolutions and in particular the manner in which capital markets anticipate these evolutions by adjusting foreign exchange rates or positioning themselves along yield curves. To some extent, we needed to predict what AMECO would tell us one year later by means of any real-time trustworthy assessments of the financial or economic situation. The latter data is what we would ideally have in an observatory.

To some extent, we needed to predict what AMECO would tell us one year later by means of any real-time trustworthy assessments of the financial or economic situation. The latter data is what we would ideally have in an observatory.

Is there a piece of information that recently surprised you? What was it?

I am currently working on water-related issues and came across a result reported in Nature Energy earlier this year that in more than one in ten hydropower stations, the extra warming from the dark surface of the water reservoir was enough to outbalance its “green” electricity generation potential, leading to no net climate benefits.

The researchers found that almost half of the reservoirs they surveyed took just four years to reach a net climate benefit. Unfortunately, they also found that 19% of those surveyed took more than 40 years to do so, and approximately 12% of them took 80 years—the average lifetime of a hydroelectric plant. Calculating the albedo-climate penalty of hydropower dammed reservoirs

Again: spatial distribution matters…

Photo: Kees Streefkerk, Unplash License

From your experience, what do you think the greatest problem with open data in 2021 will be?

Trust. In a society where “value” and even “truth” is determined more by the amount of (web) links to a particular “fact” than by its intrinsic characteristics, we need to be able to trust data — open data because it’s open and “closed” data because it’s closed.

What can our automated data observatories do to make open data more credible in the European economic policy and climate change or mitigation community and be more accepted as verified information?

If I may refer to the previous answer: credibility is enhanced through cross-links between different data from different domains that “does not disprove” one another or that is internally consistent. If, say, data on taxable income goes in one direction and taxes in another, it is the reasoned reconciliation of the - alleged or real - inconsistency that will validate the comprehensive data set. So I am a great believer in broad, real-time observatories where not only the data capture, but the data reconciliation is automated, sometimes by means of a simple comparative statics analysis, in other cases maybe through quite elaborate artificial intelligence.

Join us

Developing an Open API is the Right Direction

Mon, 07 Jun 2021 20:00:00 +0000

Botond Vitos, PhD is responsible for maintaing our API. He first started collaboration with our Digital Music Observatory and its trustwrothy AI project.

As data engineer, what type of data do you usually use in your projects?

Coming from a cultural studies background, my main research interest has been grassroots music scenes and festival cultures, which I hope to extend to my current projects as data engineer and as a data scientist. My prior research’s scope was mainly qualitative and focused on the inside views and stories of scene participants and stakeholders, which was invaluable in the understanding of specialized stylistic vocabularies. At the same time, I was interested in the “bigger picture,” which can be approximated through algorithmic approaches and data analysis. With both interests together, I shifted towards data science and engineering.

See our trustworthy AI-driven music export case study for Slovakia

I was recently involved with the development of a classification algorithm that detected stylistic directions within the music genres of electronic dance music labels found on Bandcamp. The Bandcamp Librarian project makes use of the genre taxonomy offered by the industry website Beatport, which is a very top-down approach on electronic dance music genres, often resisted by the artists themselves (many of the more niche subgenres don’t even appear on the Beatport site). Accordingly, the project defined genre clusters within each Bandcamp label, which show up as combinations of Beatport subgenres. Also, it indicated some of the folksonomies (bottom-up stylistic definitions and tags) propagated by the musicians themselves.

Screenshot of the first verison of the demo app.

In addition, working with Reprex, I became involved in the development of the Listen Local initiative. The system was aimed to protect the rights of small local artists by offering recommendation algorithms that prioritize local talent for consideration and enables the user to find local talent. The current playlist recommendations of streaming industry giants, such a,s Spotify prioritize big labels and big names, blocking access to the output of smaller, local musicians. Naturally, I looked at this project as a possible continuation of my previous work, and we are currently extending the scope of the Bandcamp Librarian to fit this initiative.

In an ideal data world, what would be the ultimate dataset or datasets that you would like to see in the Digital Music Observatory?

As my answer to the previous question suggests, my main concern is the development of a trustworthy AI framework. Acknowledging the national and cultural diversity of the European Union, it is essential to enable access to data that takes into account such diversities and the priorities of smaller stakeholders as well. This type of data needs to be comprehensive and well-maintained, and I believe that with curators’ priorities and the development of an easily accessible, open API, we are moving in the right direction.

Our API contains rich processing and descriptive metadata besides our high-quality indicators.

Join us

We Need More Reliable Datasets on the Urban Heat Resilience and Disaster Risk Reduction

Mon, 07 Jun 2021 20:00:00 +0000

Suzan Sidal is working in the service design team on validating user needs and building a sustainable business model for our observatory.

As a consultant, what type of data do you usually use in your work at ECORYS?

We work with a great variety of data – both from qualitative and quantitative sources – that we retrieve from publicly available sources or get through our clients. Since we are a public policy consultancy, most of the datasets are related to government reports, policies, statistics or surveys that we analyse and assess within a specific timeframe. Oftentimes, we gather open data like non-textual or numeric, such as maps and satellite images; so-called “raw data,” like weather, geospatial and environmental data; or data such as that generated in research like genomes, medical data, mathematical and scientific formulas.

If you were able to pick, what would be the ultimate dataset, or datasets that you would like to see in the Green Deal Data Observatory?

I would like to see more data on the consequences and impact of increasing drought and urban heat in our cities in the Green Deal Data Observatory. Because of the complexity of rapidly developing metropolitan regions and the uncertainty associated with climate change, we need to explore more climate change adaptation and mitigation activities, or disaster risk reduction, not only climate change itself.

See our drought case study on how we combine very different data in our observatory

We need more reliable datasets on the effect of global warming on urban resilience and more indicators to inform stakeholders on disaster risk reduction. The Green Deal Observatory could build indexes for public and private entities once we would have all the relevant data at hand. With this project, we could explore many possibilities to actually utilise open data for a common and societal good, working towards a great social cause.

Why did you decide to join the challenge and why do you think that this would be a game changer for policymakers and for business?

As a consultant for many socially relevant projects, everyday I see the importance of high quality and diverse datasets. I joined the challenge to contribute to significant causes enabled through the Green Deal Data Observatory and Economy Data Observatory. We can all benefit from the usage of open data, which is, in my opinion, a prerequisite for open government partnerships.

I believe that through our work and through open data collaborations, we show a good example for a cultural change in the relationship between citizens and the state, which can contribute to more transparency, more participation and more intensive cooperation.

The access and analysis of open data for the general public would make political action more transparent and more comprehensible. This can lead to greater accountability and a sense of duty on the part of public officials to the general public, which in turn can lead to greater acceptance of government action and strengthen the public’s trust in their government and administration.

Is there a number that recently surprised you? What was it?

Climate change is increasing people’s exposure to heat. Extreme temperature events have been documented to be rising in frequency, duration, and magnitude over the world. The number of persons exposed to heatwaves grew by roughly 125 million between 2000 and 2016.

Sydney by Marek Piwnicki Unplash License

From your experience, what do you think the greatest problem with open data in 2021 will be?

I see two great problems with the use of open data. The first one is the low level of exploitation. The other is the lack of transparency in data processing.

The use of open data should be transparent and meet high quality standards. If we want to enable communities to use it for solving local problems, we must do two things. First, data must be made easy to use (or actionable), and second, we have to increase public awareness and offer training for use. Furthermore, governments should release data in usable formats that follow open data guidelines. Currently, there is very little effort made at the community level to encourage the reuse of public data for the public good.

What can our automated data observatories do to make open data more credible in the European economic policy community and be more accepted as verified information?

Almost nothing is being done to help communities build the capability to analyze and implement open data without relying on technology.

Our API contains rich processing and descriptive metadata besides our high-quality indicators.

This is a critical task that the our fledlging data Observatories, the Digital Music Observatory, Green Deal Data Observatory and Economy Data Observatory, may be able to help with. Facilitating private-public partnerships is one step to encourage the data community to work with valuable open data. However, transparency and a high level quality assurance step must be given. In a joint collaboration with data curators, developers, technical specialists and academics, the datasets should be retrieved, cleaned and assessed in order to deliver efficient, relevant and credible information. The constant monitoring and regulation as well as compliance with data security guidelines are indispensable.

Join us

Comparing Data to Oil is a Cliché: Crude Oil Has to Go Through a Number of Steps and Pipes Before it Becomes Useful

Mon, 07 Jun 2021 10:00:00 +0000

As a developer at rOpenGov, and as an economic sociologist, what type of data do you usually use in your work?

Generally speaking, people’s access to (or inequalities in accessing) different types of resources and their ability in transforming these resources to other types of resources is what interests me. The data I usually work with is the kind of data that is actually nicely covered by existing rOpenGov tools: data about population demographics and administrative units from Statistics Finland, statistical information on welfare and health from Sotkanet and also data from Eurostat. Aside from these a lot of information is of course data from surveys and texts scraped from the internet.

We are placing the growing number of rOpenGov tools in a modern application with a user-friendly service and a modern data API.

In your ideal data world, what would be the ultimate dataset, or datasets that you would like to see in the Music Data Observatory?

Late spring and early summer time is, at least for me, defined by the Eurovision Song Contest. Every year watching the contest makes me ponder the state of the music industry in my home country Finland as well as in Europe. Was the song produced by homegrown talent or was it imported? Was it better received by the professional jury or the public? How well does the domestic appeal of an artist translate to the international stage? Many interesting phenomena are difficult to quantify in a meaningful way and writing a catchy song with international appeal is probably more an art than a science. Nevertheless that should not deter us from trying as music, too, is bound by certain rules and regularities that can be researched.

Music, too, is bound by certain rules and regularities that can be researched. Our Digital Music Observatory and its Listen Local experimental App does this exactly, and we would love to create Eurovision musicology datasets. Photo: Eurovision Song Contest 2021 press photo by Jordy Brada

Why did you decide to join the EU Datathon challenge team and why do you think that this would be a game changer for researchers and policymakers?

The challenge has, in my opinion, great potential in leading by example when it comes to open data access and reproducible research. Comparing data to oil is a common phrase but fitting in the sense that crude oil has to go through a number of steps and pipes before it becomes useful. Most users and especially policymakers appreciate ease-of-use of the finished product, but the quality of the product and the process must also be guaranteed somehow. Openness and peer-review practices are the best guarantors in the field of data, just as industrial standards and regulations are in the oil industry.

We provide many layers of fully transparent quality control about the data we are placing in our data APIs and provide for our end-users.

Join us

Join our open collaboration Music Data Observatory team as a data curator, developer or business developer. More interested in antitrust, innovation policy or economic impact analysis? Try our Economy Data Observatory team! Or your interest lies more in climate change, mitigation or climate action? Check out our Green Deal Data Observatory team!

Join Copernicus Climate Data Store Data with Socio-Economic and Opinion Poll Data

Sun, 06 Jun 2021 10:00:00 +0000

In this series of blogposts we will show how to collect environmental data from the EU’s Copernicus Climate Data Store, and bring it to a data format that you can join with Eurostat’s socio-economic and environmental data. We have shown in a previous blogpost how to connect this to survey (opinion poll) and tax data, and a real policy problem in Belgium. We will create now subsequent tutorials to do more!

But first, why are we doing this? The European Union and its members states are releasing every year more and more data for open re-use since 2003, yet these are often not used in the EU’s data dissemination projects (the observatories) or in EU-funded research. We believe that there are many reasons behind this. Whilst more and more people can conduct business, scientific or policy analysis programmatically or with statistical software, knowledge how to systematically collect the data from the exponentially growing availability is not everybody’s specialty. And the lack of documentation, and high re-processing and validation need for open data is another drawback.

rOpenGov has long been producing high-quality, peer-reviewed R packages to work with open data, but their use is not for all. In an open collaboration, where you can join, too, rOpenGov teamed up with open source developers, knowledgeable data curators, and a service developer team lead by the Dutch reproducible research start-up Reprex to create a sustainable infrastructure that is permanently collecting, processing, documenting and visualizing open data. What we do is that we access open data (that is not always available for direct download) and re-process it to usable data that is tidy to be integrated with your existing data or databases. We are competing for the EU Datathon Challenge 1: supporting a European Green Deal agenda with open data as a service, and research as a servcie, and you are more than welcome to join our effort as a developer, a data curator, or as an occasional contributor to open government packages.

Register to the Copernicus Climate Data Store

Koen Hufkens, Reto Stauffer and Elio Campitelli created the ecmwfr R package for programmatically accessing the Copernicus Data Store service. Follow the CDS Functionality vignette to get started.

You will need to create a Register yourself for CDS services after accepting the Terms and conditions.

wf_set_key(user:  "12345", 
           key:  "00000000-aaaa-b1b1-0000-a1a1a1a1a1a1", 
           service:  "cds")

You can check if you were successful with:

ecmwfr::wf_get_key(user:  "12345", service:  "cds")

Get the Data

Let us formulate our first request:

request_lai_hv_2019_06 <- list(
  "dataset_short_name":  "reanalysis-era5-land-monthly-means",
  "product_type"  :  "monthly_averaged_reanalysis",
  "variable"      :  "leaf_area_index_high_vegetation",
  "year"          :  "2019",
  "month"         :   "06",
  "time"          :  "00:00",
  "area"          :  "70/-20/30/60",
  "format"        :  "netcdf",
  "target"        :  "demo_file.nc")

lai_hv_2019_06.nc  <- wf_request(user:  "<your_ID>",
                     request:  request_lai_hv_2019_06 ,
                     transfer:  TRUE,
                     path:  "data-raw",
                     verbose:  FALSE)

Effective Leaf Area Index

You can find this data either in global computer raster images, or in re-processed monthly averages. Working with the raw data is not very practical – in case of cloudy weather you have missing data, and the files are extremely huge for a personal computer. For the purposes of our Green Deal Data Observatory the monthly average values are far more practical, which are called monthly_averaged_reanalysis product types.

For compatibility with other R packages, convert the data with the from raster package from rSpatial.org.

lai_file <- here::here( "data-raw", "demo_file.nc")
lai_raster <- raster::raster(lai_file)

## Loading required namespace: ncdf4

Let us convert this to a SpatialDataPointsDataFrame class, which is an augmented data frame class with coordinates.

LAI_df <- raster::rasterToPoints(lai_raster, fun=NULL, spatial=TRUE)

Get The Map

With the help fo rOpenGov, we are creating various R packages to programmatically access open data and put them into the right format. The popular eurostat package is not only useful to download data from Eurostat, but also to map it.

In this case, we want to create regional maps. Europe has five levels of geographical regions: NUTS0 for countries, NUTS1 for larger areas like states, provinces; NUTS2 for smaller areas like countries, NUTS3 for even smaller areas. The LAU level contains settlemens and their surrounding areas.

Country borders change sometimes (think about the unification of Germany, or the breakup of Czechoslovakia and Yugoslavia), but they are relatively stable entities. Sub-national regional border change very-very frequently – since 2000 there were many thousand changes in Europe. This means that you must choose one regional boundary definition. The latest edition is NUTS2021 but most of the data available is still in the NUTS2016 format, and often you will find NUTS2013 or even NUTS2010 data around. Our Green Deal Data Observatory uses the NUTS2016 definition, because it is far the most used in 2021. An offspring of the eurostat package, regions helps you take care of NUTS changes when you work, and can convert your data to NUTS2021 if you later need it.

## sf at resolution 1:60 read from local file

## Warning in eurostat::get_eurostat_geospatial(resolution:  "60", nuts_level: 
## "2", : Default of 'make_valid' for 'output_class="sf"' will be changed in the
## future (see function details).

plot(map_nuts_2)

Our measurement of the average Effective Leaf Area Index is a raster data, it is given for many points of Europe’s map. What we need to do is to overlay this raster information of the statistical map of Europe. We use the excellent sp: R Classes and Methods for Spatial Data package for this purpose. The sp::over() function decides if a point of Leaf Area Index measurement falls into the polygon (shape) of a particular NUTS2 regions, for example, Zuid-Holland or South Holland in the Netherlands, or Saarland in Germany, or not. Then it averages with the mean() function those measurements falling in the area.

LAI_nuts_2:  sp::over(sp::geometry(
  as(map_nuts_2, 'Spatial')), 
  LAI_df,
  fn=mean)

Let’s call the average LAI index lai, and bind it to the Eurostat map:

names(LAI_nuts_2)[1] <- "lai"
LAI_sfdf <- bind_cols ( map_nuts_2, LAI_nuts_2 )

If you want to work with the data in a numeric context, you do not need the geographical information, and you can “downgrade” the SpatialDataPointsDataFrame to a simple data frame.

set.seed(2019) #to always see the same sample
LAI_sfdf %>%
  as.data.frame() %>%
  select ( all_of(c("NUTS_NAME", "NUTS_ID", "lai")) ) %>%
  sample_n(10)

##                      NUTS_NAME NUTS_ID lai
## 281                       Vest    RO42  NA
## 125                     Kassel    DE73  NA
## 69              Friesland (NL)    NL12  NA
## 237 Agri, Kars, Igdir, Ardahan    TRA2  NA
## 273                East Anglia    UKH1  NA
## 119                Prov. Liège    BE33  NA
## 61                   Bourgogne    FRC1  NA
## 275                      Essex    UKH3  NA
## 282                   Istanbul    TR10  NA
## 174                    Leipzig    DED5  NA

We’ll plot the map with ggplot2.

library(ggplot2)
library(sf)
ggplot(data=LAI_sfdf) + 
  geom_sf(aes(fill=lai),
          color="dim grey", size=.1) + 
  scale_fill_gradient( low: "#FAE000", high:  "#00843A") +
  guides(fill:  guide_legend(reverse=T, title:  "LAI")) +
  labs(title="Leaf Area Index",
       subtitle:  "High vegetation half, NUTS2 regional avareage values",
       caption="\ua9 EuroGeographics for the administrative boundaries 
                \ua9 Copernicus Data Service, June 2019 average values
                Tutorial and ready-to-use data on greendeal.dataobservatory.eu") +
  theme_light() + theme(legend.position=c(.88,.78)) +
  coord_sf(xlim=c(-22,48), ylim=c(34,70))

Data Integrity

Our Green Deal Data Observatory has a data API where we place the new data with metadata for programmatic download in CSV, JSON or even with SQL queries. For data integrity purposes, we are placing an authoritative copy on Zenodo (Green Deal Data Observatory Community). You can use this for scientific citations. We are also happy if you place your own climate policy related research data here, so that we can include it in our observatory. In our subsequent tutorials, we will show how to do this programmatically in R. This particular dataset (not only with the month June, which we selected to streamline the tutorial) is available here with the digital object identifier doi.org/10.5281/zenodo.4903940.

Join us

Creating Algorithmic Tools to Interpret and Communicate Open Data Efficiently

Fri, 04 Jun 2021 10:00:00 +0000

As a developer at rOpenGov, what type of data do you usually use in your work?

As an academic data scientist whose research focuses on the development of general-purpose algorithmic methods, I work with a range of applications from life sciences to humanities. Population studies play a big role in our research, and often the information that we can draw from public sources - geospatial, demographic, environmental - provides invaluable support. We typically use open data in combination with sensitive research data but some of the research questions can be readily addressed based on open data from statistical authorities such as Statistics Finland or Eurostat.

In your ideal data world, what would be the ultimate dataset, or datasets that you would like to see in the Music Data Observatory?

One line of our research analyses the historical trends and spread of knowledge production, in particular book printing based on large-scale metadata collections. It would be interesting to extend this research to music, to understand the contemporary trends as well as the broader historical developments. Gaining access to a large systematic collection of music and composition data from different countries across long periods of time would make this possible.

Why did you decide to join the challenge and why do you think that this would be a game changer for researchers and policymakers?

Joining the challenge was a natural development based on our overall activities in this area; the rOpenGov project has been around for a decade now, since the early days of the broader open data movement. This has also created an active international developer network and we felt well equipped for picking up the challenge. The game changer for researchers is that the project highlights the importance of data quality, even when dealing with official statistics, and provides new methods to solve these issues efficiently through the open collaboration model. For policymakers, this provides access to new high-quality curated data and case studies that can support evidence-based decision-making.

Do you have a favorite, or most used open governmental or open science data source? What do you think about it? Could it be improved?

Regarding open government data, one of my favorites is not a single data source but a data representation standard. The px format is widely used by statistical authorities in various countries, and this has allowed us to create R tools that allow the retrieval and analysis of official statistics from many countries across Europe, spanning dozens of statistical institutions. Standardization of open data formats allows us to build robust algorithmic tools for downstream data analysis and visualization. Open government data is still too often shared in obscure, non-standard or closed-source file formats and this is creating significant bottlenecks for the development of scalable and interoperable AI and machine learning methods that can harness the full potential of open data.

Regarding open government data, one of my favorites is not a single data source but a data representation standard, the Px format.

From your perspective, what do you see being the greatest problem with open data in 2021?

Although there are a variety of open data sources available (and the numbers continue to increase), the availability of open algorithmic tools to interpret and communicate open data efficiently is lagging behind. One of the greatest challenges for open data in 2021 is to demonstrate how we can maximize the potential of open data by designing smart tools for open data analytics.

What can our automated data observatories do to make open data more credible in the European economic policy community and be accepted as verified information?

The role of the professional network backing up the project, and the possibility of getting critical feedback and later adoption by the academic communities will support the efforts. Transparency of the data harmonization operations is the key to credibility, and will be further supported by concrete benchmarks that highlight the critical differences in drawing conclusions based on original sources versus the harmonized high-quality data sets.

We need to get critical feedback and later adoption by the academic communities.

How we can ensure the long-term sustainability of the efforts?

The extent of open data space is such that no single individual or institution can address all the emerging needs in this area. The open developer networks play a huge role in the development of algorithmic methods, and strong communities have developed around specific open data analytical environments such as R, Python, and Julia. These communities support networked collaboration and provide services such as software peer review. The long-term sustainability will depend on the support that such developer communities can receive, both from individual contributors as well as from institutions and governments.

Join us

Economic and Environment Impact Analysis, Automated for Data-as-Service

Thu, 03 Jun 2021 16:00:00 +0000

We have released a new version of iotables as part of the rOpenGov project. The package, as the name suggests, works with European symmetric input-output tables (SIOTs). SIOTs are among the most complex governmental statistical products. They show how each country’s 64 agricultural, industrial, service, and sometimes household sectors relate to each other. They are estimated from various components of the GDP, tax collection, at least every five years.

This code tutorial is not outdated, but the iotables R package has a new release with more environmental impact analysis featues.

Click to expand table of contents of the post

Table of Contents

SIOTs offer great value to policy-makers and analysts to make more than educated guesses on how a million euros, pounds or Czech korunas spent on a certain sector will impact other sectors of the economy, employment or GDP. What happens when a bank starts to give new loans and advertise them? How is an increase in economic activity going to affect the amount of wages paid and and where will consumers most likely spend their wages? As the national economies begin to reopen after COVID-19 pandemic lockdowns, is to utilize SIOTs to calculate direct and indirect employment effects or value added effects of government grant programs to sectors such as cultural and creative industries or actors such as venues for performing arts, movie theaters, bars and restaurants.

Making such calculations requires a bit of matrix algebra, and understanding of input-output economics, direct, indirect effects, and multipliers. Economists, grant designers, policy makers have those skills, but until now, such calculations were either made in cumbersome Excel sheets, or proprietary software, as the key to these calculations is to keep vectors and matrices, which have at least one dimension of 64, perfectly aligned. We made this process reproducible with iotables and eurostat on rOpenGov

Our iotables package creates direct, indirect effects and multipliers programatically. Our observatory will make those indicators available for all European countries.

Accessing and tidying the data programmatically

The iotables package is in a way an extension to the eurostat R package, which provides a programmatic access to the Eurostat data warehouse. The reason for releasing a new package is that working with SIOTs requires plenty of meticulous data wrangling based on various metadata sources, apart from actually accessing the data itself. When working with matrix equations, the bar is higher than with tidy data. Not only your rows and columns must match, but their ordering must strictly conform the quadrants of the a matrix system, including the connecting trade or tax matrices.

When you download a country’s SIOT table, you receive a long form data frame, a very-very long one, which contains the matrix values and their labels like this:

## Table naio_10_cp1700 cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds

# we save it for further reference here 
saveRDS(naio_10_cp1700, "not_included/naio_10_cp1700_date_code_FF.rds")

# should you need to retrieve the large tempfiles, they are in 
dir (file.path(tempdir(), "eurostat"))

dplyr::slice_head(naio_10_cp1700, n:  5)

## # A tibble: 5 x 7
##   unit    stk_flow induse  prod_na geo       time        values
##   <chr>   <chr>    <chr>   <chr>   <chr>     <date>       <dbl>
## 1 MIO_EUR DOM      CPA_A01 B1G     EA19      2019-01-01 141873.
## 2 MIO_EUR DOM      CPA_A01 B1G     EU27_2020 2019-01-01 174976.
## 3 MIO_EUR DOM      CPA_A01 B1G     EU28      2019-01-01 187814.
## 4 MIO_EUR DOM      CPA_A01 B2A3G   EA19      2019-01-01      0 
## 5 MIO_EUR DOM      CPA_A01 B2A3G   EU27_2020 2019-01-01      0

The metadata reads like this: the units are in millions of euros, we are analyzing domestic flows, and the national account items B1-B2 for the industry A01. The information of a 64x64 matrix (the SIOT) and its connecting matrices, such as taxes, or employment, or C**O₂ emissions, must be placed exactly in one correct ordering of columns and rows. Every single data wrangling error will usually lead in an error (the matrix equation has no solution), or, what is worse, in a very difficult to trace algebraic error. Our package not only labels this data meaningfully, but creates very tidy data frames that contain each necessary matrix of vector with a key column.

iotables package contains the vocabularies (abbreviations and human readable labels) of three statistical vocabularies: the so called COICOP product codes, the NACE industry codes, and the vocabulary of the ESA2010 definition of national accounts (which is the government equivalent of corporate accounting).

Our package currently solves all equations for direct, indirect effects, multipliers and inter-industry linkages. Backward linkages show what happens with the suppliers of an industry, such as catering or advertising in the case of music festivals, if the festivals reopen. The forward linkages show how much extra demand this creates for connecting services that treat festivals as a ‘supplier’, such as cultural tourism.

Example

## Downloading employment data from the Eurostat database.

## Table lfsq_egan22d cached at C:\Users\...\Temp\RtmpGQF4gr/eurostat/lfsq_egan22d_date_code_FF.rds

and match it with the latest structural information on from the Symmetric input-output table at basic prices (product by product) Eurostat product. A quick look at the Eurostat website already shows that there is a lot of work ahead to make the data look like an actual Symmetric input-output table. Download it with iotable_get() which does basic labelling and preprocessing on the raw Eurostat files. Because of the size of the unfiltered dataset on Eurostat, the following code may take several minutes to run.

sk_io <-  iotable_get ( labelled_io_data:  NULL, 
                        source:  "naio_10_cp1700", geo:  "SK", 
                        year:  2015, unit:  "MIO_EUR", 
                        stk_flow:  "TOTAL",
                        labelling:  "iotables" )

## Reading cache file C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds

## Table  naio_10_cp1700  read from cache file:  C:\Users\..\Temp\RtmpGQF4gr/eurostat/naio_10_cp1700_date_code_FF.rds

## Saving 808 input-output tables into the temporary directory
## C:\Users\...\Temp\RtmpGQF4gr

## Saved the raw data of this table type in temporary directory C:\Users\...\Temp\RtmpGQF4gr/naio_10_cp1700.rds.

The input_coefficient_matrix_create() creates the input coefficient matrix, which is used for most of the analytical functions.

a_i**j: X_i**j / x_j

It checks the correct ordering of columns, and furthermore it fills up 0 values with 0.000001 to avoid division with zero.

input_coeff_matrix_sk <- input_coefficient_matrix_create(
  data_table:  sk_io
)

## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Then you can create the Leontieff-inverse, which contains all the structural information about the relationships of 64x64 sectors of the chosen country, in this case, Slovakia, ready for the main equations of input-output economics.

I_sk <- leontieff_inverse_create(input_coeff_matrix_sk)

And take out the primary inputs:

primary_inputs_sk <- coefficient_matrix_create(
  data_table:  sk_io, 
  total:  'output', 
  return:  'primary_inputs')

## Columns and rows of real_estate_imputed_a, extraterriorial_organizations are all zeros and will be removed.

Now let’s see if there the government tries to stimulate the economy in three sectors, agricultulre, car manufacturing, and R&D with a billion euros. Direct effects measure the initial, direct impact of the change in demand and supply for a product. When production goes up, it will create demand in all supply industries (backward linkages) and create opportunities in the industries that use the product themselves (forward linkages.)

direct_effects_create( primary_inputs_sk, I_sk ) %>%
  select ( all_of(c("iotables_row", "agriculture",
                    "motor_vechicles", "research_development"))) %>%
  filter (.data$iotables_row %in% c("gva_effect", "wages_salaries_effect", 
                                    "imports_effect", "output_effect"))

##            iotables_row agriculture motor_vechicles research_development
## 1        imports_effect   1.3684350       2.3028203            0.9764921
## 2 wages_salaries_effect   0.2713804       0.3183523            0.3828014
## 3            gva_effect   0.9669621       0.9790771            0.9669467
## 4         output_effect   2.2876287       3.9840251            2.2579634

Car manufacturing requires much imported components, so each extra demand will create a large importing activity. The R&D will create a the most local wages (and supports most jobs) because research is job-intensive. As we can see, the effect on imports, wages, gross value added (which will end up in the GDP) and output changes are very different in these three sectors.

This is not the total effect, because some of the increased production will translate into income, which in turn will be used to create further demand in all parts of the domestic economy. The total effect is characterized by multipliers.

Then solve for the multipliers:

multipliers_sk <- input_multipliers_create( 
  primary_inputs_sk %>%
    filter (.data$iotables_row: = "gva"), I_sk )

And select a few industries:

set.seed(12)
multipliers_sk %>% 
  tidyr::pivot_longer ( -all_of("iotables_row"), 
                        names_to:  "industry", 
                        values_to:  "GVA_multiplier") %>%
  select (-all_of("iotables_row")) %>%
  arrange( -.data$GVA_multiplier) %>%
  dplyr::sample_n(8)

## # A tibble: 8 x 2
##   industry               GVA_multiplier
##   <chr>                           <dbl>
## 1 motor_vechicles                  7.81
## 2 wood_products                    2.27
## 3 mineral_products                 2.83
## 4 human_health                     1.53
## 5 post_courier                     2.23
## 6 sewage                           1.82
## 7 basic_metals                     4.16
## 8 real_estate_services_b           1.48

Vignettes

The Germany 1990 provides an introduction of input-output economics and re-creates the examples of the Eurostat Manual of Supply, Use and Input-Output Tables, by Jörg Beutel (Eurostat Manual).

The United Kingdom Input-Output Analytical Tables Daniel Antal, based on the work edited by Richard Wild is a use case on how to correctly import data from outside Eurostat (i.e., not with eurostat::get_eurostat()) and join it properly to a SIOT. We also used this example to create unit tests of our functions from a published, official government statistical release.

Finally, Working With Eurostat Data is a detailed use case of working with all the current functionalities of the package by comparing two economies, Czechia and Slovakia and guides you through a lot more examples than this short blogpost.

Our package was originally developed to calculate GVA and employment effects for the Slovak music industry, and similar calculations for the Hungarian film tax shelter. We can now programatically create reproducible multipliers for all European economies in the Digital Music Observatory, and create further indicators for economic policy making in the Economy Data Observatory.

Environmental Impact Analysis

Our package allows the calculation of various economic policy scenarios, such as changing the VAT on meat or effects of re-opening music festivals on aggregate demand, GDP, tax revenues, or employment. But what about the C**O₂, methane and other greenhouse gas effects of the reopening festivals, or the increasing meat prices?

Technically our package can already calculate such effects, but to do so, you have to carefully match further statistical vocabulary items used by the European Environmental Agency about air pollutants and greenhouse gases.

The last released version of iotables is Importing and Manipulating Symmetric Input-Output Tables (Version 0.4.4). Zenodo. https://doi.org/10.5281/zenodo.4897472, but we are already working on a new major release. (Download the BibLaTeX entry.) In that release, we are planning to build in the necessary vocabulary into the metadata functions to increase the functionality of the package, and create new indicators for our Green Deal Data Observatory. This experimental data observatory is creating new, high quality statistical indicators from open governmental and open science data sources that has not seen the daylight yet.

rOpenGov and the EU Datathon Challenges

rOpenGov, Reprex, and other open collaboration partners teamed up to build on our expertise of open source statistical software development further: we want to create a technologically and financially feasible data-as-service to put our reproducible research products into wider user for the business analyst, scientific researcher and evidence-based policy design communities.

rOpenGov is a community of open governmental data and statistics developers with many packages that make programmatic access and work with open data possible in the R language. Reprex is a Dutch-startup that teamed up with rOpenGov and other open collaboration partners to create a technologically and financially feasible service to exploit reproducible research products for the wider business, scientific and evidence-based policy design community. Open data is a legal concept - it means that you have the rigth to reuse the data, but often the reuse requires significant programming and statistical know-how. We entered into the annual EU Datathon competition in all three challenges with our applications to not only provide open-source software, but daily updated, validated, documented, high-quality statistical indicators as open data in an open database. Our iotables package is one of our many open-source building blocks to make open data more accessible to all.

Join our Green Deal Data Observatory collaboration!

The Green Deal Data Observatory is Contesting the EU Datathon 2021 Prize

Fri, 21 May 2021 20:00:00 +0000

Reprex, a Dutch start-up enterprise formed to utilize open source software and open data, is looking for partners in an agile, open collaboration to win at least one of the three EU Datathon Prizes. We are looking for policy partners, academic partners and a consultancy partner. Our project is based on agile, open collaboration with three types of contributors.

With our competing prototypes we want to show that we have a research automation technology that can find open data, process it and validate it into high-quality business, policy or scientific indicators, and release it with daily refreshments in a modern API.

We are looking for institutions to challenge us with their data problems, and sponsors to increase our capacity. Over then next 5 months, we need to find a sustainable business model for a high-quality and open alternative to other public data programs.

The EU Datathon 2021 Challenge

To take part, you should propose the development of an application that links and uses open datasets. - our data curator team
Your application … is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.” - this application is developed by our technology contributors
Your application should showcase opportunities for concrete business models or social enterprises. - our service development team is working to make this happen!
We use open source software and open data. The applications are hosted on the cloud resources of Reprex, an early-stage technology startup currently building a viable, open-source, open-data business model to create reproducible research products.
We are working together with experts in the domain as curators (check out our guidelines if you want to join: Data Curators: Get Inspired!).
Our development team works on an open collaboration basis. Our indicator R packages, and our services are developed together with rOpenGov.

Mission statement

We want to win an EU Datathon prize by processing the vast, already-available governmental and scientific open data made usable for policy-makers, scientific researchers, and business researcher end-users.

“To take part, you should propose the development of an application that links and uses open datasets. Your application should showcase opportunities for concrete business models or social enterprises. It is also expected to find suitable new approaches and solutions to help Europe achieve important goals set by the European Commission through the use of open data.”

We aim to win at least one first prize in the EU Datathon 2021. We are contesting all three challenges, which are related to the EU’s official strategic policies for the coming decade.

Challenge 1: A European Grean Deel

Our Green Deal Data Observatory connects socio-economic and environmental data to help understanding and combating climate change.

Challenge 1: A European Green Deal, with a particular focus on the The European Climate Pact, the Organic Action Plan, and the New European Bauhaus, i.e., mitigation strategies.

Climate change and environmental degradation are an existential threat to Europe and the world. To overcome these challenges, the European Union created the European Green Deal strategic plan, which aims to make the EU’s economy sustainable by turning climate and environmental challenges into opportunities and making the transition just and inclusive for all.

Our Green Deal Data Observatory is a modern reimagination of existing ‘data observatories’; currently, there are over 70 permanent international data collection and dissemination points. One of our objectives is to understand why the dozens of the EU’s observatories do not use open data and reproducible research. We want to show that open governmental data, open science, and reproducible research can lead to a higher quality and faster data ecosystem that fosters growth for policy, business, and academic data users.

We provide high quality, tidy data through a modern API which enables data flows between public and proprietary databases. We believe that introducing Open Policy Analysis standards with open data, open-source software, and research automation, can help the Green Deal policymaking process. Our collaboration is open for individuals, citizens scientists, research institutes, NGOS, and companies.

Other Challenges

Challenge 2: An economy that works for people, with a particular focus on the Single market strategy. Big data and automation create new inequalities and injustices and have the potential to create a jobless growth economy. Our Economy Data Observatory is a fully automated, open source, open data observatory that produces new indicators from open data sources and experimental big data sources, with authoritative copies and a modern API.
Challenge 3: A Europe fit for the digital age, with a particular focus Artificial Intelligence, the European Data Strategy, the Digital Services Act, Digital Skills and Connectivity. The Digital Music Observatory (DMO) is a fully automated, open source, open data observatory that creates public datasets to provide a comprehensive view of the European music industry. It provides high-quality and timely indicators in all four pillars of the planned official European Music Observatory as a modern, open source and largely open data-based, automated, API-supported alternative solution for this planned observatory. The insight and methodologies we are refining in the DMO are applicable and transferable to about 60 other data observatories funded by the EU which do not currently employ governmental or scientific open data.

Our Product/Market Fit was validated in the world’s 2nd ranked university-backed incubator program, the Yes!Delft AI Validation Lab. We are currently developing this project with the help of the JUMP European Music Market Accelerator program.

Problem Statement

The EU has an 18-year-old open data regime and it makes public taxpayer-funded data in the values of tens of billions of euros per year; the Eurostat program alone handles 20,000 international data products, including at least 5,000 pan-European environmental indicators.

As open science principles gain increased acceptance, scientific researchers are making hundreds of thousands of valuable datasets public and available for replication every year.

The EU, the OECD, and UN institutions run around 100 data collection programs, so-called ‘data observatories’ that more or less avoid touching this data, and buy proprietary data instead. Annually, each observatory spends between 50 thousand and 3 million EUR on collecting untidy and proprietary data of inconsistent quality, while never even considering open data.

Our automated data observatories are modern reimaginations of the existing observatories that do not use open data and research automation.

The problem with the current EU data strategy is that while it produces enormous quantities of valuable open data, in the absence of common basic data science and documentation principles, it seems often cheaper to create new data than to put the existing open data into shape.

This is an absolute waste of resources and efforts. With a few R packages and our deep understanding of advanced data science techniques, we can create valuable datasets from unprocessed open data. In most domains, we are able to repurpose data originally created for other purposes at a historical cost of several billions of euros, converting these unused data assets into valuable datasets that can replace tens of millions’ worth of proprietary data.

What we want to achieve with this project – and we believe such an accomplishment would merit one of the first prizes - is to add value to a significant portion of pre-existing EU open data (for example, available on data.europa.eu/data) by re-processing and integrating them into a modern, tidy database with an API access, and to find a business model that emphasises a triangular use of data in 1. business, 2. science and 3. policy-making. Our mission is to modernize the concept of data observatories.

Is Drought Risk Uninsurable?

Fri, 23 Apr 2021 00:00:00 +0000

Climate change is real and it is everywhere. Whereas island nations in the Pacific are threatened with rising sea levels, Europe suffers from ever more frequent scorching summers and resulting drought. Take the case of Belgium, where heat waves in 2018 or 2020 have exacerbated an already fragile drought risk profile. An all too tangible effect is that houses built in areas where groundwater reservoirs are dwindling start to rupture. What adds insult to injury is that insurers appear unwilling to pay for damages: these climate-related risks simply did not feature in insurance policies made up decades ago. The public and the media have called upon the secretary of state responsible for consumer protection to come up with a solution. (Download this document in pdf.)

The Belgian insurance sector and government are currently investigating how to address the ecological and financial issue. Should the risk premium be raised on all insurance policies in an effort to spread risk, or should only policy holders in designated risk areas be subject to a raise in premia? Should urban planning initiatives and real estate projects be required to assess these new types of risk beforehand?

Driven by the Open Data Directive, we went in search for data at government websites such as waterinfo.be. That proved harder than you would want, with quite a number of technological barriers to cross. We independently explored the matter ourselves and came up with this: a dynamic map that pictures the spatial distribution of drought risk - as measured by a climate indicator known as the standardised recipitation-evapotranspiration index.

Actual drying soil.

This SPEI index, measured as a standardized variate, shows the deviations of the current climatic balance (precipitation minus evapotranspiration potential) in the long run and is presented on a monthly basis. As the SPEI in this form is more predictive for flood risk, we simply inverted the index to suggest a measure of drought risk¹.

Readers familiar with the “Kingdom by the sea” will remark that Belgium cannot possibly have a lack of precipitation. It rains more than the average Belgian cares for in the country. As a result, the water management system has historically been based on getting the water out as quickly as possible to the sea, in particular through the Ijzer, Schelde and Maas rivers. Add the abundance of concrete in the densely populated country - and its grossly mismanaged urban planning - and the capacity to hold water in surface and ground reservoirs is severely impaired. With climate change in full swing, these historical practices come back to haunt Belgium.

Are Belgians aware of climate risk?

We projected the public opinion data from Eurobarometer 90.2 (fieldwork: October-November 2018.) on the municipal map of Belgium. We used the answers to the multiple choice question QB1 Do you think that the following extreme weather events are due to climate change? We highlighted areas where people find it more likely to be exposed to Droughts and wildfires. We used the GESIS datafile (European Commission 2019) and used the (Antal 2021b, 2021a) packages to project the values to municipalities.

We see a weak spatial correlation between awareness of drought risk and actual draught risk. The least affected parts of Belgium appear least concerned. Despite its weakness, authorities and insurers can at least build their mitigation policies on a hypothesis of positive correlation. Of note is that concern for climate change effects follows regional, linguistic and other patterns. The map in particular suggests the Belgian provinces as markers for awareness.

Perception of likely drought.

Financial Capacity to Pay for Insurance

The next question we asked ourselves, was if the drought risk correlates with the ability to pay as distributed among local communities. Whether an insurance policy – or the regulation of insurance – attempts to provide cover on an individual level (through increased premia), or looks for local, regional or national mitigation strategies, the income/tax base might be an appropriate benchmark to test for financial capacity.

Financial capacity to mitigate drought risk.

The match between the (inverted) SPEI and total net income is less than perfect. Some of the areas most at risk coincide with the highest-income communities, but other threatened communities are low-income by Belgian standards. The actual risk awareness and the financial capacity to solve the problem are again only weakly correlated.^[2]

Correlation

Let’s have a look at the variables on NUTS3 level:

Correlation of the variables.

Average SPEI, which is a measure of increasing humidity, is negatively correlated with dry that we defined as -1 x avg_spei.
Dry areas, that are losing water, are less populous and more rich regions.
Dry_18 is a version of dry that only shows 12 months before the Eurobarometer survey about opinions on climate change effects, to see if the recent memory of actual weather conditions has had an affect of the perception of Belgians about these risk. It is seemingly not correlated with worries about floods or droughts.
Thedry_18 and the dry variables are largely correlated. One possible explanation is that the year before the survey was not an unusual period, it fit very well with the 2016-2020 trend.
Worries about extreme weather conditions are correlated with each other – i.e., some part of the population (concentrated geographically) is far more concerned with climate change than others.

The same on municipality (local administrative unit) level:

Correlation on the level of municipalities.

The correlations with opinion polling data are a little bit distorted, because the data is on NUTS2, and to bring it down to NUTS3 or LAU level would be a complicated small area statistical estimation task. We have also computed geospatial cross-correlation. Awareness of the climate problem and the dryness in 2018 were positively correlated in time – the drier the year was in an area, the more likely it was that people are aware of the problem; and the poorer areas were more likely to be afraid of this problem. The global spatial cross-correlation of the drying and local income was very low. This is a neutral situation: local income is not more concentrated to drying areas (which would be a lucky coincidence) nor concentrated in the relatively stable areas.

Generally, the problem map appears to be neutral to mildly favorable. The financial capacity to solve the problem is not working in the favor, nor against the problem, and awareness seems to be somewhat higher in the more affected areas.

The codes are in R/join_belgium_water_lau_dataset.R and R/join_belgium_water_nuts3_dataset.R.

Adverse Selection and Climate Solidarity

In addition to these historical analyses that put the drought risk in context, we are investigating whether climate data from integrated climate models might be harnessed to predict medium- to longer-term risk profiles on a spatially distributed basis. Urban planners, real estate promoters, individual households and governments will need to rely on such predictions to better adapt to climate change and reverse some of the earlier policy choices we mentioned.

To quote the Nobel Prize winning thoughts of Finn E. Kydland and Edward C. Prescott (Kydland and Prescott 1977):

The issues are obvious in many well-known problems of public policy. For example, suppose the socially desirable outcome is not to have houses built in a particular flood plain but, given that they are there, to take certain costly flood-control measures. If the government’s policy were not to build the dams and levees needed for flood protection and agents knew this was the case, even if houses were built there, rational agents would not live in the flood plains. But the rational agent knows that, if he and others build houses there, the government will take the necessary flood-control measures. Consequently, in the absence of a law prohibiting the construction of houses in the flood plain, houses are built there, and the army corps of engineers subsequently builds the dams and levees.

Our initial explorations at least suggest that leaving the resolution entirely to market forces, for example through increased property insurance premia may well lead to underinsurance in poorer areas that is dynamically inconsistent with government policy. If in particular severe drought will bankrupt farmers in such areas, eventually regional or national government will be forced to bail them out.

The other extreme approach, i.e., leaving the climate-change related damages entirely to the taxpayer, therefore does not seem feasible either with climate awareness and local income tax base only weakly correlating with the drought patterns. In addition, drought of course does not confine itself to municipal borders; the hydrological topology of the issue inherently implies a coordination problem between local, regional and federal entities passing the buck from one to another. One can imagine some form of solidarity and redistribution will be required to align interests and avoid adverse selection. To address these typical market failures, government will need to step in to allow these risks, that may be privately uninsurable, to be covered on a society-wide basis.

These problems are not unique to property damage. Similar problems arise in many student loan systems in the world (where it is desirable that the loan can be taken by arts students or future teachers, who may not have as high earning potential as easy-to-credit future lawyers, engineers, managers) or in many social security issues: a minimum level of health insurance for the unemployed and poor is desirable not only on the basis of humanity, but to avoid epidemic risks. Such special loan systems and special insurance systems are balancing some social welfare with individual welfare and individual risk considerations, and at the same time they try to avoid adverse selection, free-riding. We believe that our example can spark some ideas how a desirable social outcome can be aligned with the principles of insurance and personal responsibility.

In this case, on a longer term basis, incentives that may transfer water-intensive industrial and agricultural activities from the areas most at risk, could be called for, as well as better hydrological management to safeguard water reserves. We invite the authorities and relevant stakeholders to render the appropriate data needed to assess climate and drought evolution and to calculate risk premia scenarios and solidarity mechanisms open data, verified for quality through unit-tests and peer review.

References

Antal, Daniel. 2021a. Regions: Processing Regional Statistics. https://regions.danielantal.eu/.

———. 2021b. Retroharmonize: Ex Post Survey Data Harmonization. https://retroharmonize.dataobservatory.eu/.

Beguerı́a, Santiago, Sergio M Vicente-Serrano, Fergus Reig, and Borja Latorre. 2014. “Standardized Precipitation Evapotranspiration Index (SPEI) Revisited: Parameter Fitting, Evapotranspiration Models, Tools, Datasets and Drought Monitoring.” International Journal of Climatology 34 (10): 3001–23.

European Commission. 2019. “Eurobarometer 90.2 (2018).” GESIS Data Archive, Cologne. ZA7488 Data file Version 1.0.0, https://doi.org/10.4232/1.13289. https://doi.org/10.4232/1.13289.

Kydland, Finn E., and Edward C. Prescott. 1977. “Rules Rather Than Discretion: The Inconsistency of Optimal Plans.” Journal of Political Economy 85 (3): 473–91. http://www.jstor.org/stable/1830193.

Statbel. 2020. “Fiscal statistics on income.” Eurostat. https://statbel.fgov.be/en/open-data/fiscal-statistics-income.

Vicente-Serrano, Sergio M, Santiago Beguerı́a, and Juan I López-Moreno. 2010. “A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index.” Journal of Climate 23 (7): 1696–1718.

As a standardized variate, SPEI can be compared across space and time. The original calculation of SPEI is based on the FAO-56 Penman-Monteith method. Other relevant indicators might consider the soil composition for example: clay and lime soils tend to be more vulnerable to drought. We combined this ecological dimension with the socio-economic dimension to suggest that insurance premia design might be targeted to, say, income levels as well - or alternatively to real estate prices. See (Beguerı́a et al. 2014; Vicente-Serrano, Beguerı́a, and López-Moreno 2010) ↩︎

Connecting the Dots to Environmental Degradation Open Data

Thu, 11 Mar 2021 00:00:00 +0000

If you live in a polluted area, does it mean that you take climate change seriously? Following the Reprex Open Data Day 2021, we embarked on a quest to explore this question using a unique combination of micro-level data from Eurobarometer surveys, Eurostat’s sub-national socio-economic data and satellite imagery from European Environmental Agency (EEA) and NASA. Before venturing forth into the forest of open data we, as all visual creatures out there, first mapped the road ahead.

We used three sensory sources on pollution and deforestation, all of which are closely related to environmental degradation, to create these maps. In the first set of maps, we draw on EEA’s Air Quality e-Reporting data on environmental pollution (particulate matter 2.5 and 10) for the period 2014–2016. What makes these data complex is their organization on the level of the reporting stations. So, this means that we had to first figure out the nearest aerial distance from every reporting station to local administrative unit (LAU), assign the annual pollution levels to every LAU and, finally, create our fine-grained map. Using this approach, we are able to aggregate the data to any NUTS level and, with help of the retroharmonize and regions R packages, work with public opinion and sub-national data to tackle our initial question.

Below you will notice that findings are constrained to countries for which EEA commonly collects environmental data. Far from being Euro-centric, our project is inclusive of other countries and continents for which the pollution data is available – with the aforementioned packages we could work with any nation’s or larger regions data. In fact, we would like to invite contributors with greater knowledge of reliable data sources from all continents.

Our joined dataset allows hypothesis testing on how much people’s perception and attitudes to environmental degradation depends on the quality of the environment that surrounds them.

In the next map, we go beyond the EU/EEA/EU candidate focus to depict light pollution for the whole European continent. We used the Harmonized VIIRS nighttime light data for 2014–2018, which is a novel open source with calibrated global information on nightlight. This outstanding source offers an unparalleled opportunity to measure the intensity of the socioeconomic activities and urbanization. We showcase this in our map of estimated average size of urban areas for every LAU using DN values higher than 30. This is a tip of an iceberg as our mapping capabilities may extend to any available subnational data around the globe.

The VIIRS nighttime light dataset excels particularly in countries and regions where GDP estimation and desagregation is patchy or non-existent. We would like to find collaborators from Africa, the Arab World, the Caucasus and Latin America, where we have harmonized, individual level survey data and socio-econometric data, to join forces with us to build relevant sub-national regional dictionaries for the regions package, which can do the rest of the work.

Nighttime lights are accurate predictors of local income, energy use and contribution to carbon emissions.

In the final map, we use the Copernicus Tree Cover Density dataset to compute how much deforestation has taken place on the LAU level in Europe between 2015 and 2019. Using our regions package, these data could easily be paired with public opinion and NUTS-level data to analyze how deforestation influences individual attitudes on climate change.

Deforestration is a key factor in carbon emission, because trees store so much carbon. Any path to net zero carbon emission requires a vast re-forestration of the Earth.

As we can see, in most of Europe deforestation is ongoing. This is partly caused by effects of climate change, but partly further aggravate the situation as the fallen trees release previously captured CO2. For example, in Slovakia the Tatra mountains lost many trees in a devastating storm; such extreme weather conditions kill vulnerable tree cover, leading to soil errosion. Again, the Copernicus tree cover data available for the entire Earth, and our regions package only requires local geocoding and geographical vocabulary additions to allow analysis on almost all continents.

All this artwork barely scratches the surface of possibilities that mapping sensoring data could offer to NGOs, think-tanks, small enterprises as well as academic institutions. Most importantly, this powerful approach could help these actors effectively link patterns in environmental change to individual attitudes and subnational socio-economic data.

Regional Geocoding Harmonization Case Study - Regional Climate Change Awareness Datasets

Sat, 06 Mar 2021 00:00:00 +0000

library(regions)
library(lubridate)
library(dplyr)

if ( dir.exists('data-raw') ) {
  data_raw_dir <- "data-raw"
} else {
  data_raw_dir <- file.path("..", "..", "data-raw")
  }

Going beyond the national level

Let’s start with a dirty averaging by sub-national unit. The w1 weighting variable contains the post-stratification weight for the national samples. The Eurobarometer samples represent nations (with the exception of East and West Germany, Northern Ireland and Great Britain.) The average of the w1 variable is 1.00 for each sample, but it is not necessarily 1 for smaller territorial units. If sum(w)>1 for say, AT23 it only means that the AT23 region was undersampled relatively to the rest of Austria, and responses must be over-weighted in post-stratification.

There is no way to make the samples become regionally representative, and a correct post-stratification would require further data about the sampel design. But we can simply adjust to over/undersampling by making sure that oversampled territorial averages are proportionally increased and undersampled ones are decreased. [Another ‘dirty’ averaging would be the use of an unweighted average, but our method is better, because it more-or-less adjusts gender and education level biases, but leaves intra-country regional biases in the sample.]

panel <- readRDS((file.path(data_raw_dir, "climate-panel.rds")))

climate_data <-  panel %>%
  mutate ( year:  lubridate::year(date_of_interview)) %>%
  select ( all_of(c("isocntry", "geo", "w1")), 
           contains("problem")
  )  %>%
  mutate ( 
    # use the post-stratification weights for national samples
    serious_world_problems_first:  w1*serious_world_problems_first , 
    serious_world_problems_climate_change:  w1*serious_world_problems_climate_change) %>%
  group_by (  .data$geo ) %>%
  summarise( serious_world_problems_first:  mean(serious_world_problems_first, na.rm=TRUE),
             serious_world_problems_climate_change:  mean (serious_world_problems_climate_change, na.rm=TRUE),
             mean_w1:  mean(w1)
             ) %>%
  mutate ( 
    # adjust for post-stratification weight bias due to regional over/undersampling
    climate_first:  serious_world_problems_first / mean_w1, 
    climate_mentioned:  serious_world_problems_climate_change / mean_w1
    )

So, we averaged, weighted and adjusted the mentioning of climate change as the world’s most serious, or one of the most serious problems by NUTS regions.

Aggregation level

The problem is that most statistical data is available in for the NUTS regional boundaries according to the NUTS2016 definition. However, GESIS uses NUTS2013 regions, so 252 regional codes in the four survey waves are invalid. Some data is available only on national level, but it can be projected to regional level, because small countries like Luxembourg have no regional divisions. Larger countries like Germany are divided only on state level (NUTS1), while small countries are divided on NUTS3 level.

This leads to various problems. Many data is available only on NUTS2 level, in which case NUTS1 data should be projected to its constituent smaller NUTS2 regions, and NUTS3 level data must be aggregated up to larger, containing NUTS2 levels.

Of course, we also must choose if we use `NUTS2013 or NUTS2016 boundaries. Sub-national boundaries have changed many thousand times in the EU27 countries alone since 1999.

## # A tibble: 5 x 2
##   validate         n
##   <chr>        <int>
## 1 country         15
## 2 invalid        252
## 3 nuts_level_1   132
## 4 nuts_level_2   452
## 5 nuts_level_3   141

Recoding the Regions

Our regions package was designed to keep track of sub-national regional boundary changes. It can validate regional data codes, and to some extent carry out recoding, imputation or simple aggregation.

Recoding means that the boundaries are unchanged, but the country changed the names/codes of regions, because there were other boundary changes which did not affect our observation unit.
Imputation must not be done with usual, general imputation tools, because our data is regionally structured. However, some imputations are very simple, because we can use equality equasions like MT: MT0, MT00.
Often the boundary change is additive, and merged territorial units can simple aggregated for comparison in earlier data.

regional_coding_2016 <- panel %>%
  mutate ( year:  lubridate::year(date_of_interview)) %>%
  select (  all_of(c("isocntry", "geo", "region", "year") ) ) %>%
  distinct_all() %>%
  recode_nuts()

regional_coding_2013 <- panel %>%
  mutate ( year:  lubridate::year(date_of_interview)) %>%
  select (  all_of(c("isocntry", "geo", "region", "year") ) ) %>%
  distinct_all() %>%
  recode_nuts( nuts_year:  2013)

climate_data_recoded <- climate_data %>% 
  left_join ( regional_coding_2016, by:  'geo' ) %>%
  left_join ( regional_coding_2013 %>% 
                select ( all_of(c("geo", "code_2013"))), 
              by:  "geo") %>%
  distinct_all()

saveRDS ( climate_data_recoded , file.path(tempdir(), "climate_panel_recoded_agr.rds"), version:  2)

# not evaluated
saveRDS( climate_data_recoded , file:  file.path("data-raw", "climate_panel_recoded_agr.rds"))

Where Are People More Likely To Treat Climate Change as the Most Serious Global Problem?

Sat, 06 Mar 2021 00:00:00 +0000

library(regions)
library(lubridate)
library(dplyr)

if ( dir.exists('data-raw') ) {
  data_raw_dir <- "data-raw"
} else {
  data_raw_dir <- file.path("..", "..", "data-raw")
  }

The first results of our longitudinal table were difficult to map, because the surveys used an obsolete regional coding. We will adjust the wrong coding, when possible, and join the data with the European Environment Agency’s (EEA) Air Quality e-Reporting (AQ e-Reporting) data on environmental pollution. We recoded the annual level for every available reporting stations [not shown here] and all values are in μg/m3. The period under observation is 2014-2016. Data file: https://www.eea.europa.eu/data-and-maps/data/aqereporting-8 (European Environment Agency 2021).

Recoding the Regions

Recoding means that the boundaries are unchanged, but the country changed the names and codes of regions because there were other boundary changes which did not affect our observation unit. We explain the problem and the solution in greater detail in our tutorial that aggregates the data on regional levels.

panel <- readRDS((file.path(data_raw_dir, "climate-panel.rds")))

climate_data_geocode <-  panel %>%
  mutate ( year:  lubridate::year(date_of_interview)) %>%
  recode_nuts()

Let’s join the air pollution data and join it by corrected geocodes:

load(file.path("data", "air_pollutants.rda")) ## good practice to use system-independent file.path

climate_awareness_air <- climate_data_geocode %>%
  rename ( region_nuts_codes :  .data$code_2016) %>%
  left_join ( air_pollutants, by:  "region_nuts_codes" ) %>%
  select ( -all_of(c("w1", "wex", "date_of_interview", 
                     "typology", "typology_change", "geo", "region"))) %>%
  mutate (
    # remove special labels and create NA_numeric_ 
    age_education:  retroharmonize::as_numeric(age_education)) %>%
  mutate_if ( is.character, as.factor) %>%
  mutate ( 
    # we only have responses from 4 years, and this should be treated as a categorical variable
    year:  as.factor(year) 
    ) %>%
  filter ( complete.cases(.) )

The climate_awareness_air data frame contains the answers of 75086 individual respondents. 17.07% thought that climate change was the most serious world problem and 33.6% mentioned climate change as one of the three most important global problems.

summary ( climate_awareness_air  )

##                  rowid       serious_world_problems_first
##  ZA5877_v2-0-0_1    :    1   Min.   :0.0000              
##  ZA5877_v2-0-0_10   :    1   1st Qu.:0.0000              
##  ZA5877_v2-0-0_100  :    1   Median :0.0000              
##  ZA5877_v2-0-0_1000 :    1   Mean   :0.1707              
##  ZA5877_v2-0-0_10000:    1   3rd Qu.:0.0000              
##  ZA5877_v2-0-0_10001:    1   Max.   :1.0000              
##  (Other)            :75080                               
##  serious_world_problems_climate_change    isocntry    
##  Min.   :0.000                         BE     : 3028  
##  1st Qu.:0.000                         CZ     : 3023  
##  Median :0.000                         NL     : 3019  
##  Mean   :0.336                         SK     : 3000  
##  3rd Qu.:1.000                         SE     : 2980  
##  Max.   :1.000                         DE-W   : 2978  
##                                        (Other):57058  
##                                    marital_status         age_education  
##  (Re-)Married: without children           :13242   18            :15485  
##  (Re-)Married: children this marriage     :12696   19            : 7728  
##  Single: without children                 : 7650   16            : 5840  
##  (Re-)Married: w children of this marriage: 6520   still studying: 5098  
##  (Re-)Married: living without children    : 6225   17            : 5092  
##  Single: living without children          : 4102   15            : 4528  
##  (Other)                                  :24651   (Other)       :31315  
##    age_exact                      occupation_of_respondent
##  Min.   :15.0   Retired, unable to work       :22911      
##  1st Qu.:36.0   Skilled manual worker         : 6774      
##  Median :51.0   Employed position, at desk    : 6716      
##  Mean   :50.1   Employed position, service job: 5624      
##  3rd Qu.:65.0   Middle management, etc.       : 5252      
##  Max.   :99.0   Student                       : 5098      
##                 (Other)                       :22711      
##             occupation_of_respondent_recoded
##  Employed (10-18 in d15a)   :32763          
##  Not working (1-4 in d15a)  :37125          
##  Self-employed (5-9 in d15a): 5198          
##                                             
##                                             
##                                             
##                                             
##                        respondent_occupation_scale_c_14
##  Retired (4 in d15a)                   :22911          
##  Manual workers (15 to 18 in d15a)     :15269          
##  Other white collars (13 or 14 in d15a): 9203          
##  Managers (10 to 12 in d15a)           : 8291          
##  Self-employed (5 to 9 in d15a)        : 5198          
##  Students (2 in d15a)                  : 5098          
##  (Other)                               : 9116          
##                   type_of_community   is_student      no_education     
##  DK                        :   34   Min.   :0.0000   Min.   :0.000000  
##  Large town                :20939   1st Qu.:0.0000   1st Qu.:0.000000  
##  Rural area or village     :24686   Median :0.0000   Median :0.000000  
##  Small or middle sized town: 9850   Mean   :0.0679   Mean   :0.008151  
##  Small/middle town         :19577   3rd Qu.:0.0000   3rd Qu.:0.000000  
##                                     Max.   :1.0000   Max.   :1.000000  
##                                                                        
##    education       year       region_nuts_codes  country_code  
##  Min.   :14.00   2013:25103   LU     : 1432     DE     : 4531  
##  1st Qu.:17.00   2015:    0   MT     : 1398     GB     : 3538  
##  Median :18.00   2017:25053   CY     : 1192     BE     : 3028  
##  Mean   :19.61   2019:24930   SK02   : 1053     CZ     : 3023  
##  3rd Qu.:22.00                EL30   :  974     NL     : 3019  
##  Max.   :30.00                EE     :  973     SK     : 3000  
##                               (Other):68064     (Other):54947  
##      pm2_5             pm10               o3              BaP        
##  Min.   : 2.109   Min.   :  5.883   Min.   : 66.37   Min.   :0.0102  
##  1st Qu.: 9.374   1st Qu.: 28.326   1st Qu.: 90.89   1st Qu.:0.1779  
##  Median :11.866   Median : 33.673   Median :102.81   Median :0.4105  
##  Mean   :12.954   Mean   : 38.637   Mean   :101.49   Mean   :0.8759  
##  3rd Qu.:15.890   3rd Qu.: 49.488   3rd Qu.:110.73   3rd Qu.:1.0692  
##  Max.   :41.293   Max.   :123.239   Max.   :141.04   Max.   :7.8050  
##                                                                      
##       so2              ap_pc1            ap_pc2             ap_pc3       
##  Min.   : 0.0000   Min.   :-4.6669   Min.   :-2.21851   Min.   :-2.1007  
##  1st Qu.: 0.0000   1st Qu.:-0.4624   1st Qu.:-0.49130   1st Qu.:-0.5695  
##  Median : 0.0000   Median : 0.4263   Median : 0.02902   Median :-0.1113  
##  Mean   : 0.1032   Mean   : 0.1031   Mean   : 0.04166   Mean   :-0.1746  
##  3rd Qu.: 0.0000   3rd Qu.: 0.9748   3rd Qu.: 0.57416   3rd Qu.: 0.3309  
##  Max.   :42.5325   Max.   : 2.0344   Max.   : 3.25841   Max.   : 4.1615  
##                                                                          
##      ap_pc4            ap_pc5        
##  Min.   :-1.7387   Min.   :-2.75079  
##  1st Qu.:-0.1669   1st Qu.:-0.18748  
##  Median : 0.0371   Median : 0.01811  
##  Mean   : 0.1154   Mean   : 0.06797  
##  3rd Qu.: 0.3050   3rd Qu.: 0.34937  
##  Max.   : 3.2476   Max.   : 1.42816  
##

Let’s see a simple CART tree! We remove the regional codes, because there are very serious differences among regional climate awareness. These differences, together with education level, and the year we are talking about, are the most important predictors of thinking about climate change as the most important global problem in Europe.

# Classification Tree with rpart
library(rpart)

# grow tree
fit <- rpart(as.factor(serious_world_problems_first) ~ .,
   method="class", data=climate_awareness_air %>%
     select ( - all_of(c("rowid", "region_nuts_codes"))), 
   control:  rpart.control(cp:  0.005))

printcp(fit) # display the results

## 
## Classification tree:
## rpart(formula:  as.factor(serious_world_problems_first) ~ ., 
##     data:  climate_awareness_air %>% select(-all_of(c("rowid", 
##         "region_nuts_codes"))), method:  "class", control:  rpart.control(cp:  0.005))
## 
## Variables actually used in tree construction:
## [1] age_education                         isocntry                             
## [3] serious_world_problems_climate_change year                                 
## 
## Root node error: 12817/75086:  0.1707
## 
## n= 75086 
## 
##          CP nsplit rel error  xerror      xstd
## 1 0.0240566      0   1.00000 1.00000 0.0080438
## 2 0.0082703      3   0.92783 0.92783 0.0078055
## 3 0.0050000      5   0.91129 0.91425 0.0077588

plotcp(fit) # visualize cross-validation results

summary(fit) # detailed summary of splits

## Call:
## rpart(formula:  as.factor(serious_world_problems_first) ~ ., 
##     data:  climate_awareness_air %>% select(-all_of(c("rowid", 
##         "region_nuts_codes"))), method:  "class", control:  rpart.control(cp:  0.005))
##   n= 75086 
## 
##            CP nsplit rel error    xerror        xstd
## 1 0.024056592      0 1.0000000 1.0000000 0.008043837
## 2 0.008270266      3 0.9278302 0.9278302 0.007805478
## 3 0.005000000      5 0.9112897 0.9142545 0.007758824
## 
## Variable importance
## serious_world_problems_climate_change                              isocntry 
##                                    31                                    26 
##                          country_code                                   BaP 
##                                    20                                     8 
##                                 pm2_5                                ap_pc1 
##                                     4                                     3 
##                         age_education                                  pm10 
##                                     2                                     2 
##                             education                                ap_pc2 
##                                     2                                     1 
##                                  year 
##                                     1 
## 
## Node number 1: 75086 observations,    complexity param=0.02405659
##   predicted class=0  expected loss=0.1706976  P(node): 1
##     class counts: 62269 12817
##    probabilities: 0.829 0.171 
##   left son=2 (25229 obs) right son=3 (49857 obs)
##   Primary splits:
##       serious_world_problems_climate_change < 0.5          to the right, improve=2214.2040, (0 missing)
##       isocntry                              splits as  RRLLLRRRLLRLRLLLLLLLLLLRRLLLRLL, improve= 728.0160, (0 missing)
##       country_code                          splits as  RRLLLRRLLRLLLLLLLLLLRRLLLRLL, improve= 673.3656, (0 missing)
##       BaP                                   < 0.4300347    to the right, improve= 310.6229, (0 missing)
##       pm2_5                                 < 13.38264     to the right, improve= 296.4013, (0 missing)
##   Surrogate splits:
##       age_education splits as  ----RRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRRRRRR-RRRRRL-RRR-RRRRRRRRR--RRRLLR--R-R, agree=0.664, adj=0, (0 split)
##       pm10          < 7.491315     to the left,  agree=0.664, adj=0, (0 split)
## 
## Node number 2: 25229 observations
##   predicted class=0  expected loss=0  P(node): 0.3360014
##     class counts: 25229     0
##    probabilities: 1.000 0.000 
## 
## Node number 3: 49857 observations,    complexity param=0.02405659
##   predicted class=0  expected loss=0.2570752  P(node): 0.6639986
##     class counts: 37040 12817
##    probabilities: 0.743 0.257 
##   left son=6 (34631 obs) right son=7 (15226 obs)
##   Primary splits:
##       isocntry     splits as  RRLLLRRRLLRLRLLLLLLLLLLRRLLLRLL, improve=1454.9460, (0 missing)
##       country_code splits as  RRLLLRRLLRLLLLLLLLLLRRLLLRLL, improve=1359.7210, (0 missing)
##       BaP          < 0.4300347    to the right, improve= 629.8844, (0 missing)
##       pm2_5        < 13.38264     to the right, improve= 555.7484, (0 missing)
##       ap_pc1       < -0.005459537 to the left,  improve= 533.3579, (0 missing)
##   Surrogate splits:
##       country_code splits as  RRLLLRRLLRLLLLLLLLLLRRLLLRLL, agree=0.987, adj=0.957, (0 split)
##       BaP          < 0.1749425    to the right, agree=0.775, adj=0.264, (0 split)
##       pm2_5        < 5.206993     to the right, agree=0.737, adj=0.140, (0 split)
##       ap_pc1       < 1.405527     to the left,  agree=0.733, adj=0.126, (0 split)
##       pm10         < 25.31211     to the right, agree=0.718, adj=0.076, (0 split)
## 
## Node number 6: 34631 observations
##   predicted class=0  expected loss=0.1769802  P(node): 0.4612178
##     class counts: 28502  6129
##    probabilities: 0.823 0.177 
## 
## Node number 7: 15226 observations,    complexity param=0.02405659
##   predicted class=0  expected loss=0.4392487  P(node): 0.2027808
##     class counts:  8538  6688
##    probabilities: 0.561 0.439 
##   left son=14 (11607 obs) right son=15 (3619 obs)
##   Primary splits:
##       isocntry      splits as  LL---LLR--L-L----------LL---R--, improve=337.5462, (0 missing)
##       country_code  splits as  LL---LR--L-L--------LL---R--, improve=337.5462, (0 missing)
##       age_education splits as  ----LLLLLL-LLLRRRRRRR-RRRRRRRRRL-RRRRRRLLRR-RRRRLLRLRL-RRLRRR-RRR-LLLLRRR-----LR-----L-R, improve=294.0807, (0 missing)
##       education     < 22.5         to the left,  improve=262.3747, (0 missing)
##       BaP           < 0.053328     to the right, improve=232.7043, (0 missing)
##   Surrogate splits:
##       BaP           < 0.053328     to the right, agree=0.878, adj=0.485, (0 split)
##       pm2_5         < 4.810361     to the right, agree=0.827, adj=0.271, (0 split)
##       ap_pc2        < 0.8746175    to the left,  agree=0.792, adj=0.124, (0 split)
##       so2           < 0.3302972    to the left,  agree=0.781, adj=0.078, (0 split)
##       age_education splits as  ----LLLLLL-LLLLLLLRLR-LRRLRRRRRR-RRRRLLLLLR-LRLRLLRRLL-LLRLLR-LLR-RRLLLLL-----RR-----R-L, agree=0.779, adj=0.071, (0 split)
## 
## Node number 14: 11607 observations,    complexity param=0.008270266
##   predicted class=0  expected loss=0.3804601  P(node): 0.1545827
##     class counts:  7191  4416
##    probabilities: 0.620 0.380 
##   left son=28 (7462 obs) right son=29 (4145 obs)
##   Primary splits:
##       age_education                    splits as  ----LLLLLL-LRRRRRRRRR-RRLRRLRRLL-RRRRLRLLRR-RLRLLLRLRL-RR-RR--RRL-L-LLRRR------------L-R, improve=123.71070, (0 missing)
##       year                             splits as  R-LR, improve=107.79460, (0 missing)
##       education                        < 20.5         to the left,  improve= 90.28724, (0 missing)
##       occupation_of_respondent         splits as  LRRLRRRRRLRLLLRLLL, improve= 84.62865, (0 missing)
##       respondent_occupation_scale_c_14 splits as  LRLLLRRL, improve= 68.88653, (0 missing)
##   Surrogate splits:
##       education                        < 20.5         to the left,  agree=0.950, adj=0.861, (0 split)
##       occupation_of_respondent         splits as  LLLLRLLRRLRLLLRLLL, agree=0.738, adj=0.267, (0 split)
##       respondent_occupation_scale_c_14 splits as  LRLLLLRL, agree=0.733, adj=0.251, (0 split)
##       is_student                       < 0.5          to the left,  agree=0.709, adj=0.186, (0 split)
##       age_exact                        < 23.5         to the right, agree=0.676, adj=0.094, (0 split)
## 
## Node number 15: 3619 observations
##   predicted class=1  expected loss=0.3722023  P(node): 0.04819807
##     class counts:  1347  2272
##    probabilities: 0.372 0.628 
## 
## Node number 28: 7462 observations
##   predicted class=0  expected loss=0.326052  P(node): 0.09937938
##     class counts:  5029  2433
##    probabilities: 0.674 0.326 
## 
## Node number 29: 4145 observations,    complexity param=0.008270266
##   predicted class=0  expected loss=0.4784077  P(node): 0.05520337
##     class counts:  2162  1983
##    probabilities: 0.522 0.478 
##   left son=58 (2573 obs) right son=59 (1572 obs)
##   Primary splits:
##       year                     splits as  L-LR, improve=40.13885, (0 missing)
##       occupation_of_respondent splits as  LRLLRRRRRLRLLLRLLL, improve=18.33254, (0 missing)
##       marital_status           splits as  LRRRLRRRLRRLRLLRRRRRRLRLRLLRR, improve=17.86888, (0 missing)
##       type_of_community        splits as  LRLRL, improve=17.55254, (0 missing)
##       age_education            splits as  ------------LLRRRRRRR-RR-RL-RR---LRRR-R--LR-R-R---R-R--RR-RR--RR------RRR--------------R, improve=14.66121, (0 missing)
##   Surrogate splits:
##       type_of_community splits as  LLLRL, agree=0.777, adj=0.412, (0 split)
##       marital_status    splits as  RRLLLLLRLLLLLLLRRRLLLLLLRLRLL, agree=0.680, adj=0.155, (0 split)
##       isocntry          splits as  LL---LL---L-R----------LL------, agree=0.669, adj=0.127, (0 split)
##       country_code      splits as  LL---L---L-R--------LL------, agree=0.669, adj=0.127, (0 split)
##       o3                < 83.06345     to the right, agree=0.650, adj=0.076, (0 split)
## 
## Node number 58: 2573 observations
##   predicted class=0  expected loss=0.4240187  P(node): 0.03426737
##     class counts:  1482  1091
##    probabilities: 0.576 0.424 
## 
## Node number 59: 1572 observations
##   predicted class=1  expected loss=0.43257  P(node): 0.02093599
##     class counts:   680   892
##    probabilities: 0.433 0.567

# plot tree
plot(fit, uniform=TRUE,
   main="Classification Tree: Climate Change Is The Most Serious Threat")
text(fit, use.n=TRUE, all=TRUE, cex=.8)

## Warning in labels.rpart(x, minlength:  minlength): more than 52 levels in a
## predicting factor, truncated for printout

saveRDS ( climate_awareness_air , file.path(tempdir(), "climate_panel_recoded.rds"), version:  2)

# not evaluated
saveRDS( climate_awareness_air, file:  file.path("data-raw", "climate-panel_recoded.rds"))

Retrospective Survey Harmonization Case Study - Climate Awareness Change in Europe 2013-2019.

Fri, 05 Mar 2021 00:00:00 +0000

Retrospective survey harmonization comes with many challenges, as we have shown in the introduction to this tutorial case study. In this example, we will work with Eurobarometer’s data.

This code tutorial is not outdated, but the retroharmonize R package has a new (development) release with more featues.

Click to expand table of contents of the post

Table of Contents

Please use the development version of retroharmonize:

devtools::install_github("antaldaniel/retroharmonize")

library(retroharmonize)
library(dplyr)       # this is necessary for the example 
library(lubridate)   # easier date conversion

## Warning: package 'lubridate' was built under R version 4.0.4

library(stringr)     # You can also use base R string processing functions

Get the Data

retroharmonize is not associated with Eurobarometer, or its creators, Kantar, or its archivists, GESIS. We assume that you have acquired the necessary files from GESIS after carefully reading their terms and you placed it on a path that you call gesis_dir. The precise documentation of the data we use can be found in this supporting blogpost. To reproduce this blogpost, you will need ZA5877_v2-0-0.sav, ZA6595_v3-0-0.sav, ZA6861_v1-2-0.sav, ZA7488_v1-0-0.sav, ZA7572_v1-0-0.sav in a directory that you will name gesis_dir.

#Not run in the blogpost. In the repo we have a saved version.
climate_change_files <- c("ZA5877_v2-0-0.sav", "ZA6595_v3-0-0.sav",  "ZA6861_v1-2-0.sav", 
                          "ZA7488_v1-0-0.sav", "ZA7572_v1-0-0.sav")

eb_waves <- read_surveys(file.path(gesis_dir, climate_change_files), .f='read_spss')

if (dir.exists("data-raw")) {
  save ( eb_waves,  file:  file.path("data-raw", "eb_climate_change_waves.rda") )
}

if ( file.exists( file.path("data-raw", "eb_climate_change_waves.rda") )) {
  load (file.path( "data-raw", "eb_climate_change_waves.rda" ) )
} else {
  load (file.path("..", "..",  "data-raw", "eb_climate_change_waves.rda") )
}

The eb_waves nested list contains five surveys imported from SPSS to the survey class of retroharmonize. The survey class is a data.frame that retains important metadata for further harmonization.

document_waves (eb_waves)

## # A tibble: 5 x 5
##   id            filename           ncol  nrow object_size
##   <chr>         <chr>             <int> <int>       <dbl>
## 1 ZA5877_v2-0-0 ZA5877_v2-0-0.sav   604 27919   139352456
## 2 ZA6595_v3-0-0 ZA6595_v3-0-0.sav   519 27718   119370440
## 3 ZA6861_v1-2-0 ZA6861_v1-2-0.sav   657 27901   151397528
## 4 ZA7488_v1-0-0 ZA7488_v1-0-0.sav   752 27339   169465928
## 5 ZA7572_v1-0-0 ZA7572_v1-0-0.sav   348 27655    80562432

Beware the object sizes. If you work with many surveys, memory-efficient programming becomes imperative. We will be subsetting whenever possible.

Metadata analysis

As noted before, prepare to work with nested lists. Each imported survey is nested as a data frame in the eb_waves list.

Metadata: Protocol Variables

Eurobarometer calls certain metadata elements, like interviewee cooperation level or the date of a survey interview as protocol variable. Let’s start here. This will be our template to harmonize more and more aspects of the five surveys (which are, in fact, already harmonization of about 30 surveys conducted in a single ‘wave’ in multiple countries.)

# select variables of interest from the metadata
eb_protocol_metadata <- eb_climate_metadata %>%
  filter ( .data$label_orig %in% c("date of interview") |
             .data$var_name_orig: = "rowid")  %>%
  suggest_var_names( survey_program:  "eurobarometer" )

# subset and harmonize these variables in all nested list items of 'waves' of surveys
interview_dates <- harmonize_var_names(eb_waves, 
                                       eb_protocol_metadata )

# apply similar data processing rules to same variables
interview_dates <- lapply (interview_dates, 
                      function (x) x %>% mutate ( date_of_interview:  as_character(.data$date_of_interview) )
                      )

# join the individual survey tables into a single table 
interview_dates <- as_tibble ( Reduce (rbind, interview_dates) )

# Check the variable classes.

vapply(interview_dates, function(x) class(x)[1], character(1))

##             rowid date_of_interview 
##       "character"       "character"

This is our sample workflow for each block of variables.

Get a unique identifier.
Add other variables
Harmonize the variable names
Subset the data leaving out anything that you do not harmonize in this block.
Apply some normalization in a nested list.
When the variables are harmonized to same name, class, merge them into a data.frame-like tibble object.

Now finish the harmonization. Wednesday, 31st October 2018 should become a Date type 2018-10-31.

require(lubridate)
harmonize_date <- function(x) {
  x <- tolower(as.character(x))
  x <- gsub("monday|tuesday|wednesday|thursday|friday|saturday|sunday|\\,|th|nd|rd|st", "", x)
  x <- gsub("decemberber", "december", x) # all those annoying real-life data problems!
  x <- stringr::str_trim (x, "both")
  x <- gsub("^0", "", x )
  x <- gsub("\\s\\s", "\\s", x)
  lubridate::dmy(x) 
}

interview_dates <- interview_dates %>%
  mutate ( date_of_interview:  harmonize_date(.data$date_of_interview) )

vapply(interview_dates, function(x) class(x)[1], character(1))

##             rowid date_of_interview 
##       "character"            "Date"

To avoid duplication of row IDs in surveys that may not be unique in different surveys, we created a simple, sequential ID for each survey, including the ID of the original file.

set.seed(2021)
sample_n(interview_dates, 6)

## # A tibble: 6 x 2
##   rowid               date_of_interview
##   <chr>               <date>           
## 1 ZA7488_v1-0-0_7016  2018-10-28       
## 2 ZA7488_v1-0-0_19187 2018-11-02       
## 3 ZA6861_v1-2-0_1218  2017-03-18       
## 4 ZA6861_v1-2-0_4142  2017-03-21       
## 5 ZA7572_v1-0-0_12363 2019-04-17       
## 6 ZA7572_v1-0-0_8071  2019-04-18

After this type-conversion problem let’s see an issue when an original SPSS variable can have two meaningful R representations.

Metadata: Geographical information

Let’s continue with harmonizing geographical information in the files. In this example, var_name_suggested will contain the harmonized variable name. It is likely that you have to make this call, after carefully reading the original questionnaires and codebooks.

eb_regional_metadata <- eb_climate_metadata %>%
  filter ( grepl( "rowid|isocntry|^nuts$", .data$var_name_orig)) %>%
  suggest_var_names( survey_program:  "eurobarometer" ) %>%
  mutate ( var_name_suggested:  case_when ( 
    var_name_suggested: = "region_nuts_codes"     ~ "geo",
    TRUE ~ var_name_suggested ))

The harmonize_var_names() takes all variables in the subsetted, geographical metadata table, and brings them to the harmonized var_name_suggested name. The function subsets the surveys to avoid the presence of non-harmonized variables. All regional NUTS codes become geo in our case:

geography <- harmonize_var_names(eb_waves, 
                                 eb_regional_metadata)

If you are used to work with single survey files, you are likely to work in a tabular format, which easily converts into a data.frame like object, in our example, to tidyverse’s tibble. However, when working with longitudinal data, it is far simpler to work with nested lists, because the tables usually have different dimensions (neither the rows corresponding to observations or the columns are the same across all survey files.)

In the nested list, each list element is a single, tabular-format survey. (In fact, the survey are in retroharmonize’s survey class, which is a rich tibble that contains the metadata and the processing history of the survey.)

The regional information in the Eurobarometer files is contained in the nuts variable. We want to keep both the original labels and values. The original values are the region’s codes, and the labels are the names. The easiest and fastest solution is the base R lapply loop.

geography <- lapply ( geography, 
                      function (x) x %>% mutate ( region:  as_character(geo), 
                                                  geo   :  as.character(geo) )  
)

Because each table has exactly the same columns, we can simply use rbind() and reduce the list to a modern data.frame, i.e. a tibble.

geography <- as_tibble ( Reduce (rbind, geography) )

Let’s see a dozen cases:

set.seed(2021)
sample_n(geography, 12)

## # A tibble: 12 x 4
##    rowid               isocntry geo   region              
##    <chr>               <chr>    <chr> <chr>               
##  1 ZA7488_v1-0-0_7016  SI       SI012 Podravska           
##  2 ZA7488_v1-0-0_19187 PL       PL63  Pomorskie           
##  3 ZA6861_v1-2-0_1218  DK       DK02  Sjaelland           
##  4 ZA6861_v1-2-0_4142  FI       FI1B  Helsinki-Uusimaa    
##  5 ZA7572_v1-0-0_12363 SE       SE12  Oestra Mellansverige
##  6 ZA7572_v1-0-0_8071  IT       ITH   Nord-Est [IT]       
##  7 ZA6861_v1-2-0_6145  IE       IE021 Dublin              
##  8 ZA6861_v1-2-0_24638 RO       RO31  South [RO]          
##  9 ZA7488_v1-0-0_11315 CY       CY    REPUBLIC OF CYPRUS  
## 10 ZA6595_v3-0-0_27568 HR       HR041 Grad Zagreb         
## 11 ZA7572_v1-0-0_17397 CZ       CZ06  Jihovychod          
## 12 ZA6861_v1-2-0_10993 PT       PT17  Lisboa

The idea is that we do similar variable harmonization block by block, and eventually we will join them together. Next step: socio-demography and weights.

Socio-demography and Weights

There are a few peculiar issues to look out for. This example shows that survey harmonization requires plenty of expert judgment, and you cannot fully automate the process.

The Eurobarometer archives do not use all weight and demographic variable names consistently. For example, the wex variable, which is a projected weight for the country’s 15 years old or older population is sometimes called wex, sometimes wextra. The individual survey’s post-stratification weight is the w1 variable, but this is not necessarily what you need to use.

The suggest_var_names() function has a parameter for survey_program: "eurobaromater" which normalizes a bit the most used variables. For example, all variations of wex, wextra wil be noramlized to wex. You can ignore this parameter and use your own names, too.

eb_demography_metadata  <- eb_climate_metadata %>%
  filter ( grepl( "rowid|isocntry|^d8$|^d7$|^wex|^w1$|d25|^d15a|^d11$", .data$var_name_orig) ) %>%
  suggest_var_names( survey_program:  "eurobarometer")

As you can see, using the original labels would not help, because they also contain various alterations.

eb_demography_metadata %>%
  select ( filename, var_name_orig, label_orig, var_name_suggested ) %>%
  filter (var_name_orig %in% c("wex", "wextra") )

##            filename var_name_orig                                  label_orig
## 1 ZA5877_v2-0-0.sav        wextra      weight extrapolated population 15 plus
## 2 ZA6595_v3-0-0.sav        wextra      weight extrapolated population 15 plus
## 3 ZA6861_v1-2-0.sav           wex weight extrapolated population aged 15 plus
## 4 ZA7488_v1-0-0.sav           wex weight extrapolated population aged 15 plus
## 5 ZA7572_v1-0-0.sav           wex weight extrapolated population aged 15 plus
##   var_name_suggested
## 1                wex
## 2                wex
## 3                wex
## 4                wex
## 5                wex

demography <- harmonize_var_names ( waves:  eb_waves, 
                                    metadata:  eb_demography_metadata )

Socio-demographic variables like level of highest education or occupation are rather country-specific. Eurobarometer uses standardized occupation and marital status scales, and a proxy for education levels, age of leaving full-time education.

This is a particularly tricky variable, because it’s coding in fact contains three different variables - school leaving age, except for students, and except for people who did not finish their compulsory primary school. And while school leaving age was a good proxy since the 1970s, in the age when the EU is promoting life-long-learning becomes less and less useful, as people stop and re-start their education throughout their lives.

example <- demography[[1]] %>%
  mutate ( across ( -any_of(c("rowid", "w1", "wex")), as_character) ) %>%
  mutate ( across (any_of(c("w1", "wex")), as_numeric) )
unique ( example$age_education )

##  [1] "22"                     "25"                     "17"                    
##  [4] "19"                     "12"                     "23"                    
##  [7] "18"                     "20"                     "21"                    
## [10] "14"                     "24"                     "16"                    
## [13] "26"                     "15"                     "Still studying"        
## [16] "DK"                     "31"                     "29"                    
## [19] "27"                     "13"                     "32"                    
## [22] "28"                     "30"                     "53"                    
## [25] "42"                     "62"                     "40"                    
## [28] "No full-time education" "Refusal"                "37"                    
## [31] "39"                     "34"                     "35"                    
## [34] "47"                     "36"                     "45"                    
## [37] "51"                     "33"                     "43"                    
## [40] "38"                     "49"                     "46"                    
## [43] "41"                     "57"                     "7"                     
## [46] "48"                     "44"                     "50"                    
## [49] "56"                     "8"                      "11"                    
## [52] "10"                     "9"                      "75 years"              
## [55] "6"                      "3"                      "54"                    
## [58] "55"                     "60"                     "64"                    
## [61] "2 years"                "58"                     "52"                    
## [64] "72"                     "61"                     "4"                     
## [67] "63"

The seamingly trival age_exact variable has its own issues, too:

unique ( example$age_exact)

##  [1] "54"       "66"       "56"       "53"       "33"       "72"      
##  [7] "83"       "62"       "86"       "77"       "64"       "46"      
## [13] "44"       "59"       "60"       "67"       "63"       "20"      
## [19] "43"       "37"       "78"       "49"       "90"       "45"      
## [25] "28"       "29"       "30"       "39"       "51"       "38"      
## [31] "41"       "71"       "25"       "48"       "79"       "88"      
## [37] "61"       "85"       "70"       "35"       "81"       "52"      
## [43] "57"       "27"       "47"       "15 years" "21"       "42"      
## [49] "32"       "68"       "36"       "34"       "19"       "31"      
## [55] "26"       "23"       "24"       "22"       "16"       "84"      
## [61] "65"       "18"       "55"       "40"       "50"       "73"      
## [67] "69"       "87"       "89"       "74"       "75"       "98 years"
## [73] "76"       "80"       "58"       "82"       "17"       "93"      
## [79] "91"       "92"       "95"       "94"       "97"

Let’s see all the strange labels attached to age-type variables:

collect_val_labels(metadata:  eb_demography_metadata %>%
                     filter ( var_name_suggested %in% c("age_exact", "age_education")) )

##  [1] "2 years"                  "75 years"                
##  [3] "No full-time education"   "Still studying"          
##  [5] "15 years"                 "98 years"                
##  [7] "96 years"                 "[NOT CLEARLY DOCUMENTED]"
##  [9] "74 years"                 "99 and older"            
## [11] "Refusal"                  "87 years"                
## [13] "DK"                       "88 years"

We must handle many exception, so we created a function for this purpose:

remove_years  <- function(x) { 
  x <- gsub("years|and\\solder", "", tolower(x))
  stringr::str_trim (x, "both")}

process_demography <- function (x) { 
  
  x %>% mutate ( across ( -any_of(c("rowid", "w1", "wex")), as_character) ) %>%
    mutate ( across (any_of(c("w1", "wex")), as_numeric) ) %>%
    mutate ( across (contains("age"), remove_years)) %>%
    mutate ( age_exact:  as.numeric (age_exact)) %>%
    mutate ( is_student:  ifelse ( tolower(age_education): = "still studying", 
                                   1, 0), 
             no_education:  ifelse ( tolower(age_education): = "no full-time education", 1, 0)) %>%
    mutate ( education:  case_when (
      grepl("studying", age_education) ~ age_exact, 
      grepl ("education", age_education)  ~ 14, 
      grepl ("refus|document|dk", tolower(age_education)) ~ NA_real_,
      TRUE ~ as.numeric(age_education)
    ))  %>%
    mutate ( education:  case_when ( 
      education < 14 ~ NA_real_, 
      education > 30 ~ 30, 
      TRUE ~ education )) 
}

demography <- lapply ( demography, process_demography )

## Warning in eval_tidy(pair$rhs, env:  default_env): NAs introduced by coercion

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## Warning in eval_tidy(pair$rhs, env:  default_env): NAs introduced by coercion

## Warning in eval_tidy(pair$rhs, env:  default_env): NAs introduced by coercion

## Warning in eval_tidy(pair$rhs, env:  default_env): NAs introduced by coercion

## Warning in eval_tidy(pair$rhs, env:  default_env): NAs introduced by coercion

## WE'll full join and not use rbind, because we have different variables in different waves.
demography <- Reduce ( full_join, demography )

## Joining, by:  c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by:  c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by:  c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")
## Joining, by:  c("rowid", "isocntry", "w1", "wex", "marital_status", "age_education", "age_exact", "occupation_of_respondent", "occupation_of_respondent_recoded", "respondent_occupation_scale_c_14", "type_of_community", "is_student", "no_education", "education")

Now let’s see what we have here:

set.seed(2021)
sample_n(demography, 12)

## # A tibble: 12 x 14
##    rowid    isocntry    w1    wex marital_status        age_education  age_exact
##    <chr>    <chr>    <dbl>  <dbl> <chr>                 <chr>              <dbl>
##  1 ZA7488_~ SI       0.828  1428. (Re-)Married: withou~ 19                    43
##  2 ZA7488_~ PL       1.01  32830. (Re-)Married: withou~ 19                    64
##  3 ZA6861_~ DK       0.641  3100. (Re-)Married: withou~ 22                    78
##  4 ZA6861_~ FI       1.83   8601. (Re-)Married: childr~ 30                    38
##  5 ZA7572_~ SE       0.342  2645. (Re-)Married: withou~ 17                    68
##  6 ZA7572_~ IT       0.630 32287. (Re-)Married: childr~ 20                    40
##  7 ZA6861_~ IE       0.868  3054. (Re-)Married: childr~ 32                    42
##  8 ZA6861_~ RO       0.724 11805. (Re-)Married: withou~ 14                    59
##  9 ZA7488_~ CY       0.691  1013. (Re-)Married: childr~ 18                    67
## 10 ZA6595_~ HR       0.580  2098. Single living w part~ 27                    30
## 11 ZA7572_~ CZ       1.86  16908. Single: without chil~ still studying        20
## 12 ZA6861_~ PT       0.932  7448. Widow: with children  no full-time ~        84
## # ... with 7 more variables: occupation_of_respondent <chr>,
## #   occupation_of_respondent_recoded <chr>,
## #   respondent_occupation_scale_c_14 <chr>, type_of_community <chr>,
## #   is_student <dbl>, no_education <dbl>, education <dbl>

Harmonizing Variable Labels

So far we have been working with metadata, weights and socio-demography. In other words, we have not even started the desired harmonization of climate change awareness. The methodology is the same, but here we really must look out for the answer options in the questionnaire. (Refer to our data summary again here.)

climate_awareness_metadata <- eb_climate_metadata %>%
  suggest_var_names( survey_program:  "eurobarometer" ) %>%
  filter ( .data$var_name_suggested  %in% c("rowid",
                                            "serious_world_problems_first", 
                                             "serious_world_problems_climate_change")
  ) 

hw <- harmonize_var_names ( waves:  eb_waves, 
                            metadata:  climate_awareness_metadata )

The retroharmoinze package comes with a generic harmonize_values() function that will change the value labels of categorical variables (including binary ones) to a unitary format. It will also take care of various types of missing values.

First, let’s go back to our metadata and collect all value labels that will show up with collect_val_labels():

collect_val_labels(climate_awareness_metadata)

##  [1] "Climate change"                            
##  [2] "International terrorism"                   
##  [3] "Poverty, hunger and lack of drinking water"
##  [4] "Spread of infectious diseases"             
##  [5] "The economic situation"                    
##  [6] "Proliferation of nuclear weapons"          
##  [7] "Armed conflicts"                           
##  [8] "The increasing global population"          
##  [9] "Other (SPONTANEOUS)"                       
## [10] "None (SPONTANEOUS)"                        
## [11] "Not mentioned"                             
## [12] "Mentioned"                                 
## [13] "DK"

In this case, we want to select Climate change as the mentioned most serious problem, and Climate change taken from a list of three serious problems. The first question type is a single-choice one, where Climate change is either mentioned, or the alternative answer is labeled as Not mentioned. In the multiple choice case, the alternative may be something else, for example, Spread of infectious diseases, as we all well know by 2021.

We want to see who thought Climate change was the most serious problem, or one of the most serious problems, so we label each mentions of Climate change as mentioned and we pair it with a numeric value of 1. All other cases are labeled as not_mentioned, with the exceptions of various missing observations, which in these cases are Do not know answers, Declined to answer cases, and Inappropriate cases [The latter one is Eurobarometer’s label for questions that were for one reason or other not asked from a particular interviewee – for example, because the Turkish Cypriot community received a different questionnaire.]

# positive cases
label_1:  c("^Climate\\schange", "^Mentioned")
# missing cases 
na_labels <- collect_na_labels( climate_awareness_metadata)
na_labels

## [1] "DK"                             "Inap. (10 or 11 in qa1a)"      
## [3] "Inap. (coded 10 or 11 in qc1a)" "Inap. (coded 10 or 11 in qb1a)"

# negative cases
label_0 <- collect_val_labels( climate_awareness_metadata)
label_0 <- label_0[! label_0 %in% label_1 ]

The harmonize_serious_problems() function harmonizes the labels within the special labeled class of retroharmonize. This class retains all information to give categorical variables a character or numeric representation, and various processing metadata for documentation purposes. While this class is very reach (it contains whatever was imported from SPSS’s proprietary data format and the history), it is not suitable for statistical analysis. We could, of course, directly call the harmonize_values() from the retroharmonize package, but the parameterization would be very complicated even in a simple function call, not to mention a looped call. Because this function is the heart of the retroharmonize package, it has a tutorial article on its own.

harmonize_serious_problems <- function(x) {
  label_list <- list(
    from:  c(label_0, label_1, na_labels), 
    to:  c( rep ( "not_mentioned", length(label_0) ),   # use the same order as in from!
            rep ( "mentioned", length(label_1) ),
            "do_not_know", "inap", "inap", "inap"), 
    numeric_values:  c(rep ( 0, length(label_0) ), # use the same order as in from!
                       rep ( 1, length(label_1) ),
                       99997,99999,99999,99999)
  )
  
  harmonize_values(x, 
                   harmonize_labels:  label_list, 
                   na_values:  c("do_not_know"=99997,
                                 "declined"=99998,
                                 "inap"=99999), 
                   remove:  "\\(|\\)|\\[|\\]|\\%"
  )
}

Our objects are rather big in memory, so first, let’s remove the surveys that do not contain these world problem variables. In this cases, the subsetted and harmonized surveys in the nested list have only one columns, i.e. the rowid.

hw <- hw[unlist ( lapply ( hw, ncol)) > 1 ]

Now we have a smaller problem to deal with. With many surveys, it is easy to fill up your computer’s memory, so let’s start building up our joined panel data from a smaller set of nested, subsetted surveys.

hw <- lapply ( hw, function (x) x %>% mutate ( across ( contains("problem"), harmonize_serious_problems) ) )

Our lapply loop calls an anonymous function which in turn calls the harmonize_serious_problems parameterized version of the harmonize_values() on all variables that have problem in their names.

once we are done, our variables have harmonized names, and harmonized values, and harmonized label, but they are stored in the complex retroharmonize_labelled_spss_survey class, inherited from the haven_labelled_spss in haven.

We reduced our single and multiple choice questions to binary choice variables. We can now give them a numeric representation. Be mindful that retroharmonize has special methods for its special labeled class that retains metadata from SPSS. This means that as_character and as_numeric knows how to handle various types of missing values, whereas the base R as.character and as.numeric may coerce special values to unwanted results. This is particularly dangerous with numeric variables – and this is the reason why we introduced a new set of S3 objects and methods in the package.

We will ignore the differences between various forms of missingness, i.e. the person said that she did not know, or did not want to answer, or for some reason was not asked in the survey. In a more descriptive, non-harmonized analysis you would probably want to explore them as various ‘categories’ and use a character representation.

hw <- lapply ( hw, function(x) x %>% mutate ( across ( contains("problem"), as_numeric) ))

hw <- Reduce ( full_join, hw) # we must use joins instead of binds because the number of columns vary.

Let’s see what we have:

set.seed(2021)
sample_n (hw, 12)

## # A tibble: 12 x 3
##    rowid             serious_world_problems_fi~ serious_world_problems_climate_~
##    <chr>                                  <dbl>                            <dbl>
##  1 ZA6595_v3-0-0_23~                          0                               NA
##  2 ZA7572_v1-0-0_70~                          0                                0
##  3 ZA6595_v3-0-0_18~                          0                               NA
##  4 ZA6861_v1-2-0_27~                          0                                0
##  5 ZA6595_v3-0-0_26~                          0                               NA
##  6 ZA7572_v1-0-0_19~                          0                                1
##  7 ZA5877_v2-0-0_16~                          0                                0
##  8 ZA6861_v1-2-0_12~                          0                                0
##  9 ZA7572_v1-0-0_17~                          0                                0
## 10 ZA5877_v2-0-0_17~                          0                                1
## 11 ZA6861_v1-2-0_41~                          0                                0
## 12 ZA6861_v1-2-0_61~                          0                                1

Creating the Longitudional Table

Now we just need to join the partial table by the rowid together:

#start from the smallest (we removed the survey that had no relevant questionnaire item)
panel <- hw %>%
  left_join ( geography, by:  'rowid' ) 

panel <- panel %>%
  left_join ( demography, by:  c("rowid", "isocntry") ) 

panel <- panel %>%
  left_join ( interview_dates, by:  'rowid' )

And let’s see a small sample:

sample_n(panel, 12)

## # A tibble: 12 x 19
##    rowid  serious_world_pr~ serious_world_pr~ isocntry geo   region    w1    wex
##    <chr>              <dbl>             <dbl> <chr>    <chr> <chr>  <dbl>  <dbl>
##  1 ZA686~                 0                 0 ES       ES41  Casti~ 1.21  46787.
##  2 ZA686~                 0                 0 RO       RO31  South~ 0.724 11805.
##  3 ZA686~                 0                 0 SK       SK02  Zapad~ 0.774  3499.
##  4 ZA757~                 0                 1 PT       PT16  Centr~ 1.11   9336.
##  5 ZA659~                 1                NA HR       HR041 Grad ~ 0.580  2098.
##  6 ZA659~                 1                NA RO       RO21  North~ 1.21  20160.
##  7 ZA686~                 0                 0 PT       PT17  Lisboa 0.932  7448.
##  8 ZA659~                 0                NA GB-GBN   UKI   London 0.994 50133.
##  9 ZA757~                 0                 0 CY       CY    REPUB~ 0.594   874.
## 10 ZA686~                 0                 0 LT       LT003 Klaip~ 0.623  1564.
## 11 ZA757~                 0                 0 IE       IE013 West ~ 0.490  1651.
## 12 ZA659~                 0                NA LT       LT003 Klaip~ 1.16   2917.
## # ... with 11 more variables: marital_status <chr>, age_education <chr>,
## #   age_exact <dbl>, occupation_of_respondent <chr>,
## #   occupation_of_respondent_recoded <chr>,
## #   respondent_occupation_scale_c_14 <chr>, type_of_community <chr>,
## #   is_student <dbl>, no_education <dbl>, education <dbl>,
## #   date_of_interview <date>

saveRDS ( panel, file.path(tempdir(), "climate_panel.rds"), version:  2)

# not evaluated
saveRDS( panel, file:  file.path("data-raw", "climate-panel.rds"), version=2)

Putting It on a Map

This is not the end of the story. If you put all this on a map, the results are a bit disappointing.

Why? Because sub-national (provincial, state, county, district, parish) borders are changing all the time - within the EU and everywhere. The next step is to harmonize the geographical information. We have another CRAN released package to help you with. See the next post: Regional Climate Change Awareness Dataset.

What is Retrospective Survey Harmonization?

Thu, 04 Mar 2021 00:00:00 +0000

Reproducible ex post harmonization of survey microdata

Retrospective survey harmonization allows the comparison of opinion poll data conducted in different countries or time. In this example we are working with data from surveys that were ex ante harmonized to a certain degree – in our tutorials we are choosing questions that were asked in the same way in many natural languages. For example, you can compare what percentage of the European people in various countries, provinces and regions thought climate change was a serious world problem back in 2013, 2015, 2017 and 2019.

We developed the retroharmonize R package to help this process. We have tested the package with about 80 Eurobarometer, 5 Afrobarometer survey files extensively, and a bit with Arabbarometer files. This allows the comparison of various survey answers in about 70 countries. This policy-oriented survey programs were designed to be harmonized to a certain degree, but their ex post harmonization is still necessary, challenging and errorprone. Retrospective harmonization includes harmonization of the different coding used for questions and answer options, post-stratification weights, and using different file formats.

Eurobarometer, Afrobaromer, Arab Barometer and Latinobarómetro make survey files that are harmonized across countries available for research with various terms. Our retroharmonize is not affiliated with them, and to run our examples, you must visit their websites, carefully read their terms, agree to them, and download their data yourself. What we add as a value is that we help to connect their files across time (from different years) or across these programs.

The survey programs mentioned above publish their data in the proprietary SPSS format. This file format can be imported and translated to R objects with the haven package; however, we needed to re-design haven’s labelled_spss class to maintain far more metadata, which, in turn, a modification of the labelled class. The haven package was designed and tested with data stored in individual SPSS files.

The author of labelled, Joseph Larmarange describes two main approaches to work with labelled data, such as SPSS’s method to store categorical data in the Introduction to labelled.

Two main approaches of labelled data conversion.

Our approach is a further extension of Approach B. Survey harmonization in our case always means the joining data from several SPSS files, which requires a consistent coding among several data sources. This means that data cleaning and recoding must take place before conversion to factors, character or numeric vectors. This is particularly important with factor data (and their simple character conversions) and numeric data that occasionally contains labels, for example, to describe the reason why certain data is missing. Our tutorial vignette labelled_spss_survey gives you more information about this.

In the next series of tutorials, we will deal with an array of problems. These are not for the faint heart – you need to have a solid intermediate level of R to follow.

Tidy, joined survey data

The original files identifiers may not be unique, we have to create new, truly unique identifiers. Weighting may not be straightforward.
Neither the number of observations or the number of variables (which represents the survey questions and their translation to coded data) is the same. Certain data may be only present in one survey and not the other. This means that you will likely to run loops on lists and not data.frames, but eventually you must carefully join them.

Class conversion

Similar questions may be imported from a non-native R format, in our case, from an SPSS files, in an inconsistent manner. SPSS’s variable formats cannot be translated unambiguously to R classes. retroharmonize introduced a new S3 class system that handles this problem, but eventually you will have to choose if you want to see a numeric or character coding of each categorical variable.
The harmonized surveys, with harmonized variable names and harmonized value labels, must be brought to consistent R representations (most statistical functions will only work on numeric, factor or character data) and carefully joined into a single data table for analysis.

Harmonization of variables and variable labels

Same variables may come with dissimilar variable names and variable labels. It may be a challenge to match age with age. We need to harmonize the names of variables.
The harmonized variables may have different labeling. One may call refused answers as declined and the other refusal. On a simple choice, climate change may be ‘Climate change’ or Problem: Climate change. Binary choices may have survey-specific coding conventions. Value labels must be harmonized. There are good tools to do this in a single file - but we have to work with several of them.

Missing value harmonization

There are likely to be various types of missing values. Working with missing values is probably where most human judgment is needed. Why are some answers missing: was the question not asked in some questionnaires? Is there a coding error? Did the respondent refuse the question, or sad that she did not have an answer? retroharmonize has a special labeled vector type that retains this information from the raw data, if it is present, but you must make the judgment yourself – in R, eventually you will either create a missing category, or use NA_character_ or NA_real_.

That’s a lot to put on your plate.

It is unlikely that you will be able to work with completely unfamiliar survey programs if you do not have a strong intermediate level of R. Our package comes with tutorials for Eurobarometer, Afrobarometer and our development version already covers Arab Barometer, highlighting some peculiar issues with these survey programs, that we hope to give a head start for less experienced R users.

Open Data Day Interview: Mapping Data with Milos Popovic

Wed, 03 Mar 2021 22:23:00 +0200

Milos Popovic is a researcher, a data scientist, Marie Curie postdoc & Top 10 dataviz & R contributor on Twitter according to NodeXL. He took part in policy debates about terrorism and military intervention and appeared on a number of TV channels including N1 (the CNN affiliate in the Western Balkans), Serbian National Television and Al-Jazeera Balkans. My research interests are at the intersection of civil war dynamics and postwar politics in the Balkans. He is going to join the Data & Lyrics team on International Open Data Day to help us put harmonized environmental degradation perception and environmental sensory data on maps. We asked him four questions about his passion, mapping data. Please join us 6 March 2021 9.30 EST / 15.30 CET for an informal digital coffee.

As a researcher, why are you so much drawn into maps? Is this connected to your interest in territorial conflicts, or you have some other inspiration?

That’s a great question that really makes me pause and look back at the past 5 years. My mapping story started out of curiosity: I found interesting data on the post-WWII violence in Serbia and thought how cool it would be to make a map in R. I quickly made an unimpressive choropleth map and noticed some unexpected patterns. Then I realized just how much unused violence and census data sits out there while we have no clue about geographic patterns. So, it began. I started off with map-making but my curiosity took me to the world of georeferencing and geospatial analysis. In the process, I created over 300 maps hosted on my website as well as dozens of shapefiles from the scratch.

I used to think that my interest is linked to growing up in a war-torn country. But, as my map-making evolved, I discovered that my passion is to use maps as a way to democratize the data: to take the scores of unused, and often buried datasets, place them on the map and share the dataviz with people.

Can you show us an example of the best use of mapped data, and the best map that you have personally created? What is their distinctive value?

I’m immensely proud of my work that required making the shapefiles from the scratch. For instance, my shapefile of over 1500 Kosovo cadastral settlements came into being after I turned dozens of high-resolution raster files into a shapefile fully compatible with Open Street Maps. After months of hard work, I managed to merge the shapefile with the 2011 Kosovo census and present several laser-focused demographic maps to my audience. Same goes for the settlement shapefile of Republika Srpska [the Serb-speaking entity of Bosnia-Herzegovina — the editor], which I made out of a pdf file and merged with the 2013 census data. Whereas most existing maps take a bird’s eye view, my work offers a more fine-grained view of the local dynamics to stakeholders.

Another similar undertaking was my transformation of the pre-WWII German military map of Yugoslavia into a unique shapefile of a few hundred Yugoslav municipalities. I combined this shapefile with the 1931 census data, 80 years after it was first published (better late than never!). It took me almost a year to complete this tremendous project but I enjoyed every bit of it. I have teamed up with my brother who is a web developer and we even made an interactive map of Yugoslavia based on the 1931 census.[The screenshot of this interactive map is the top image in the post – the editor] We hope this project would serve not only scholars but also history enthusiasts to better understand a history of the country that is no more.

Check out Milos’s beautiful static and interactive maps on https://milosp.info/

What do you think about collaboration based on open data and open-source software that processes such data?

It’s a fantastic opportunity for small teams to bypass traditional gatekeepers such as state institutions or big companies and use open source apps for the benefit of their local communities. For example, the access to Open Street Map allows small teams to map pressing communal issues as crime, deceases, or environmental degradation and come up with innovative solutions. In my work, too, I used OSM has helped me create several fine-grained maps that shed more light on local problems in Serbia such as pollution, car accidents or violence.

We are hoping to bring together environmental, sensory data and public attitude data on environmental issues? How can mapping help? What do you expect from this project?

More than ever, we are compelled to figure out how maladies spreads locally. Without mapping the hotspots, our understanding of the consequences of, for example, viral transmission or pollution is shrouded with a lot of uncertainty. We might have no clue how environmental issues shape public attitudes in localities until we use the mapping to turn on the light. Mapping would help this project pin down geographic clusters that require immediate attention from the private and public stakeholders.

Please join us for a digital coffee, tea or beer on International Open Data Day - we will put never seen data on maps, and discuss how to build successful open collaborations, with little, independent contributions to build large data observatories. Make sure you check out Milos’ amazing website, too!

This blogpost was originally posted on our Data & Lyrics blog and its mutation on Medium.

Eurobarometer Surveys Used In Our Project

Wed, 03 Mar 2021 00:00:00 +0000

In our tutorial series, we are going to harmonize the following questionnaire items from five Eurobarometer harmonized survey files. The Eurobarometer survey files are harmonized across countries, but they are only partially harmonized in time.

All data must be downloaded from the GESIS Data Archive in Cologne. We are not affiliated with GESIS and you must read and accept their terms to use the data.

Eurobarometer 80.2 (2013)

GESIS Data Archive, Cologne. ZA5877 Data file Version 2.0.0, https://doi.org/10.4232/1.12792

Data file: ZA6595 data file (European Commission 2017).
Questionnaire: Eurobarometer 83.4 Basic Bilingual Questionnaire
Citation: ZA6595 Bibtex

QA1a Which of the following do you consider to be the single most serious problem facing the world as a whole? (single choice)

QA1b Which others do you consider to be serious problems? (multiple choice)

QA2 And how serious a problem do you think climate change is at this moment? Please use a scale from 1 to 10, with '1' meaning it is "not at all a serious problem (scale 1-10)

QA4 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

QA4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU could benefit the EU economically (agreement-disagreement 4-scale)

QA5 Have you personally taken any action to fight climate change over the past six months? (binary)

Eurobarometer 83.4 (2015)

European Commission, Brussels; Directorate General Communication COMM.A.1 ´Strategy, Corporate Communication Actions and Eurobarometer´GESIS Data Archive, Cologne. ZA6595 Data file Version 3.0.0, https://doi.org/10.4232/1.13146

Data file: ZA6595 data file (European Commission 2018).
Questionnaire: Eurobarometer 83.4 Basic Bilingual Questionnaire
Citation: ZA6595 Bibtex

Eurobarometer 87.1 (2017)

European Commission, Brussels; Directorate General Communication, COMM.A.1 ‘Strategic Communication’; European Parliament, Directorate-General for Communication, Public Opinion Monitoring Unit GESIS Data Archive, Cologne. ZA6861 Data file Version 1.2.0, https://doi.org/10.4232/1.12922

Data file: ZA6861 data file.
Questionnaire: Eurobarometer 90.2 Basic Bilingual Questionnaire
Citation: ZA6861 Bibtex

QC1a Which of the following do you consider to be the single most serious problem facing the world as a whole? (single choice)

QC1b Which others do you consider to be serious problems? (multiple choice)

QC2 And how serious a problem do you think climate change is at this moment? Please use a scale from 1 to 10, with '1' meaning it is "not at all a serious problem (scale 1-10)

Qc4 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can increase the security of EU energy supplies (agreement-disagreement 4-scale)

Qc4 To what extent do you agree or disagree with each of the following statements? - More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced. (agreement-disagreement 4-scale)

Qc5 Have you personally taken any action to fight climate change over the past six months? (binary)

Eurobarometer 90.2 (2018)

European Commission, Brussels; Directorate General Communication, COMM.A.3 ‘Media Monitoring and Eurobarometer’ GESIS Data Archive, Cologne. ZA7488 Data file Version 1.0.0, https://doi.org/10.4232/1.13289

Data file: ZA7488 data file (European Commission 2019a)
Questionnaire: Eurobarometer 90.2 Basic Bilingual Questionnaire
Citation: ZA7488 Bibtex

QB5 To what extent do you agree or disagree with each of the following statements? - Fighting climate change and using energy more efficiently can boost the economy and jobs in the EU (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can increase the security of EU energy supplies (agreement-disagreement 4-scale)

QB5 To what extent do you agree or disagree with each of the following statements? - More public financial support should be given to the transition to clean energies even if it means subsidies to fossil fuels should be reduced. (agreement-disagreement 4-scale)

Eurobarometer 91.3 (2019)

European Commission, Brussels; Directorate General Communication, COMM.A.3 ‘Media Monitoring and Eurobarometer’ GESIS Data Archive, Cologne. ZA7572 Data file Version 1.0.0, https://doi.org/10.4232/1.13372

Data file: ZA7572 data file (European Commission 2019b).
Questionnaire: Eurobarometer 91.3 Basic Bilingual Questionnaire
Citation: ZA7572 Bibtex

QB4 To what extent do you agree or disagree with each of the following statements? - Taking action on climate change will lead to innovation that will make EU companies more competitive (N) (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Promoting EU expertise in new clean technologies to countries outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Reducing fossil fuel imports from outside the EU can benefit the EU economically (agreement-disagreement 4-scale)

QB4 To what extent do you agree or disagree with each of the following statements? - Adapting to the adverse impacts of climate change can have positive outcomes for citizens in the EU (agreement-disagreement 4-scale)

QB5 Have you personally taken any action to fight climate change over the past six months? (binary)

References

European Commission, Brussels. 2017. “Eurobarometer 80.2 (2013).” GESIS Data Archive, Cologne. ZA5877 Data file Version 2.0.0, https://doi.org/10.4232/1.12792. https://doi.org/10.4232/1.12792.

———. 2018. “Eurobarometer 83.4 (2015).” GESIS Data Archive, Cologne. ZA6595 Data file Version 3.0.0, https://doi.org/10.4232/1.13146. https://doi.org/10.4232/1.13146.

———. 2019a. “Eurobarometer 90.2 (2018).” GESIS Data Archive, Cologne. ZA7488 Data file Version 1.0.0, https://doi.org/10.4232/1.13289. https://doi.org/10.4232/1.13289.

———. 2019b. “Eurobarometer 91.3 (2019).” GESIS Data Archive, Cologne. ZA7572 Data file Version 1.0.0, https://doi.org/10.4232/1.13372. https://doi.org/10.4232/1.13372.

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