XVI.3 An Alternative Data-Driven Country Map
One way to understand a country is to compare it to others. This macroscope lets you pick metrics for comparison. How much education do citizens have? How much does the government spend on health care? How effective is the judicial system? Using this award-winning macroscope by Nikita Rokotyan, Olya Stukova, and Dasha Kolmakova of Interacta studio, you can raise or lower indicators in a country’s profile to see how that metric shapes quality of life for citizens in that country.
This macroscope uses a machine learning algorithm called t-SNE, a technique for simplifying data with multiple dimensions while retaining meaningful properties of the data. The algorithm groups countries based on their similarity in a high-dimensional space and then projects the solution into a lower-dimensional space to make it easier to see. Because the algorithm can analyze really complex data, it might find similarities between countries that people wouldn’t otherwise notice or investigate.
An Alternative Data-Driven Country Map can be found at https://projects.interacta.io/country-tsne/.
Rokotyan, Nikita, Olya Stukova, Dasha Kolmakova. 2020. An Alternative Data-Driven Country Map. In “16th Iteration (2020): Macroscopes for Harnessing the Power of Data.” Places & Spaces: Mapping Science, edited by Katy Börner, Lisel Record, and Todd Theriault. http://scimaps.org.
- What is a Science Map?
- What is a Macroscope?
- Annual Report 2016
- Annual Report 2015
- Annual Report 2014
- Annual Report 2013
- Annual Report 2012
Acknowledgements: This exhibit is supported by the National Science Foundation under Grant No. IIS-0238261, CHE-0524661, IIS-0534909 and IIS-0715303, the James S. McDonnell Foundation; Thomson Reuters; the Cyberinfrastructure for Network Science Center, University Information Technology Services, and the School of Library and Information Science, all three at Indiana University. Some of the data used to generate the science maps is from the Web of Science by Thomson Reuters and Scopus by Elsevier. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.