Correspondence: philipp.koch@ecoaustria.ac.at

Summary of the Session’s Theme and Objectives

This session will explore the intersection of big data, machine learning and economic geography. Recent advances propose innovative approaches to apply big data or machine learning methods for regional and industrial planning, augment traditional economic data, or develop novel analytical methods that enhance our understanding of spatial dynamics and regional economics more broadly. The objectives of this special session are to foster interdisciplinary dialogue and discuss how big data and machine learning methods can help us better understand complex macroeconomic phenomena in economic geography. Submissions can include methodological advancements as well as applications of big data or machine learning techniques.

List of Topics to Be Presented in the Special Session

We welcome submissions on a wide range of topics related to the session’s theme. Examples are:

• Machine learning techniques to investigate social and economic networks
• Using new or unconventional data to explore questions in economic geography
• Using machine learning to augment data and test theories of economic geography
• Machine learning models for industrial diversification processes
• Methodological advances and applications of Economic Complexity
• Predicting economic and technological development trajectories of countries, regions or cities using machine learning
• Identifying and interpreting causal relationships in economic geography using machine learning

Key References

Abramitzky, R., Boustan, L. P., & Storeygard, A. (2025). New Data and Insights in Regional and Urban Economics. NBER Working Paper Series, No. 33561. https://www.nber.org/papers/w33561

Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485. https://doi.org/10.1126/science.aal4321

Caldarelli, G., Chiesi, L., Chirici, G., Galmarini, B., Mancuso, S., Moi, J., & De

Domenico, M. (2025). Lessons from complex networks to smart cities. Nature Cities, 2(2), 127–134. https://doi.org/10.1038/s44284-024-00188-5

Chi, G., Fang, H., Chatterjee, S., & Blumenstock, J. E. (2022). Microestimates of wealth for all low- and middle-income countries. Proceedings of the National Academy of Sciences, 119(3), e2113658119. https://doi.org/10.1073/pnas.2113658119

Cruz, J.-L., & Rossi-Hansberg, E. (2024). The Economic Geography of Global Warming. Review of Economic Studies, 91(2), 899–939. https://doi.org/10.1093/restud/rdad042

Hidalgo, C. A. (2021). Economic complexity theory and applications. Nature Reviews Physics, 3, 92–113. https://doi.org/10.1038/s42254-020-00275-1

Koch, P., Stojkoski, V., & Hidalgo, C. A. (2024). Augmenting the availability of historical GDP per capita estimates through machine learning. Proceedings of the National Academy of Sciences, 121(39), e2402060121. https://doi.org/10.1073/pnas.2402060121

Stojkoski, V., Koch, P., Coll, E., & Hidalgo, C. A. (2024). Estimating digital product trade through corporate revenue data. Nature Communications, 15(1), 5262. https://doi.org/10.1038/s41467-024-49141-z

Xu, F., Wang, Q., Moro, E., Chen, L., Salazar Miranda, A., González, M. C., Tizzoni, M.,

Song, C., Ratti, C., Bettencourt, L., Li, Y., & Evans, J. (2025). Using human mobility data to quantify experienced urban inequalities. Nature Human Behaviour. https://doi.org/10.1038/s41562-024-02079-0