SS14: Data and network science in economic geography

SPECIAL SESSIONS

Name and affiliations of the session organisers

Neave O’Clery (University College London)
Balázs Lengyel (Eötvös Loránd Research Network)
Sándor Juhász (Complexity Science Hub)
César Hidalgo (University of Toulouse)

Description

In recent years exciting advancements have been made at the intersection of data science and economic geography. Data-driven ideas and methods borrowed from computer science, mathematics, physics, and computational social science have been harnessed to explore socio-economic phenomena and provide new fertilizing potential across disciplines. Examples are wide ranging. The application of text analysis to mine datasets such as patents, research abstracts or social media, and the deployment of network analysis tools to uncover the structure of social interactions, mobility or economic transactions belong to this quickly evolving interdisciplinary area. Given the ever-increasing range of new types of data exploited to better understand economic and technological progress, and the growing interest in the field, the time is ripe to foster further innovation and exploration at this interface.

This special session aims to highlight quantitative techniques, methodological developments and applications across a range of domains including economic complexity, evolutionary economic geography and innovation studies, urban mobility and planning, and processes on spatial social- and economic networks. In particular, we wish to feature new methodological advancements specifically developed or tailored for applications in these areas, and novel uses of existing algorithms and tools derived from machine learning and network science.

We welcome submissions on a wide range of topics within these areas. Examples include:

Innovative data collections to test theories of economic geography
Statistical models from network science applied to questions in economic geography
Machine learning techniques to investigate social and economic networks
Causal analysis of network and other models for economic growth
Statistical or hierarchical community detection on networks (e.g. industry or occupation networks)
Network and machine learning models for industry diversification processes
Network resilience models for understanding crises in regions
Agent-based models on transaction and social networks for knowledge and innovation diffusion
Classification of visitation and mobility patterns in cities for urban segregation analyses
Prediction of economic and technological progress using ML and GIS techniques
Analysis of production, supply chain, and trade networks

Click here to go back to the Call for Papers and submission link

References

Alshamsi, A., Pinheiro, F. L., & Hidalgo, C. A. (2018). Optimal diversification strategies in the networks of related products and of related research areas. Nature Communications, 9(1), 1-7.

Brummitt, C. D., Gómez-Liévano, A., Hausmann, R., & Bonds, M. H. (2020). Machine-learned patterns suggest that diversification drives economic development. Journal of the Royal Society Interface, 17(162), 20190283.

Lengyel, B., Bokányi, E., Di Clemente, R., Kertész, J., & González, M. C. (2020). The role of geography in the complex diffusion of innovations. Scientific Reports, 10(1), 1-11.

Mealy, P., Farmer, J. D., & Teytelboym, A. (2019). Interpreting economic complexity. Science Advances, 5(1), eaau1705.

Moro, E., Frank, M. R., Pentland, A., Rutherford, A., Cebrian, M., & Rahwan, I. (2021). Universal resilience patterns in labor markets. Nature Communications, 12(1), 1-8.

O’Clery, N., Yıldırım, M. A., & Hausmann, R. (2021). Productive Ecosystems and the arrow of development. Nature Communications, 12(1), 1-14.

Park, J., Wood, I. B., Jing, E., Nematzadeh, A., Ghosh, S., Conover, M. D., & Ahn, Y. Y. (2019). Global labor flow network reveals the hierarchical organization and dynamics of geo-industrial clusters. Nature Communications, 10(1), 1-10.

Straulino, D., Landman, M. & O’Clery, N. (2021). A bi-directional approach to comparing the modular structure of networks. EPJ Data Science, 10(13).

Tóth, G., Wachs, J., Di Clemente, R., Jakobi, Á., Ságvári, B., Kertész, J., & Lengyel, B. (2021). Inequality is rising where social network segregation interacts with urban topology. Nature Communications, 12(1), 1-9.

SPECIAL SESSIONS