Correspondence: m.abbasiharofteh@rug.nl
Summary of the Session’s Theme and Objectives
As innovation increasingly shapes regional development, mapping the geography of innovation (GeoInno) has emerged as a foundational approach in economic geography. The importance of collecting reliable data and the construction of indicators has been increasing steadily in the last decades. In addition, economic geography has embraced a broader definition of innovation, which goes beyond technological innovation to include social and institutional dimensions. This shift also suggests considering the human well-being of innovation processes in places (Binz & Castaldi, 2024).
Quantitative research in innovation geography has, however largely relied on a limited number of secondary data sources, such as patents, scientific publications, and R&D projects. This reliance may bias theory-building on the spatiality of innovation. New data sources—such as unstructured textual data—are becoming increasingly available, and the rise of language models as well as AI-powered tools to analyze such data has gained momentum across a wide range of social sciences (e.g., Dell, 2024). Several GeoInno studies have pioneered the use of alternative data and methods, including firms’ web text, user-generated text, open-source software contributions, job posting calls, and digitized historical archives (Abbasiharofteh, Kinne, & Krüger, 2023; Abbasiharofteh & Kriesch, 2024; Abbasiharofteh, Krüger, Kinne, Lenz, & Resch, 2023; Henning, Eriksson, Garefelt, Martin, & Elekes, 2025; Peris, Meijers, & van Ham, 2021). However, the geography of innovation has yet to fully explore and leverage the potential of novel data sources and AI-powered tools, and existing use cases remain somewhat fragmented.
Thus, this special session aims to bring together GeoInno researchers to share their latest findings and exchange experiences utilizing novel data sources and analysis techniques such as natural language processing and machine learning for mapping GeoInno. We welcome both conceptual and empirical contributions that engage with these emerging opportunities and challenges.
List of Topics to Be Presented in the Special Session
Submissions may address a wide range of related topics, including but not limited to mapping the geography of:
Key References
Abbasiharofteh, M., Kinne, J., & Krüger, M.F.C. (2023). Leveraging the digital layer: The strength of weak and strong ties in bridging geographic and cognitive distances. Journal of Economic Geography, 24(2), 241–262.
Abbasiharofteh, M., & Kriesch, L. (2024). Not all twins are identical: the digital layer of “twin” transition market applications. Papers in Innovation Studies no. 2024/16, Lund University, CIRCLE.
Abbasiharofteh, M., Krüger, M., Kinne, J., Lenz, D., & Resch, B. (2023). The Digital Layer: Alternative Data for Regional and Innovation Studies. Spatial Economic Analysis, 1–23. 10.1080/17421772.2023.2193222
Binz, C., & Castaldi, C. (2024). Toward a normative turn in research on the geography of innovation?: Evolving perspectives on innovation, institutions, and human well-being. Progress in Economic Geography, 2(2), 100018.
Castaldi, C. (2025). Mapping innovation in space. Spatial Economic Analysis. 10.1080/17421772.2025.2481276.
Dell, M. (2024). Deep Learning for Economists. Cambridge, MA: National Bureau of Economic Research.
Henning, M., Eriksson, R., Garefelt, P., Martin, H., & Elekes, Z. (2025). Job relatedness, local skill coherence and economic performance: A job postings approach. Regional Studies, Regional Science, 12(1), 95–122.
Peris, A., Meijers, E., & van Ham, M. (2021). Information diffusion between Dutch cities: Revisiting Zipf and Pred using a computational social science approach. Computers, Environment and Urban Systems, 85(4), 101565.