Correspondence: kkopczewska@wne.uw.edu.pl

Summary of the Session’s Theme and Objectives

The increasing availability of geo-coded data, including point data, on innovation and entrepreneurship activity gives rise to opportunities to advance research at the intersection of geography, economics, and data science, especially machine learning. These developments enable novel methodological approaches, the exploration of new data sources, and the formulation of innovative research questions.
This special session invites contributions that study innovation, entrepreneurship and R&D by applying new methods to analyze spatially referenced, high-resolution data. We particularly welcome studies that explore what the literature on geographies of innovation and enterpreneurship can learn from methods and approaches in data science and machine learning and to what extent this can lead to new insights with relevant policy implications. By fostering discussions on novel quantitative methods, this session aims to enhance our understanding of the spatial dynamics of innovation and entrepreneurship and encourage evidence-based policy recommendations.

List of Topics to Be Presented in the Special Session

• application of analytical tools and methods from data science and machine learning,
• spatial dimensions of innovation and entrepreneurship,
  application of spatial statistics, spatial econometrics, or spatial machine learning techniques,
• modeling of spatio-temporal patterns in innovation and technological entrepreneurship dynamics,
• comparative analyses of innovation processes across developed and developing regions,
• explicit linkages between quantitative findings and research and innovation policy,

Key References

• Andersson, M., Klaesson, J., & Larsson, J. P. (2016). How local are spatial density externalities? Neighbourhood effects in agglomeration economies. Regional studies, 50(6), 1082-1095.
• Kopczewska, K. (2022). Spatial machine learning: new opportunities for regional science. The Annals of Regional Science, 68(3), 713-755.
• Kusetogullari, A., Kusetogullari, H., Yavariabdi, A., Andersson, M., & Eklund, J. (2022, November). Genetic Algorithm-based Variable Selection Approach for High-Growth Firm Prediction. In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (pp. 1-6). IEEE.
• Larsson, J. P., & Öner, Ö. (2014). Location and co-location in retail: a probabilistic approach using geo-coded data for metropolitan retail markets. The Annals of Regional Science, 52, 385-408.