Spatial machine learning: new opportunities for regional science

This paper is a methodological guide to using machine learning in the spatial context. It provides an overview of the existing spatial toolbox proposed in the literature: unsupervised learning, which deals with clustering of spatial data, and supervised learning, which displaces classical spatial econometrics. It shows the potential of using this developing methodology, as well as its pitfalls. It catalogues and comments on the usage of spatial clustering methods (for locations and values, both separately and jointly) for mapping, bootstrapping, cross-validation, GWR modelling and density indicators. It provides details of spatial machine learning models, which are combined with spatial data integration, modelling, model fine-tuning and predictions to deal with spatial autocorrelation and big data. The paper delineates “already available” and “forthcoming” methods and gives inspiration for transplanting modern quantitative methods from other thematic areas to research in regional science.

Entrepreneurship and geography—some thoughts about a complex relationship

This review article sheds a light on the complex and hitherto under-researched relationship between geography and entrepreneurship. This relationship is considered to be interdependent. Both directions are discussed. The paper also describes the perspectives of both academic disciplines involved in regional entrepreneurship research, namely (geographically sensitive) economics and management studies on the one hand, and economic geography on the other. Based on a comprehensive overview of the theoretical and empirical literature on regional entrepreneurship, several research gaps are identified that could be helpful for designing future research. Some have strong relevance for government policy, which has recently paid much more attention to entrepreneurship than in the past (e.g. related to the entrepreneurial ecosystem approach), but which rather rarely has been considered in academic evaluations so far. This paper ends with a suggestion for an agenda for future regional entrepreneurship research. Digital transformation with its potential for a disruptive transformation of economies and societies will provide an excellent and, of course, a currently not well-understood research field for regional entrepreneurship research.