Exploring spatial path dependence in industrial space with big data: A case study of Beijing

The notion of path dependence provides a useful perspective to understand the dynamics of industrial space. However, it is much developed on institutional and technological aspects. This paper proposes the idea of spatial path dependence, arguing that previous spatial distribution of economic activities and associated factors in a given industrial space shall affect current and future ones. Availing of big data technology, the spatial distribution is quantified, and spatial path dependence is examined by means of standard deviation ellipse and machine learning method for information service and its sub-sectors in Beijing during the periods of 2008 and 2013. The analysis shows an existence of spatial path dependence for those industries in the two periods. The dominant factors are screened out, which are differ in different sub-sectors and in different periods, but contribute to the same or similar spatial path. The findings call for the attention of the existing situation for industrial spatial planning, and new emerging people-oriented factors in influencing the spatial layout of information services industries.

Automated summary

The notion of path dependence is utilized to understand the dynamics of industrial space, with a focus on spatial path dependence in this paper. Through the use of big data technology, the study quantifies spatial distribution and examines spatial path dependence in information service industries in Beijing during 2008 and 2013. The findings reveal the existence of spatial path dependence in these industries during the two periods, highlighting the importance of considering previous spatial distribution in future spatial planning.