Exact Statistical Method for Analyzing Co-location on a Street Network and Its Computational Implementation

Authors: Wataru Morioka*, University of Illinois at Urbana-Champaign, Atsuyuki Okabe, Aoyama Gakuin University, Mei-Po Kwan, The Chinese University of Hong Kong, Sara L McLafferty, University of Illinois at Urbana-Champaign
Topics: Geographic Information Science and Systems, Spatial Analysis & Modeling, Applied Geography
Keywords: network spatial analysis, spatial point process, Ripley's K function, GIS, economic geography
Session Type: Virtual Paper
Day: 4/9/2021
Start / End Time: 3:05 PM / 4:20 PM
Room: Virtual 14
Presentation File: No File Uploaded

In many central districts in cities across the world, different types of stores form clusters based on the benefits of spatial agglomeration. To precisely analyze the co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method using a computational method. Third, this method is extended to the analysis of the phenomena of the repulsive-location where stores tend to locate repulsively among different types of stores. Fourth, the paper shows a graph-theoretic representation for revealing the spatial structure of stores in a central district in terms of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure of the central district in terms of co-location and repulsive-location.

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