Network-constrained and category-based point pattern analysis for Suguo retail stores in Nanjing, China

Abstract

The distribution of many geographical objects and events is affected by the road network; thus, network-constrained point pattern analysis methods are helpful to understand their space structures and distribution patterns. In this study, network kernel density estimation and network K-function are used to study retail service hot-spot areas and the spatial clustering patterns of a local retail giant (Suguo), respectively, in Nanjing city. Stores and roads are categorized to investigate the influence of weighting different categories of point events and network on the analysis. In addition, the competitive relation between Suguo and foreign-brand retail chains was revealed. The comprehensive analysis results derived from the combination of the first-order and second-order properties can be further used to examine the reasonability of the existing store distribution and optimize the locational choice of new stores.

Keywords:

• point pattern analysis
• network KDE
• network K-function
• retail stores

Additional information

Funding

This project was supported by the National Natural Science Foundation of China [41401450]; the National Natural Science Foundation of China [41571377]; the General Financial Grant from the China Postdoctoral Science Foundation [2015M571731]; the Program for New Century Excellent Talents in University [NCET-13-0280].