Next Article in Journal
Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation
Previous Article in Journal
College Students’ Shared Bicycle Use Behavior Based on the NL Model and Factor Analysis
Open AccessArticle

Identifying Spatial Patterns of Retail Stores in Road Network Structure

by 1,2,3,4, 2,4,*, 5, 1,2,4 and 6
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education, Kaifeng 475004, China
College of Environment and Planning, Henan University, Kaifeng 475004, China
Institute of Henan Spatio-temporal Bigdata Industrial Technology, Henan University, Zhengzhou 450046, China
Urban Bigdata Institute, Henan University, Kaifeng 475004, China
Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng 475004, China
Department of Geography, University of Connecticut, Storrs, CT 06269, USA
Author to whom correspondence should be addressed.
Sustainability 2019, 11(17), 4539;
Received: 26 June 2019 / Revised: 9 August 2019 / Accepted: 16 August 2019 / Published: 21 August 2019
(This article belongs to the Special Issue Sustainable Urban Planning Techniques)
Understanding the spatial patterns of retail stores in urban areas contributes to effective urban planning and business administration. A variety of methods have been proposed in the scientific literature to identify the spatial patterns of retail stores. These methods invariably employ arbitrary grid cells or administrative units (e.g., census tracts) as the fundamental analysis units. As most urban retail stores are distributed along street networks, using area-based analysis units is subject to statistical biases and may obfuscate the spatial pattern to some extent. Using the street segment as the analysis unit, this paper derives the spatial patterns of retail stores by crawling points of interest (POI) data in Zhengzhou, a city in central China. Then, the paper performs the network-based kernel density estimation (NKDE) and employs several network metrics, including the global, local, and weighted closeness centrality. Additionally, the paper discusses the correlation between the NKDE value and the closeness centrality across different store types. Further analysis indicates that stores with a high correlation tend to be distributed in city centers and subnetwork centers. The comparison between NKDE and cell-based KDE shows that our proposed method can address potential statistical issues induced by the area-based unit analysis. Our finding can help stakeholders better understand the spatial patterns and trends of small business expansion in urban areas and provide strategies for sustainable planning and development. View Full-Text
Keywords: POI; road network; kernel density estimation; closeness centrality POI; road network; kernel density estimation; closeness centrality
Show Figures

Figure 1

MDPI and ACS Style

Han, Z.; Cui, C.; Miao, C.; Wang, H.; Chen, X. Identifying Spatial Patterns of Retail Stores in Road Network Structure. Sustainability 2019, 11, 4539.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

Back to TopTop