Abstract
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.
Highlights
Location is key to the success of the brick-and-mortar retail business
A poor location can be a significant burden that even a good retailer cannot overcome [1]
By focusing on a street as a linear analysis unit, this paper aims to reveal more detailed spatial distribution rules of different types of retail stores and to explore their interrelated characteristics with street closeness centrality
Summary
Location is key to the success of the brick-and-mortar retail business. An excellent location may be enough to make a retailer successful even if its strategy is mediocre. A poor location can be a significant burden that even a good retailer cannot overcome [1]. Finding the optimal location for a retail store and conducting retail location analyses have been longstanding aims of both retail business and urban planning. The spatial configuration of the urban retail industry is of great significance for maximizing the economic benefits and optimizing the transport system [4]. Understanding the spatial distribution of commercial facilities is essential to the future planning of urban commercial retail systems and contribute to the efficient design of urban commercial space. Aiming at the sustainable development of the city, this improved understanding can avoid squandering planning resources that could be better allocated to other needs
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