A Spatial Characteristics Study Of Unban Chain Stores Based On Spatial Point Pattern Methods

Discovering the hidden information from the spatial data, especially from the disorderly and unsystematic point data, and interpreting its spatial process and pattern is a major challenge and must be addressed problem for scientific workers. Spatial point pattern analysis is used to explore the spatial pattern of point data based on its location information, the theory of which has enjoyed a rapid expansion driven by various applications along with the development of GIS and enrichment of spatial data. Urban chain store data is an important kind of spatial point data, analyzing and exploring its characteristics and patterns of its spatial distribution can provide the basis for business planning and decision making.The spatial point pattern analysis is used to analyze the chain store data in this paper. The Suguo stores and bank ATMs in the main urban area of Nanjing are picked up to do the spatial point pattern analysis in order to prove whether the spatial point analysis is a practical and appropriate method for the chain store data, if so, it is also to be requested to make a scientific explanation. Specifically, this paper has carried out in the following four studies:1. The study of spatial point pattern analysis methods. Taking into account that the chain store is always located along the road network, the spatial point pattern analysis is summarized under the two perspectives:planar and network. The bandwidth determination for kernel density estimation, the correction for edge effect and the Monte Carlo simulation testing are discussed in depth. All the examples for the spatial point pattern method are realized in R language and the SANET toolkit.2. The application of kernel density estimation in exploring the hotspots of urban chain stores. Within the main unban area of Nanjing, the kernel density estimation is used to analyze the hotspot region for Suguo stores and ATMs which are widely distributed in the study area. And the contributing factors are discussed.3. The application of K function analysis in discovering the cluster patterns of urban chain stores. The K and cross-K methods are used to dig out the overall spatial cluster patterns of urban chain stores and the relationship between their and metro stations under planar and network perspectives.4. The application of Voronoi diagram in delimiting the trade area of urban chain stores. With planar and network Voronoi diagram methods delimiting the trade area of the supermarkets in study area, the spatial characteristics of Suguo stores that fall into the trade area is also discussed.The results of spatial point pattern analysis for Suguo stores and bank ATMs indicate that the hotspot regions or roads are mainly concentrated around the commercial centers or their surrounding areas in the central city area of Nanjing and the hotspot regions of the two kinds of chain stores are some overlapping but not totally identical; the K and cross-K function methods show the aggregation property of Suguo and ATMs in space, as well as the dependent relationship between their and metro stations; Suguo stores within the trade area of chain supermarkets divided by Voronoi diagram have a high maturity level associated with Wal-Mart.In summary, this paper performs the spatial point pattern analysis on the chain store data like Suguo and ATM in the main urban area of Nanjing and draws some conclusions about the spatial pattern of chain store, which has certain significance. Meanwhile, there are many shortcomings of this article, the following work can be further expanded to marked point pattern, and spatial-temporal pattern analysis by adding more attribute information to the chain store data, and combining the chain store data with socio-economic data will contribute to a more reasonable explanation for the formation of the spatial point pattern.