Location Selection and Prediction of SexyTea Store in Changsha City based on Multisource Spatial Data and Random Forest Model

SexyTea, as a local milk tea brand in China, combines traditional Chinese tea culture with fashion elements and incorporates a strong Chinese style, making it a must-drink milk tea drink for tourists who visit Changsha. Exploring its spatial distribution and evaluating the suitability of its store location is of great practical significance for optimizing store layout, promoting economic development, and improving tourism service level. This article is based on the API of AMAP to crawl the SexyTea POI in Changsha City, and the spatial pattern is analyzed using the average nearest neighbor index, geographic concentration index, unbalanced index, standard deviation ellipse, kernel density estimation, and other methods. We integrate multi-source heterogeneous spatial data to select a series of factors that affect its spatial distribution and use the random forest model to evaluate the suitability of the store layout. The analysis results show that: ① The spatial distribution of SexyTea in Changsha is agglomerated as a whole (ANN=0.558, G=40.283), clustered around the city's core business clusters, forming a spatial pattern of "one super-multi-core"; ② The average test accuracy after optimization of the random forest model is 92.18%, and the OOB test accuracy is 93.45%. The evaluation results can accurately reflect the suitability and spatial distribution heterogeneity of the SexyTea store in Changsha City; ③ SexyTea location suitability results show that the suitability probability in the core business clusters of Changsha City is generally high, and there is an obvious high-value agglomeration phenomenon, which is in line with Friedman's "center-periphery" theory. If the business clusters are stratified into centers of different levels, the service functions and scope of influence provided by them will be affected by the attenuation of spatial distance, and the spatial distribution conforms to the Tobler's First Law of Geography; ④ The ranking result of feature importance shows that competitive environment, transportation location, and socio-economic development have the greatest contribution to the model. This is complementary to the minimum difference criterion emphasizing agglomeration effect and traditional commercial location strategy emphasizing location selection. Therefore, such factors can be considered when selecting store locations. The methods and conclusions of this research that integrate multi-source spatial data and use data mining technology to solve the location problem can provide reference for the location and spatial layout of SexyTea stores. ©2022, Science Press. All right reserved.