地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (4): 723-737.doi: 10.12082/dqxxkx.2022.210478

• 地理空间分析综合应用 • 上一篇    下一篇


黄钦1,2,*(), 杨波1,2,*(), 徐新创3, 郝汉舟3, 梁莉莉1,2, 王敏1,2   

  1. 1.湖南师范大学地理科学学院,长沙 410081
    2.湖南师范大学地理空间大数据挖掘与应用湖南省重点实验室,长沙 410081
    3.湖北科技学院资源环境科学与工程学院,咸宁 437100
  • 收稿日期:2021-08-16 修回日期:2021-09-17 出版日期:2022-04-25 发布日期:2022-06-25
  • 通讯作者: *杨 波(1974— ),男,湖南张家界人,教授,博士,主要从事资源环境遥感与旅游大数据研究。 E-mail: yb@hunnu.edu.cn
    *杨 波(1974— ),男,湖南张家界人,教授,博士,主要从事资源环境遥感与旅游大数据研究。 E-mail: yb@hunnu.edu.cn
  • 作者简介:黄 钦(1995— ),男,湖北神农架人,硕士生,主要从事空间数据挖掘与机器学习。E-mail: 1182674026@qq.com
  • 基金资助:

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

HUANG Qin1,2,*(), YANG Bo1,2,*(), XU Xinchuang3, HAO Hanzhou3, LIANG Lili1,2, WANG Min1,2   

  1. 1. School of Geographic Sciences, Hunan Normal University, Changsha 410081, China
    2. Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    3. College of Resources and Environmental Science and Engineering, Hubei University of Science and Technology, Xianning 437100, China
  • Received:2021-08-16 Revised:2021-09-17 Online:2022-04-25 Published:2022-06-25
  • Supported by:
    National Natural Science Foundation of China(41171342);Key Project of Hunan Provincial Education Department(17A127)


茶颜悦色作为中国本土奶茶品牌,将中国传统茶文化与时尚元素相结合,融入浓郁中国风,成为来长旅游者打卡必喝的一种奶茶饮品。探索其空间分布并对其门店选址适宜性进行评估对于优化门店布局、促进经济发展和提升旅游服务水平等具有重要的实际意义。本文基于高德地图API爬取长沙市茶颜悦色POI,运用平均最近邻指数、地理集中指数、不平衡指数、标准差椭圆、核密度估计等方法分析其空间格局,在此基础上融合多源异构空间数据选取一系列影响其空间分布的指示因子并运用随机森林模型对其门店布局适宜性开展实证研究。分析结果表明:① 长沙市茶颜悦色空间分布整体上为集聚型(ANN=0.354,G=40.283),围绕城市核心商圈集聚分布,形成了“一超多核”的空间格局;② 随机森林模型优化后的平均测试精度为92.18%,OOB测试精度为93.45%,其评价结果能够准确反映长沙市茶颜悦色门店选址适宜性与空间分布的异质性;③ 茶颜悦色选址适宜性结果表明,长沙市核心商圈内适宜性概率整体较高,存在明显的高值集聚现象,符合弗里德曼“中心-外围”理论。若将各商圈抽象为不同等级的中心地,其所提供的服务职能和影响范围受到空间距离衰减作用的影响,在空间分布上符合地理学第一定律;④ 特征重要性排序结果显示竞争环境、交通区位和社会经济发展因素对模型的贡献率较大,这与最小差异化准则强调集聚效应和传统商业选址强调区位选择相得益彰,因此在进行门店选址时可以重点考虑此类因素。本研究融合多源空间数据运用数据挖掘技术解决选址问题的方法和结论可以为茶颜悦色门店选址和空间布局提供参考和借鉴。

关键词: 机器学习, 门店选址, 多源异构空间数据, 随机森林, 分类预测, 长沙市, 茶颜悦色, 空间格局


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.

Key words: machine learning, store location, multi-source heterogeneous spatial data, random forest, classification prediction, Changsha City, SexyTea, spatial pattern