禹文豪, 艾廷华, 杨敏, 刘纪平. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报 ( 信息科学版), 2016, 41(2): 221-227. DOI: 10.13203/j.whugis20140092
引用本文: 禹文豪, 艾廷华, 杨敏, 刘纪平. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报 ( 信息科学版), 2016, 41(2): 221-227. DOI: 10.13203/j.whugis20140092
YU Wenhao, AI Tinghua, YANG Min, LIU Jiping. Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 221-227. DOI: 10.13203/j.whugis20140092
Citation: YU Wenhao, AI Tinghua, YANG Min, LIU Jiping. Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2): 221-227. DOI: 10.13203/j.whugis20140092

利用核密度与空间自相关进行城市设施兴趣点分布热点探测

Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique

  • 摘要: 城市设施兴趣点(POI)在局部地理空间下往往呈现聚集型分布特征(即热点),表达该特征的核密度法(kernel density estimation)是最常用到的可视化工具。考虑到核密度方法中缺少量化统计分析,提出了一种城市设施POI分布热点探测的新方法。首先基于"距离衰减效应"计算地理单元的属性值;然后采用Getis-Ord Gi*统计指数定量分析设施POI点的局部空间相关性特征。与传统基于样方法的空间自相关相比,核密度法由于顾及了地理学第一定律的区位影响,计算获得的地理单元属性值可保留空间的细节信息,热点的空间自相关分析结果可以反映设施服务影响的连续性特征。通过实际金融设施数据的自相关分析实验,表明该方法能有效提取POI基础设施在城市区域中的分布热点范围。

     

    Abstract: The distribution pattern of urban facility POIs usually forms clusters (i.e. "hot spots") in local geographic space. The kernel density estimation (KDE), which has been usually utilized for expressing these spatial characteristics, is one of the most popular visualization tools. Considering the missing of quantitative statistical inference assessment in KDE, this paper proposes a novel method to detect the hot spots of urban facility POIs. First, this method computes the attribute value of geographic unit with the "distance decay effect", then by adopting the statistical index of Getis-Ord Gi*, we analysis the local spatial cluster characteristics of urban facilities. Comparing this method with the conventional spatial autocorrelation based on the Quadrat clustering, the attribute value of kernel density computing can preserve the local information of data, and the spatial cluster characteristics of urban facilities can reflect the continuity characteristics of urban services, for that the KDE considers the regional impact based on the First Law of Geography. The actual data experiment for analyzing the financial POIs' distribution patterns indicates that this approach is effective to extract the hot spots of urban facility POIs in city areas.

     

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