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Volume 41 Issue 2
Feb.  2016
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Article Contents
Article Navigation >  Geomatics and Information Science of Wuhan University  > 2016  >  41(2): 221-227

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

doi: 10.13203/j.whugis20140092
  • 1.

    School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;

  • 2.

    School of Marine Science and Technology, Tianjin 300072, China;

  • 3.

    Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation, Chengdu 610100, China;

  • 4.

    Key Laboratory of Mine Spatial Information Technologies, National Administration of Surveying, Mapping and Geoinformation, Henan Polytechnic University, Jiaozuo 454000, China;

  • 5.

    Chinese Academy of Surveying and Mapping, Beijing 100039, China

Funds:  The National High Technology Reaserch and Development Program (863 Program) of China, No. 2012AA12A404; the National Science-technology Support Plan Project, No. 2012BAJ22B02-01; Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation, No. DM2014SC07;the Open Fund of NASG Key Laboratory, No. KLM201304; the National Natural Science Foundation of China (41531180).
  • Received Date: 2014-11-05
  • Publish Date: 2016-02-05
  • 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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    [15] Wang Jinfeng, Haining R, Cao Z D. Sample Surveying to Estimate the Mean of a Heterogeneous Surface:Reducing the Error Variance Through Zoning[J]. International Journal of Geographical Information Science, 2010, 24(4):523-543
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Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique

doi: 10.13203/j.whugis20140092
  • 1. School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China;
  • 2. School of Marine Science and Technology, Tianjin 300072, China;
  • 3. Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation, Chengdu 610100, China;
  • 4. Key Laboratory of Mine Spatial Information Technologies, National Administration of Surveying, Mapping and Geoinformation, Henan Polytechnic University, Jiaozuo 454000, China;
  • 5. Chinese Academy of Surveying and Mapping, Beijing 100039, China
Funds:  The National High Technology Reaserch and Development Program (863 Program) of China, No. 2012AA12A404; the National Science-technology Support Plan Project, No. 2012BAJ22B02-01; Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation, No. DM2014SC07;the Open Fund of NASG Key Laboratory, No. KLM201304; the National Natural Science Foundation of China (41531180).
  • Received Date: 2014-11-05
  • Publish Date: 2016-02-05

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.

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
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