Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Volume 41 Issue 2
Feb.  2016
Turn off MathJax
Article Contents

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

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

doi: 10.13203/j.whugis20140092
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.
  • [1] Xu Xueqiang, Zhou Yixing, Ning Yuemin. Urban Geography[M]. Beijing:Higher Education Press, 1997(许学强,周一星,宁越敏.城市地理学[M].北京:高等教育出版社,1997)
    [2] Wang Jingfeng. Spatial Analysis[M]. Beijing:Science Press, 2006:76-84(王劲峰. 空间分析[M]. 北京:科学出版社, 2006:76-84)
    [3] Silverman B W. Dehnad K. Density Estimation for Statistics and Data Analysis[M]. London:Chapman Hall, 1986
    [4] Xie Z, Yan J. Kernel Density Estimation of Traffic Accidents in a Network Space[J]. Computers, Environment and Urban Systems, 2008, 32(5):396-406
    [5] Chen Fei, Du Daosheng. Application of the Integration of Spatial Statistical Analysis with GIS to the Analysis of Regional Economy[J]. Geomatics and Information Science of Wuhan University, 2002, 27(4):391-396(陈斐, 杜道生. 空间统计分析与GIS在区域经济分析中的应用[J]. 武汉大学学报\5信息科学版, 2002, 27(4):391-396)
    [6] Anselin L. Local Indicators of Spatial Association-LISA[J]. Geographical Analysis, 1995, 27(2):93-115
    [7] Ord J K, Getis A. Local Spatial Autocorrelation Statistics:Distributional Issues and Application[J].Geographical Analysis,1995, 27(4):286-306
    [8] Borruso G. Network Density Estimation:A GIS Approach for Analysing Point Patterns in a Network Space[J]. Transactions in GIS, 2008, 12(3):377-402
    [9] Tobler W. A Computer Movie Simulating Urban Growth in the Detroit Region[J]. Economic Geography, 1970, 46(2):234-240
    [10] Sheather S J, Jones M C. A Reliable Data-based Bandwidth Selection Method for Kernel Density Estimation[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1991:683-690
    [11] Elgammal A, Duraiswami R, Harwood D, et al. Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance[J]. Proceedings of the IEEE, 2002, 90(7):1151-1163
    [12] Goodchild M, Haining R, Wise S. Integrating GIS and Spatial Data Analysis:Problems and Possibilities[J]. International Journal of Geographical Information Systems, 1992, 6(5):407-423
    [13] Swift A, Liu L, Uber J. Reducing MAUP Bias of Correlation Statistics Between Water Quality and GI Illness[J]. Computers, Environment and Urban Systems, 2008, 32(2):134-148
    [14] Xu Jianhua, Yue Wenze, Tan Wenqi. A Statistical Study on Spatial Scaling Effects of Urban Landscape Pattern:A Case Study of the Central Area of the External Circle Highway in Shanghai[J]. Acta Geographica Sinica,2004, 59(6):1058-1067(徐建华, 岳文泽, 谈文琦. 城市景观格局尺度效应的空间统计规律——以上海中心城区为例[J]. 地理学报, 2004, 59(6):1058-1067)
    [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
    [16] Chen Jiangping, Zhang Yao, Yu Yuanjian. Effect of MAUP in Spatial Autocorrelation[J]. Acta Geographica Sinica, 2011, 66(12):1597-1606(陈江平, 张瑶, 余远剑. 空间自相关的可塑性面积单元问题效应[J]. 地理学报, 2011, 66(12):1597-1606)
    [17] Cao Zhidong, Wang Jinfeng, Gao Yige, et al. Risk Factors and Autocorrelation Characteristics on Severe Acute Respiratory Syndrome in Guangzhou[J]. Acta Geographica Sinica, 2008, 63(9):981-993(曹志冬, 王劲峰, 高一鸽, 等. 广州SARS流行的空间风险因子与空间相关性特征[J]. 地理学报, 2008, 63(9):981-993)
    [18] Chen Peiyang, Zhu Xigang. Regional Inequalities in China at Different Scales[J]. Acta Geographica Sinica, 2012, 67(8):1085-1097(陈培阳, 朱喜钢. 基于不同尺度的中国区域经济差异[J]. 地理学报, 2012, 67(8):1085-1097)
    [19] Jiang Haining, Gu Renxu, Li Guangbing. Headquarter Spatial Pattern and Location Choice of Top 500 Enterprises of Chinese Manufacturing Industries[J]. Economic Geography, 2012, 31(10):1666-1673(姜海宁,谷人旭,李广斌. 中国制造业企业500强总部空间格局及区位选择[J]. 经济地理,2012, 31(10):1666-1673)
    [20] Hai Beibei, Li Xiaojian, Xu Jiawei. Spatio-temporal Evolution of Rural Settlements in Gongyi[J]. Geographical Research, 2013,32(12):2257-2269(海贝贝,李小建,许家伟. 巩义市农村居民点空间格局演变及其影响因素[J]. 地理研究,2013,32(12):2257-2269)
    [21] Ma Xiaodong, Li Quanlin, Shen Yi. Morphological Difference and Regional Types of Rural Settlements in Jiangsu Province[J]. Geographica Sinica, 2012,67(4):516-525(马晓冬,李全林,沈一. 江苏省乡村聚落的形态分异及地域类型[J]. 地理学报,2012,67(4):516-525)
    [22] Steiner R L. Traditional Shopping Centers[J]. Access:Research at the University of California Transportation Center,1998, 12:8-13
    [23] Urban Planning Land and Resources Commission of Shenzhen Municipality. The Comprehensive Plan of Shenzhen City (2010-2020)[OL].http://www.szpl.gov.cn/szupb/,2014-07-18(深圳市城市规划委员会. 深圳市城市总体规划(2010-2020)[OL].http://www.szpl.gov.cn/szupb/,2014-07-18)
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(3520) PDF downloads(1262) Cited by()

Related
Proportional views

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

doi: 10.13203/j.whugis20140092
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).

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
Reference (23)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return