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山东大学学报(医学版) ›› 2012, Vol. 50 ›› Issue (5): 129-132.

• 论文 • 上一篇    

空间自相关分析在探究疾病分布热点区域中的应用

张冰冰1,姜祥坤2,张世英2,薛付忠1   

  1. 1. 山东大学公共卫生学院流行病与卫生统计学研究所, 济南 250012;
    2. 山东省聊城市疾病预防控制中心疾病控制所, 山东 聊城 252000
  • 收稿日期:2011-11-08 出版日期:2012-05-10 发布日期:2012-05-10
  • 通讯作者: 薛付忠(1964- ),男,教授,博士生导师,主要从事空间流行病学与空间遗传学理论方法及其应用研究。E-mail:xuefzh@sdu.edu.cn
  • 作者简介:张冰冰(1987- ),男,硕士研究生,主要从事空间流行病学与空间遗传学理论方法及其应用研究。

Application of spatial autocorrelation analysis to
explore disease‘hot spots’regions

ZHANG Bing-bing1, JIANG Xiang-kun2, ZHANG Shi-ying2, XUE Fu-zhong1   

  1. 1. Institute of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China;
    2. Department of Disease Control, Liaocheng Center for Disease Control and Prevention, Liaocheng 252000, Shandong, China
  • Received:2011-11-08 Online:2012-05-10 Published:2012-05-10

摘要:

目的   运用空间自相关分析探究疾病空间分布的热点区域,为疾病的防治提供科学依据。方法   将ArcGIS 93软件作为数据管理和分析平台,利用软件空间统计模块进行全局空间自相关分析和热点分析,综合反映疾病空间分布特征。结果   2010年聊城市手足口病发病率存在热点区域。全局空间自相关Moran′s I=0-69,u=11-46,P<0-01;广义G统计量G=0-01,u(G)=9.51,P<0-01。利用局部G统计量得出疾病分布聚集性,热点区域主要集中在东昌府区和高唐县。结论   在空间统计中,利用空间自相关分析可以有效探究研究区域内的热点区域。

关键词: 空间自相关分析;Moran′s I;广义G统计量;局部G统计量;热点区域

Abstract:

Objective   To explore the ‘hot spot’ regions of disease spatial distribution based on spatial autocorrelation analysis, so as to provide evidence for disease control and prevention. Methods   ArcGIS9-3 was used for data management and analysis, applying global spatial autocorrelation analysis and ‘hot spot’ analysis to reflect characters of the disease spatial distribution based on spatial statistical section. Results   The incidence of hand-foot-mouth disease(HFMD) in 2010 was clustered in Liaocheng City. The global spatial autocorrelation Moran′s I index=069, u=11.46, and P<0-01. Under the generalized G statistics, the G index=0-01, u(G) score=9.51, and P<0-01. Local G statistics revealed high clustering of disease distribution in  the Dongchangfu district and Gaotang county. Conclusion   In spatial statistics, spatial autocorrelation analysis could effectively detect the ‘hot spot’ regions.

Key words: Spatial autocorrelation analysis; Moran′s I; Generalized G statistics; Local G statistics; ‘Hot spots’ region

中图分类号: 

  • R181-2
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