刘海霞, 孟蕾, 刘东鹏, 任晓卫, 李娟生, 蒋小娟, 李治平, 刘新凤. 空间数据分析在甘肃省细菌性痢疾空间聚集性研究中的应用[J]. 疾病监测, 2015, 30(5): 415-419. DOI: 10.3784/j.issn.1003-9961.2015.05.019
引用本文: 刘海霞, 孟蕾, 刘东鹏, 任晓卫, 李娟生, 蒋小娟, 李治平, 刘新凤. 空间数据分析在甘肃省细菌性痢疾空间聚集性研究中的应用[J]. 疾病监测, 2015, 30(5): 415-419. DOI: 10.3784/j.issn.1003-9961.2015.05.019
LIU Hai-xia, MENG Lei, LIU Dong-peng, REN Xiao-wei, LI Juan-sheng, JIANG Xiao-juan, LI Zhi-ping, LIU Xin-feng. Application of spatial data analysis on clustering of bacillary dysentery in Gansu[J]. Disease Surveillance, 2015, 30(5): 415-419. DOI: 10.3784/j.issn.1003-9961.2015.05.019
Citation: LIU Hai-xia, MENG Lei, LIU Dong-peng, REN Xiao-wei, LI Juan-sheng, JIANG Xiao-juan, LI Zhi-ping, LIU Xin-feng. Application of spatial data analysis on clustering of bacillary dysentery in Gansu[J]. Disease Surveillance, 2015, 30(5): 415-419. DOI: 10.3784/j.issn.1003-9961.2015.05.019

空间数据分析在甘肃省细菌性痢疾空间聚集性研究中的应用

Application of spatial data analysis on clustering of bacillary dysentery in Gansu

  • 摘要: 目的 应用空间自相关分析和空间扫描统计分析甘肃省2013年细菌性痢疾发病的空间分布特征,探讨空间自相关性和聚集范围. 方法 收集中国疾病控制信息系统中2013年甘肃省87个县(区)细菌性痢疾报告病例资料,采用Geoda 1.60软件进行空间全局和局部自相关分析,SaTScan 9.1.1.0软件进行空间扫描,分析结果使用ArcGIS 10.2软件进行可视化地图展示. 结果 2013年甘肃省细菌性痢疾报告发病数8191例,报告发病率为31.81/10万.总体层面上具有空间自相关性(Moran's I=0.4555,Z=6.51,P=0.001);局部空间自相关分析,甘肃省东部的庆阳市和南部甘南州的9个县(区),呈高值聚集状态,为细菌性痢疾发病的热点区域,中西部的金昌市、张掖市和武威市的11个县(区),呈低值聚集状态,是细菌性痢疾发病冷点区域.空间扫描探测到的主要聚集区为兰州市及周边共5个县(区)(LLR=137.10,RR=2.38);庆阳市的9个县(区)(LLR=428.60,RR=2.40). 结论 2013年甘肃省细菌性痢疾空间分布呈非随机分布,具有空间自相关性,存在明显聚集性.

     

    Abstract: Objective To understand the spatial distribution of bacillary dysentery cases in Gansu in 2013 and its spatial autocorrelation and clustering areas. Methods The incidence data of bacillary dysentery in 87 counties in Gansu in 2013 were collected from National Disease Reporting Information System to analyze the spatial autocorrelation by using Geoda 1.60 and conduct spatial scan statistics by using SaTScan 9.1.1.0. The results were visualized by using ArcGIS 10. 0 software. Results In 2013, 8191 bacillary dysentery cases were reported, the incidence was 31.81/100 000. The bacillary dysentery distribution showed a spatial autocorrelation in all the study areas (Moran's I=0.4555,Z=6.51,P=0.001). By local spatial autocorrelation analysis, the highly autocorrelation of bacillary dysentery cases was observed in Qingyang in eastern Gansu and 9 countries in southern Gansu, the hot spot areas, and the low autocorrelation of bacillary dysentery cases was observed in 11 counties in west-central Gansu, the cold spot areas. The spatial scan detected the major clustering areas in 5 counties near Lanzhou(LLR=137.10,RR =2.38)and in 9 counties in Qingyang(LLR=428.60,RR=2.40). Conclusion The bacillary dysentery cases were not distributed randomly in Gansu in 2013, the spatial autocorrelation and obvious clustering were observed.

     

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