摘要
目的探讨谷歌地图在传染病空间聚集性分析中的应用,探索以社区(行政村)为单位进行空间聚集性分析。方法利用谷歌地图的地理译码技术和计算几何算法以及文本解析方法,将宁波市镇海区2005-2010年结核病的报告数据和模拟数据,转换成以社区(行政村)为单位的发病统计数据,以此为基础利用SaTScan进行扫描统计分析,比较以街道为单位和以社区(行政村)为单位进行空间聚集性分析的结果。结果无论是以街道为单位还是以行政村为单位的聚集性分析,均发现镇海区蛟川街道2005-2010年具有明显的结核病空间聚集性现象,而以行政村为单位的聚集性分析更精确地给出主要聚集区位于中官路村、五里牌村和俞范社区等行政村。对镇海区招宝山街道模拟数据的分析表明,以社区为单位进行聚集性分析能够在早期发现街道内部小范围的空间聚集性,这与以街道为单位进行聚集性分析着重发现较大范围的空间聚集性形成互补。结论谷歌地图及其地理译码技术在传染病空间聚集性分析中具有广阔的应用前景,以社区(行政村)为单位进行空间聚集性分析具有可行性和现实意义。
Objective To explore the method for communities （administrative villages）based spatial clustering with Google Maps. Methods The geocoding technology of Google Maps, the algorithm for computation geometry and text parsing were used for data transforming,which converted the reported tuberculosis data during 2005 -2010 for Zhenhai district,Ningbo municipality and simulated data into the communities and administrative villages based statistics. Then scan statistics implemen- ted by SaTScan was respectively performed for the neighborhoods based and the communities （administrative villages ）based spa- tial clustering analysis. Results Both the neighborhoods based and the adnfinistrative villages based spatial clustering analyses detected the tuberculosis spatial clustering in Jiaochuan neighborhood,Zhenhai district during 2005 -2010, while the administra- tive villages based analysis pointed out that the main clustering regions were lying in Zhongguanlu village, Wulipai village and Yufan community. Furthermore, the results for the analysis of simulated data showed that the communities based analysis could find the local clustering regions in Zhabaoshan neighborhood. It was mutually complementary with the neighborhood based analysis that tended to find the large clustering regions. Conclusion Google Maps and its geocoding technology were applicable to the spatial clustering analysis for infectious diseases and it was meaningful to detect clustering regions with communities （administrative villages）based spatial clustering methods.
出处
《中国卫生统计》
CSCD
北大核心
2014年第3期414-417,共4页
Chinese Journal of Health Statistics
基金
国家自然科学基金项目(31000594)
浙江省教育厅基金(Y200906182)
宁波大学学科项目(XKL11D2123)
