应用地理信息系统分析中国肾综合征出血热的空间分布

目的　利用地理信息系统 (GIS)技术分析中国大陆肾综合征出血热 (HFRS)的空间区域分布 ,建立HFRS危险区域分布图。方法　收集中国 41个HFRS监测点 1 995～ 1 998年的人群发病资料建立数据库 ,以中国省级、县级、一级河流数字地图为背景 ,在ArcGIS软件的支持下 ,与建立的数据库关联并对监测点人群发病资料进行反距离权重插值分析 ;收集 1 998年 6月SPOT4卫星覆盖东南亚地区的植被影像 ,逐一提取出各监测点监测区域的归一化植被指数 (NDVI) ,与 1 995～ 1 998年HFRS监测点人群平均发病率进行相关分析 ;根据不同流行强度监测区域的NDVI值 ,对遥感影像使用不同的颜色进行密度分割。结果　HFRS监测点人群发病资料的空间分析预测模型显示 ,中国HFRS主要分布于黑龙江流域、黄河中下游地区、长江中下游地区及京杭大运河 淮河流域地区 ,与1 995～ 1 998年全国各县HFRS平均发病率分布图进行比较 ,发现两者基本一致 ;相关分析显示HFRS发病率与NDVI之间呈现正相关 (r=0 .41 7,P <0 .0 1 ) ,根据各监测点NDVI的差异建立了中国HFRS危险区域分布图。结论　根据监测点数据采用GIS技术建立的预测模型对预测全国HFRS的分布情况有重要意义

Application of geographic information system analysis the spatial distribution of hemorrhagic fever of renal syndrome in China

Objective To analyze the spatial distribution of hemorrhagic fever with renal syndrome (HFRS) in China by geographic information system, and to draw up a map on HRFS risk areas. Methods A set of database was set up using the information collected and linked to electronic maps of China in a software ArcGIS 8.01 from 41 HFRS surveillance sites during 1995 1998. A HFRS spatial distribution model was developed using inverse distance weighted interpolation of ArcGIS's spatial analysis method. The normalized difference vegetation index (NDVI) in each HFRS surveillance site was extracted from SPOT4 satellite vegetation imagery. Correlation analysis was performed through SPSS 10.0 to analyze the association between NDVI and HFRS incidence, HFRS risk areas were mapped under different colors. Results Spatial distribution model from HFRS surveillance sites showed that HFRS foci mainly presented in the Heilongjiang River drainage,the middle and lower reaches of the Yellow River, the middle and lower reaches of the Yangtze River, and the Jinghang grant Canal Huaihe River drainage. It was consistent with HFRS distribution map derived from national infectious disease reporting system. Correlation analysis indicated that HFRS incidence rates were significantly associated with NDVI ( r = 0.417 , P < 0.01 ). The HFRS risk areas was mapped according to NDVI of each surveillance site. Conclusion It is promising to apply GIS technology in predication of the distribution of HFRS by establishing this prediction model.