Abstract:

istosome infection cases and population，the spatial analysis technology，global autocorrelation and hotspot detection methods in ArcGIS 9.2 software were used to render the spatial autocorrelation coefficient graph. The autocorrelation index change trend of schistosome infection rates in different spatial scales was described. In addition，the hotspot area of schistosome infection rates in study area was detected by using the representative distance for pace parameters combined with actual situation. Results The spatial distribution of schistosome infection in study area was characteristic of clusters in 2009. The spatial autocorrelation coefficient graph indicated that the peak of wave happened in the distances of 1 900 m，2 600 m，3 800 m and 5 000 m，respectively. Comparing with the geographical conditions，the hotspots in 5 000 m spatial scale were more realistic than other distances. In this scale，2 hotspots and 2 second hotspots were explored. Conclusion The spatial clustering analysis combined with the spatial autocorrelation analysis can explore the schistosomiasis gathering area more precisely.

Key words: Schistosomiasis；Spatial statistics analysis；GIS；Hotspot；Nanchang County

摘要：

目的探测江西省南昌县血吸虫病疫情聚集区范围。方法收集2009年南昌县血吸虫病流行区血吸虫感染率数据，采用ArcGIS 9.2软件中空间聚集性分析、全局自相关以及空间热点探测方法，绘制空间自相关系数图，描述不同空间距离尺度下的血吸虫感染率自相关指数变化趋势；结合实际情况，以代表性的距离为空间关系参数，对研究区域的血吸虫感染率热点区域进行探测。结果2009年研究区域内的血吸虫感染空间分布具有明显的聚集性；从自相关系数图发现在空间距离尺度为1 900、2 600、3 800 m和5 000 m时出现了波峰；结合研究区域地理环境，发现在空间距离尺度为5 000 m的血吸虫病热点区域更符合实际，存在2个热点区域和2个次热点区域。结论空间聚集性分析结合空间自相关分析能够更加精确地探测血吸虫病疫情聚集区域。

关键词: 血吸虫病；空间统计分析；GIS；热点区域；南昌县