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目的 应用地理信息系统 (GIS)对全国疟疾流行区分布态势进行预测。 方法 在ArcView 3 .0a软件及spatialanalyst模块的支持下 ,分别对疟原虫年生长发育累积度日 (TGDD)、降雨、相对湿度进行单因素的表面趋势空间分析 ,并根据Delphi法咨询结果 ,按上述 3种气象因素的 5∶3∶2比例进行空间叠加分析 ,以建立GIS复合模型。 结果 获多层GIS空间复合模型 ,在此基础上获TGDD、降雨、相对湿度的全国疟疾影响因素多层分布图 ,预测了全国疟疾流行地区分布态势。 结论 多因素GIS复合模型预测的全国疟疾流行区域分布与以往的文献报道结果基本相似 ,因此 ,本法可供疟疾传播区进行大范围、多因素预测作参考。目的 应用地理信息系统 (GIS)对全国疟疾流行区分布态势进行预测。 方法 在ArcView 3 .0a软件及spatialanalyst模块的支持下 ,分别对疟原虫年生长发育累积度日 (TGDD)、降雨、相对湿度进行单因素的表面趋势空间分析 ,并根据Delphi法咨询结果 ,按上述 3种气象因素的 5∶3∶2比例进行空间叠加分析 ,以建立GIS复合模型。 结果 获多层GIS空间复合模型 ,在此基础上获TGDD、降雨、相对湿度的全国疟疾影响因素多层分布图 ,预测了全国疟疾流行地区分布态势。 结论 多因素GIS复合模型预测的全国疟疾流行区?
Objective To predict the distribution of malaria endemic areas in China by using geographic information system (GIS). Methods The ArcView 3 .0a software and the spatialanalyst module were used to carry out single factor analysis of surface trend of accumulated growth days (TGDD), rainfall and relative humidity of malaria parasite respectively. Based on the consultation results of Delphi method, 3 kinds of meteorological factors 5: 3: 2 ratio of space overlay analysis to establish GIS composite model. The results obtained by the multi-layer GIS spatial composite model, on this basis, by TGDD, rainfall and relative humidity of the national multi-layer distribution of malaria map, forecast the distribution of malaria endemic areas. Conclusion The distribution of the national malaria epidemic predicted by the multi-factor GIS composite model is basically similar to the results reported in the past. Therefore, this method can be used for reference of large-scale and multi-factor prediction of malaria transmitting area. Objective To predict the distribution of malaria endemic areas in China by using geographic information system (GIS). Methods The ArcView 3 .0a software and the spatialanalyst module were used to carry out single factor analysis of surface trend of accumulated growth days (TGDD), rainfall and relative humidity of malaria parasite respectively. Based on the consultation results of Delphi method, 3 kinds of meteorological factors 5: 3: 2 ratio of space overlay analysis to establish GIS composite model. The results obtained by the multi-layer GIS spatial composite model, on this basis, by TGDD, rainfall and relative humidity of the national multi-layer distribution of malaria map, forecast the distribution of malaria endemic areas. Conclusions Multifactorial GIS composite model predicts the national malaria epidemic area?