运用Kriging 法对我国黄淮流域疟疾空间分布特征的研究

目的研究我国黄淮流域疟疾空间分布特征。方法收集安徽、河南及湖北省沿黄淮流域地区2005年有疟疾报告的156个市、县的当年发病率资料，利用Arcgis 9.0软件建立疟疾发病的地理信息系统，并在该软件的统计学扩展模块支持下，利用Kriging法对已建立的疟疾地理信息系统数据库进行空间插值分析，根据无偏最优的原则绘制疟疾发病概率的空间分布图，建立半变异函数，并对预测值的标准误差的分布制图。结果2005年黄淮地区疟疾发病分布呈空间自相关，自相关阈值98 928 m，其变异函数为球形模型，显示疟疾发病分布呈空间聚集性。交叉检验显示Krig-ing法生成的疟疾概率分布图是对黄淮地区疟疾空间分布的最优无偏估计，标准化均值为0.008 621。结论Kriging法能较好估计黄淮地区疟疾的空间分布特征，该地区的疟疾在空间分布上与距离有关，并呈现以安徽省中北部与河南交界及河南省与湖北省交界的两个明显的聚集中心，而且其空间分布并非与行政区划分类完全一致。

Study on the spatial distribution of malaria in Yellow River and Huai River areas based on the "Kriging" method

Objective To explore the spatial distribution of malaria in areas along the Yellow River and Huai River. Methods Data for malaria incidence of 156 counties or cities along the Yellow River and Huai River in 2005 were collected to establish the geographical information system data base by Arcgis 9.0 software. Mapping the malaria probability distribution based on the GIS data base by the spatial local interpolation method in the extension function. The predictive incidence probability map and semi-variance function was produced by unbiased criterion. Cross-validation technique was used to evaluate the fitness of the distribution maps by mapping the error distribution map. Results The distribution of malaria in counties along the Yellow River and Huai River in the year 2005 was auto-correlated in spatial and the range was 98 928 m. The semi-variogram model was spherical. The cross-validation showed that the map could estimate the spatial distribution of malaria correctly and the standardized mean was 0. 008 621. Conclusion Kriging method could predict the spatial distribution of malaria in counties along the Yellow River and Huai River. The variation of malaria incidence in spatial were related with distance apart, which demonstrated two aggregative centers including the boundary of north Anhui Province and Henan Province and the boundary of Henan and Hubei Provinces. The spatial distribution of malaria was not coincident with the administrative map.