中国寄生虫学与寄生虫病杂志 ›› 2004, Vol. 22 ›› Issue (3): 12-172.

• 论著 • 上一篇    下一篇

普通克立格法预测江宁县江滩钉螺分布

张治英1,徐德忠1,彭华2,周云3,张波1,刘士军4,周晓农5,龚自立4
  

  1. 1 第四军医大学流行病学教研室,西安 710032 2 西安市碑林区卫生防疫站,西安 710001 3 江苏省江宁县疾病预防控制中心,江宁 211100 4 南京军区联勤部防疫队,南京 210014 5 中国疾病预防控制中心寄生虫病预防控制所,上海200025
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2004-06-30 发布日期:2004-06-30

Prediction of the Spatial Distribution of Oncomelania Snails in Marshland of Jiangning County using the Ordinary Kriging

ZHANG Zhi-ying,XU De-zhong,PENG Hua,ZHOU Yun,ZHANG Bo,LIU Shi-jun,ZHOU Xiao-nong,GONG Zi-li
  

  1. Department of Epidemiology,the Fourth Military Medical University,Xi’an 710032,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2004-06-30 Published:2004-06-30

摘要:   目的 探讨江宁县江滩钉螺孳生分布的预测方法。 方法 以变异函数分析江宁县江滩钉螺分布的空间自相关性,并以此为基础用普通克立格法 (OrdinaryKriging)预测江宁县江滩钉螺的分布。 结果  2000年江宁县江滩钉螺分布呈空间自相关性,其变异函数为球型模型,且当距离小于0.0301时,钉螺空间分布变异与距离有关。进一步以此为基础用普通克立格法建立了江宁县江滩钉螺分布预测图,交叉核验显示预测图是对江滩钉螺分布最优无偏估计,预测模型的决定系数R2=0.973。 结论 普通克立格法能有效利用监测资料预测江宁县江滩钉螺的分布状况。

关键词: 钉螺, 普通克立格, 预测

Abstract:  Objective To explore the methods to predict the distribution of Oncomelania hupensis in the marshland of Jiangning County. Methods Semi-variogram was used to analyze the spatial autocorrelation of snails’distribution in the marshland of Jiangning using the Arcview8.1. A prediction map for the snails’distribution was established using the Ordinary Kriging and evaluated using the cross-validation. Results Analysis showed that the distribution of alive snails in the marshland of Jiangning in the year 2000 was auto-correlated in spatial. The semi-variogram model which was spherical demonstrated that the variation of alive snails in spatial were related with distance apart when the distance was less than 0.0301. The prediction map of the snail distribution in the marshland of Jiangning was established based on the semi-variogram using the Ordinary Kriging. The cross-validation showed that the prediction map could estimate the distribution of snails in the marshland of Jiangning correctly. And the determinant coefficient for the prediction model was 0.973. Conclusion It is feasible to predict the snail distribution in the marshland of Jiangning County by using Ordinary Kringing and data from the surveillance spot.

Key words: Oncomelania hupensis, Ordinary Kriging, Prediction