基于GIS的区域气象因子空间变异分析方法+源代码
毕业论文关键词：GIS；气象因子；空间变异；K均值；减聚类
Spatial Variability Analysis of Regional Meteorological Factors Based on GIS
Abstract：The aim of this study is to explore the method of spatial variability analysis of meteorological factors based on the GIS platform in Jiangsu province. Firstly, the meteorological data of 68 meteorological stations in Jiangsu province was obtained from the Chinese meteorological data network, and then the data were collated. Secondly, the spatial variability of the three meteorological factors in Jiangsu Province was analyzed and the spatial distribution map of three meteorological were made based on geo-statistics. Finally, the partition algorithm is used to manage the meteorological stations, and the spatial variability of meteorological factors is compared after the partition. The three meteorological factors show the spatial variability of the north-south latitude or east-west meridian within the Jiangsu Province. The management partition algorithm has a good partition effect, and the three kinds of meteorological elements are consistent in the partition, but have a district difference among the partition. The improved K-means algorithm exhibits the better partitioning effect. It is feasible to use the geo-statistics and management partitioning algorithm to analyze the variability of meteorological elements.
Key words: GIS; meteorological factors; Spatial variability; K-mean; subtraction clustering
目录
1 绪论 1
1.1 研究背景 2
1.2 研究意义 2
1.3 国内外发展及研究现状 2
1.3.1 空间插值方法研究 2
1.3.2 聚类算法研究 2
2 数据与方法 2
2.1 数据来源与处理 2
2.2 研究方法 3
2.2.1 变异分析 3
2.2.2 插值方法介绍 3
3 变异结果与分析 4
3.1 气象因子区域模拟结果统计分析 4
3.2 气象因子变异分析 4
3.2.1 半方差函数模型 5
3.2.2 半变异函数图 5
3.3 插值 6
3.3.1 插值方法精度检验 6
3.3.2 空间插值结果 6
4 管理分区算法研究 7
4.1 K-means 算法 7
4.1.1 算法介绍 7
4.1.2 算法实现 8
4.2 减聚类 8
4.2.1 算法介绍 8
4.2.2 算法实现 10
4.3 改进的K均值算法 10
4.3.1 算法介绍 10
4.3.2 算法实现 11
5 算法结果与分析 11
5.1 一个变量为分区指标 11
5.1.1 K均值算法分区结果分析 11