Geostatistical Analyst 工具
1、使用地统计图层
# Process: GA 图层至格网
arcpy.GALayerToGrid_ga("", 输出表面栅格, "", "1", "1")
# Process: GA 图层至点
arcpy.GALayerToPoints_ga("", "", "", 输出点位置处的统计数据, "ALL")
# Process: GA 图层转等值线
arcpy.GALayerToContour_ga("", "SAME_AS_LAYER", 输出要素类, "", "", "", "")
# Process: 创建地统计图层
arcpy.GACreateGeostatisticalLayer_ga("", "", 输出地统计图层)
# Process: 获取模型参数
arcpy.GAGetModelParameter_ga("", "")
# Process: 计算 Z 值
arcpy.GACalculateZValue_ga("", "")
# Process: 设置模型参数
arcpy.GASetModelParameter_ga("", "", "", 输出模型)
# Process: 面插值图层到面
arcpy.ArealInterpolationLayerToPolygons_ga("", "", 输出面要素类, "ALL")
2、工具
# Process: 交叉验证
arcpy.CrossValidation_ga("", 输出点要素类)
# Process: 半变异函数灵敏度
arcpy.GASemivariogramSensitivity_ga("", "", "", "10", "3", "0", "0", "0", "0", "0", "0", 输出表)
# Process: 子集要素
arcpy.SubsetFeatures_ga("", 输出训练要素类, 输出测试要素类, "50", "PERCENTAGE_OF_INPUT")
# Process: 邻域选择
arcpy.GANeighborhoodSelection_ga("", 输出图层, "", "", "", "", "", "", "ONE_SECTOR")
3、插值分析
# Process: 全局多项式插值法
arcpy.GlobalPolynomialInterpolation_ga("", "", 输出地统计图层, 输出栅格, "", "1", "")
# Process: 反距离权重法
arcpy.IDW_ga("", "", 输出地统计图层__2_, 输出栅格__2_, "", "2", "NBRTYPE=Standard S_MAJOR=1.#QNAN S_MINOR=1.#QNAN ANGLE=0 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR", "")
# Process: 含障碍的扩散插值法
arcpy.DiffusionInterpolationWithBarriers_ga("", "", 输出地统计图层__3_, 输出栅格__3_, "", "", "", "100", "", "", "", "")
# Process: 含障碍的核插值法
arcpy.KernelInterpolationWithBarriers_ga("", "", 输出地统计图层__4_, 输出栅格__4_, "", "", "POLYNOMIAL5", "", "1", "50", "PREDICTION")
# Process: 局部多项式插值法
arcpy.LocalPolynomialInterpolation_ga("", "", 输出地统计图层__5_, 输出栅格__5_, "", "1", "NBRTYPE=Standard S_MAJOR=1.#QNAN S_MINOR=1.#QNAN ANGLE=0 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR", "EXPONENTIAL", "", "NO_USE_CONDITION_NUMBER", "", "", "PREDICTION")
# Process: 径向基函数(RBF)插值法
arcpy.RadialBasisFunctions_ga("", "", 输出地统计图层__6_, 输出栅格__6_, "", "NBRTYPE=Standard S_MAJOR=1.#QNAN S_MINOR=1.#QNAN ANGLE=0 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR", "COMPLETELY_REGULARIZED_SPLINE", "")
# Process: 移动窗口克里金法
arcpy.GAMovingWindowKriging_ga("", "", "", "", 输出要素类, "", 输出表面栅格)
# Process: 经验贝叶斯克里金法
arcpy.EmpiricalBayesianKriging_ga("", "", 输出地统计图层__7_, 输出栅格__7_, "", "NONE", "100", "1", "100", "NBRTYPE=StandardCircular RADIUS=1.#QNAN ANGLE=0 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR", "PREDICTION", "0.5", "EXCEED", "", "POWER")
4、模拟
# Process: 提取值到表
arcpy.ExtractValuesToTable_ga("", "", 输出表, 输出栅格名称表, "ADD_WARNING_FIELD")
# Process: 高斯地统计模拟
arcpy.GaussianGeostatisticalSimulations_ga("", "10", "", "", "", "", "", "", "DO_NOT_SAVE_SIMULATIONS", "", "", "", "", "")
5、采样网络设计
# Process: 创建空间平衡点
arcpy.CreateSpatiallyBalancedPoints_ga("", "", 输出点要素类)
# Process: 增密采样网络
arcpy.DensifySamplingNetwork_ga("", "", 输出点要素类__2_, "STDERR", "", "", "", "")
箴言：因为这些东西是非常简单的。不要抱怨自己学不会，那是因为你没有足够用心。