1、區域分析
# Process: 以表格顯示分區幾何統計
arcpy.gp.ZonalGeometryAsTable_sa("", "", 輸出表, "")
# Process: 以表格顯示分區統計
arcpy.gp.ZonalStatisticsAsTable_sa("", "", "", 輸出表__2_, "DATA", "ALL")
# Process: 分區幾何統計
arcpy.gp.ZonalGeometry_sa("", "", 輸出柵格, "AREA", "")
# Process: 分區統計
arcpy.gp.ZonalStatistics_sa("", "", "", 輸出柵格__2_, "MEAN", "DATA")
# Process: 區域填充
arcpy.gp.ZonalFill_sa("", "", 輸出柵格__3_)
# Process: 區域直方圖
arcpy.gp.ZonalHistogram_sa("", "", "", 輸出表__3_, 輸出圖表名稱)
# Process: 面積制表
arcpy.gp.TabulateArea_sa("", "", "", "", 輸出表__4_, "")
2、疊加分析
# Process: 加權疊加
arcpy.gp.WeightedOverlay_sa("", 輸出柵格)
# Process: 加權總和
arcpy.gp.WeightedSum_sa("", 輸出柵格__2_)
# Process: 查找區域
arcpy.gp.LocateRegions_sa("", 輸出柵格__3_, "", "SQUARE_MAP_UNITS", "1", "CIRCLE", "0", "50", "HIGHEST_AVERAGE_VALUE", "", "", "", "", "MAP_UNITS", "", "EIGHT", "NO_ISLANDS", "AUTO", "AUTO", "AUTO")
# Process: 模糊疊加
arcpy.gp.FuzzyOverlay_sa("", 輸出柵格__4_, "AND", "0.9")
# Process: 模糊隸屬度
arcpy.gp.FuzzyMembership_sa("", 輸出柵格__5_, "MSSMALL 1 1", "NONE")
3、地下水分析
# Process: 孔隙擴散
arcpy.gp.PorousPuff_sa("", "", "", 輸出柵格, "", "", "", "3", "1", "0")
# Process: 粒子追蹤
arcpy.gp.ParticleTrack_sa("", "", "", 輸出粒子追蹤文件, "", "", 輸出追蹤折線要素)
# Process: 達西流
arcpy.gp.DarcyFlow_sa("", "", "", "", 輸出地下水水量平衡殘差柵格數據, 輸出方向柵格數據, 輸出量級柵格數據)
# Process: 達西速度
arcpy.gp.DarcyVelocity_sa("", "", "", "", 輸出方向柵格數據__2_, 輸出量級柵格數據__2_)
4、地圖代數
# Process: 柵格計算器
arcpy.gp.RasterCalculator_sa("", 輸出柵格)
5、多元分析
# Process: Iso 聚類
arcpy.gp.IsoCluster_sa("", 輸出特征文件, "", "20", "20", "10")
# Process: Iso 聚類非監督分類
arcpy.gp.IsoClusterUnsupervisedClassification_sa("", "", 輸出分類的柵格數據, "20", "10", 輸出特征文件__2_)
# Process: 主成分分析
arcpy.gp.PrincipalComponents_sa("", 輸出多波段柵格, "", 輸出數據文件)
# Process: 創建特征文件
arcpy.gp.CreateSignatures_sa("", "", 輸出特征文件__3_, "COVARIANCE", "")
# Process: 最大似然法分類
arcpy.gp.MLClassify_sa("", "", 輸出分類的柵格數據__2_, "0.0", "EQUAL", "", 輸出置信柵格)
# Process: 樹狀圖
arcpy.gp.Dendrogram_sa("", 輸出樹狀圖文件, "VARIANCE", "78")
# Process: 波段集統計
arcpy.gp.BandCollectionStats_sa("", 輸出統計文件, "BRIEF")
# Process: 類別概率
arcpy.gp.ClassProbability_sa("", "", 輸出多波段柵格__2_, "100", "EQUAL", "")
# Process: 編輯特征文件
arcpy.gp.EditSignatures_sa("", "", "", 輸出特征文件__4_, "10")
6、太陽輻射
# Process: 太陽輻射區域
arcpy.gp.AreaSolarRadiation_sa("", 輸出總輻射柵格, "45", "200", "MultiDays 2021 5 160", "14", "0.5", "NOINTERVAL", "1", "FROM_DEM", "32", "8", "8", "UNIFORM_SKY", "0.3", "0.5", 輸出直接輻射柵格數據, 輸出散射輻射柵格數據, 輸出直接輻射持續時間柵格數據)
# Process: 太陽輻射圖
arcpy.gp.SolarRadiationGraphics_sa("", 輸出視域柵格數據, "", "200", "0", "32", "45", "MultiDays 2021 5 160", "14", "0.5", 輸出太陽圖柵格, "8", "8", 輸出星空圖柵格)
# Process: 太陽輻射點
arcpy.gp.PointsSolarRadiation_sa("", "", 輸出總輻射要素, "0", "45", "200", "MultiDays 2021 5 160", "14", "0.5", "NOINTERVAL", "1", "FROM_DEM", "32", "8", "8", "UNIFORM_SKY", "0.3", "0.5", 輸出直接輻射要素, 輸出散射輻射要素, 輸出直接輻射持續時間要素)
7、密度分析
# Process: 核密度分析
arcpy.gp.KernelDensity_sa("", "", 輸出柵格, "", "", "SQUARE_MAP_UNITS", "DENSITIES", "PLANAR")
# Process: 點密度分析
arcpy.gp.PointDensity_sa("", "", 輸出柵格__2_, "", "Circle 0 MAP", "SQUARE_MAP_UNITS")
# Process: 線密度分析
arcpy.gp.LineDensity_sa("", "", 輸出柵格__3_, "", "", "SQUARE_MAP_UNITS")
8、局部分析
# Process: 像元統計數據
arcpy.gp.CellStatistics_sa("", 輸出柵格, "MEAN", "DATA")
# Process: 合並
arcpy.gp.Combine_sa("", 輸出柵格__2_)
# Process: 大於頻數
arcpy.gp.GreaterThanFrequency_sa("", "", 輸出柵格__3_)
# Process: 小於頻數
arcpy.gp.LessThanFrequency_sa("", "", 輸出柵格__4_)
# Process: 最低位置
arcpy.gp.LowestPosition_sa("", 輸出柵格__5_)
# Process: 最高位置
arcpy.gp.HighestPosition_sa("", 輸出柵格__6_)
# Process: 等於頻數
arcpy.gp.EqualToFrequency_sa("", "", 輸出柵格__7_)
# Process: 等級
arcpy.gp.Rank_sa("", "", 輸出柵格__8_)
# Process: 頻數取值
arcpy.gp.Popularity_sa("", "", 輸出柵格__9_)
9、影像分割和分類
# Process: Mean shift 影像分割
arcpy.gp.SegmentMeanShift_sa("", 輸出柵格數據集, "15.5", "15", "20", "")
# Process: 從種子點生成訓練樣本
arcpy.gp.GenerateTrainingSamplesFromSeedPoints_sa("", "", 輸出訓練樣本要素類, "30", "50")
# Process: 分類柵格
arcpy.gp.ClassifyRaster_sa("", "", 輸出分類的柵格數據, "")
# Process: 創建精度評估點
arcpy.gp.CreateAccuracyAssessmentPoints_sa("", 輸出精度評估點, "CLASSIFIED", "500", "STRATIFIED_RANDOM")
# Process: 導出深度學習的訓練數據
arcpy.gp.ExportTrainingDataForDeepLearning_sa("", 輸出文件夾, "", "TIFF", "256", "256", "128", "128", "ONLY_TILES_WITH_FEATURES", "KITTI_rectangles", "0", "", "0")
# Process: 更新精度評估點
arcpy.gp.UpdateAccuracyAssessmentPoints_sa("", "", 輸出精度評估點__2_, "CLASSIFIED")
# Process: 檢查訓練樣本
arcpy.gp.InspectTrainingSamples_sa("", "", "", 輸出訓練樣本要素類_含分數_, 輸出誤分類柵格, "")
# Process: 移除柵格影像分割塊偽影
arcpy.gp.RemoveRasterSegmentTilingArtifacts_sa("", 輸出柵格數據集__2_, "512", "512")
# Process: 計算分割影像屬性
arcpy.gp.ComputeSegmentAttributes_sa("", 輸出段索引柵格, "", "COLOR;MEAN")
# Process: 計算混淆矩陣
arcpy.gp.ComputeConfusionMatrix_sa("", 輸出混淆矩陣)
# Process: 訓練 ISO 聚類分類器
arcpy.gp.TrainIsoClusterClassifier_sa("", "", 輸出分類程序定義文件, "", "20", "20", "10", "COLOR;MEAN", "5", "0.5")
# Process: 訓練支持向量機分類器
arcpy.gp.TrainSupportVectorMachineClassifier_sa("", "", 輸出分類程序定義文件__2_, "", "500", "COLOR;MEAN")
# Process: 訓練最大似然法分類器
arcpy.gp.TrainMaximumLikelihoodClassifier_sa("", "", 輸出分類程序定義文件__3_, "", "COLOR;MEAN")
# Process: 訓練隨機樹分類器
arcpy.gp.TrainRandomTreesClassifier_sa("", "", 輸出分類器定義文件, "", "50", "30", "1000", "COLOR;MEAN")
10、提取分析
# Process: 值提取至點
arcpy.gp.ExtractValuesToPoints_sa("", "", 輸出點要素, "NONE", "VALUE_ONLY")
# Process: 多值提取至點
arcpy.gp.ExtractMultiValuesToPoints_sa("", "", "NONE")
# Process: 按圓提取
arcpy.gp.ExtractByCircle_sa("", "", "", 輸出柵格, "INSIDE")
# Process: 按多邊形提取
arcpy.gp.ExtractByPolygon_sa("", "", 輸出柵格__2_, "INSIDE")
# Process: 按屬性提取
arcpy.gp.ExtractByAttributes_sa("", "", 輸出柵格__3_)
# Process: 按掩膜提取
arcpy.gp.ExtractByMask_sa("", "", 輸出柵格__4_)
# Process: 按點提取
arcpy.gp.ExtractByPoints_sa("", "", 輸出柵格__5_, "INSIDE")
# Process: 按矩形提取
arcpy.gp.ExtractByRectangle_sa("", "DEFAULT", 輸出柵格__6_, "INSIDE")
# Process: 采樣
arcpy.gp.Sample_sa("", "", 輸出表, "NEAREST", "", "CURRENT_SLICE")
11、插值分析
# Process: 克里金法
arcpy.gp.Kriging_sa("", "", 輸出表面柵格, "None #", "", "VARIABLE 12", 輸出預測柵格數據的方差)
# Process: 反距離權重法
arcpy.gp.Idw_sa("", "", 輸出柵格, "", "2", "VARIABLE 12", "")
# Process: 含障礙的樣條函數
arcpy.gp.SplineWithBarriers_sa("", "", "", "", 輸出柵格__2_, "0")
# Process: 地形轉柵格
arcpy.gp.TopoToRaster_sa("", 輸出表面柵格__2_, "", "DEFAULT", "20", "", "", "ENFORCE", "CONTOUR", "20", "", "1", "0", "2.5", "100", 輸出河流折線要素, 輸出其余匯點要素, 輸出診斷文件, 輸出參數文件, "", 輸出殘差點要素, 輸出河流和懸崖錯誤點要素, 輸出等值線錯誤點要素)
# Process: 樣條函數法
arcpy.gp.Spline_sa("", "", 輸出柵格__3_, "", "REGULARIZED", "0.1", "12")
# Process: 自然鄰域法
arcpy.gp.NaturalNeighbor_sa("", "", 輸出柵格__4_, "")
# Process: 趨勢面法
arcpy.gp.Trend_sa("", "", 輸出柵格__5_, "", "1", "LINEAR", 輸出_RMS_文件)
# Process: 通過文件實現地形轉柵格
arcpy.gp.TopoToRasterByFile_sa("", 輸出表面柵格__3_, 輸出河流折線要素__2_, 輸出其余匯點要素__2_, 輸出殘差點要素__2_, 輸出河流和懸崖錯誤點要素__2_, 輸出等值線錯誤點要素__2_)
12、數學分析
Spatial Analyst 工具-數學分析
13、條件分析
# Process: 條件函數
arcpy.gp.Con_sa("", "", 輸出柵格, "", "")
# Process: 設為空函數
arcpy.gp.SetNull_sa("", "", 輸出柵格__2_, "")
# Process: 選取函數
arcpy.gp.Pick_sa("", "", 輸出柵格__3_)
14、柵格創建
# Process: 創建常量柵格
arcpy.gp.CreateConstantRaster_sa(輸出柵格, "", "INTEGER", "1", "0 0 250 250")
# Process: 創建正態柵格
arcpy.gp.CreateNormalRaster_sa(輸出柵格__2_, "1", "0 0 250 250")
# Process: 創建隨機柵格
arcpy.gp.CreateRandomRaster_sa(輸出柵格__3_, "", "1", "0 0 250 250")
15、柵格綜合
# Process: Nibble
arcpy.gp.Nibble_sa("", "", 輸出柵格, "ALL_VALUES", "PRESERVE_NODATA", "")
# Process: 眾數濾波
arcpy.gp.MajorityFilter_sa("", 輸出柵格__2_, "FOUR", "MAJORITY")
# Process: 區域合並
arcpy.gp.RegionGroup_sa("", 輸出柵格__3_, "FOUR", "WITHIN", "ADD_LINK", "")
# Process: 收縮
arcpy.gp.Shrink_sa("", 輸出柵格__4_, "", "")
# Process: 散度
arcpy.gp.Expand_sa("", 輸出柵格__5_, "", "")
# Process: 細化
arcpy.gp.Thin_sa("", 輸出柵格__6_, "ZERO", "NO_FILTER", "ROUND", "")
# Process: 聚合
arcpy.gp.Aggregate_sa("", 輸出柵格__7_, "", "SUM", "EXPAND", "DATA")
# Process: 邊界清理
arcpy.gp.BoundaryClean_sa("", 輸出柵格__8_, "NO_SORT", "TWO_WAY")
16、水文分析
# Process: 填窪
arcpy.gp.Fill_sa("", 輸出表面柵格, "")
# Process: 捕捉傾瀉點
arcpy.gp.SnapPourPoint_sa("", "", 輸出柵格, "0", "")
# Process: 柵格河網矢量化
arcpy.gp.StreamToFeature_sa("", "", 輸出折線要素, "SIMPLIFY")
# Process: 水流長度
arcpy.gp.FlowLength_sa("", 輸出柵格__2_, "DOWNSTREAM", "")
# Process: 匯
arcpy.gp.Sink_sa("", 輸出柵格__3_)
# Process: 河流鏈接
arcpy.gp.StreamLink_sa("", "", 輸出柵格__4_)
# Process: 河網分級
arcpy.gp.StreamOrder_sa("", "", 輸出柵格__5_, "STRAHLER")
# Process: 流動距離
arcpy.gp.FlowDistance_sa("", "", 輸出柵格__6_, "", "VERTICAL", "D8", "MINIMUM")
# Process: 流向
arcpy.gp.FlowDirection_sa("", 輸出流向柵格數據, "NORMAL", 輸出下降率柵格數據, "D8")
# Process: 流量
arcpy.gp.FlowAccumulation_sa("", 輸出蓄積柵格數據, "", "FLOAT", "D8")
# Process: 盆域分析
arcpy.gp.Basin_sa("", 輸出柵格__7_)
# Process: 集水區
arcpy.gp.Watershed_sa("", "", 輸出柵格__8_, "")
17、表面分析
# Process: 可見性
arcpy.gp.Visibility_sa("", "", 輸出柵格, 輸出地平面以上的柵格, "FREQUENCY", "ZERO", "1", "FLAT_EARTH", "0.13", "", "", "", "", "", "", "", "", "")
# Process: 含障礙的等值線
arcpy.gp.ContourWithBarriers_sa("", 輸出等值線要素, "", "POLYLINES", "", "NO_EXPLICIT_VALUES_ONLY", "0", "", "0", "", "1")
# Process: 坡向
arcpy.gp.Aspect_sa("", 輸出柵格__2_, "PLANAR", "METER")
# Process: 坡度
arcpy.gp.Slope_sa("", 輸出柵格__3_, "DEGREE", "1", "PLANAR", "METER")
# Process: 填挖方
arcpy.gp.CutFill_sa("", "", 輸出柵格__4_, "1")
# Process: 山體陰影
arcpy.gp.HillShade_sa("", 輸出柵格__5_, "315", "45", "NO_SHADOWS", "1")
# Process: 曲率
arcpy.gp.Curvature_sa("", 輸出曲率柵格, "1", 輸出剖面曲線柵格, 輸出平面曲線柵格)
# Process: 等值線
arcpy.gp.Contour_sa("", 輸出要素類, "", "0", "1", "CONTOUR", "")
# Process: 等值線列表
arcpy.gp.ContourList_sa("", 輸出折線要素, "")
# Process: 視域
arcpy.gp.Viewshed_sa("", "", 輸出柵格__6_, "1", "FLAT_EARTH", "0.13", 輸出地平面以上的柵格__2_)
# Process: 視域 2
arcpy.gp.Viewshed2_sa("", "", 輸出柵格__7_, 輸出地平面以上的柵格__3_, "FREQUENCY", "0 Meters", 輸出觀察點_區域關系表, "0.13", "0 Meters", "", "1 Meters", "", "GROUND", "", "GROUND", "0", "360", "90", "-90", "ALL_SIGHTLINES")
# Process: 視點分析
arcpy.gp.ObserverPoints_sa("", "", 輸出柵格__8_, "1", "FLAT_EARTH", "0.13", 輸出地平面以上的柵格__4_)
18、距離
# Process: 廊道分析
arcpy.gp.Corridor_sa("", "", 輸出柵格)
# Process: 成本分配
arcpy.gp.CostAllocation_sa("", "", 輸出分配柵格數據, "", "", "", 輸出距離柵格數據, 輸出回溯鏈接柵格數據, "", "", "", "", "")
# Process: 成本回溯鏈接
arcpy.gp.CostBackLink_sa("", "", 輸出回溯鏈接柵格數據__2_, "", 輸出距離柵格數據__2_, "", "", "", "", "")
# Process: 成本距離
arcpy.gp.CostDistance_sa("", "", 輸出距離柵格數據__3_, "", 輸出回溯鏈接柵格數據__3_, "", "", "", "", "")
# Process: 成本路徑
arcpy.gp.CostPath_sa("", "", "", 輸出柵格__2_, "EACH_CELL", "", "INPUT_RANGE")
# Process: 成本路徑折線
arcpy.gp.CostPathAsPolyline_sa("", "", "", 輸出折線要素, "BEST_SINGLE", "", "INPUT_RANGE")
# Process: 成本連通性
arcpy.gp.CostConnectivity_sa("", "", 輸出要素類, 相鄰連接的輸出要素類)
# Process: 歐氏分配
arcpy.gp.EucAllocation_sa("", 輸出分配柵格數據__2_, "", "", "", "", 輸出距離柵格數據__4_, 輸出方向柵格數據, "PLANAR", "", 輸出反向柵格)
# Process: 歐氏反向
arcpy.gp.EucBackDirection_sa("", 輸出反向柵格__2_, "", "", "", "PLANAR")
# Process: 歐氏方向
arcpy.gp.EucDirection_sa("", 輸出方向柵格數據__2_, "", "", 輸出距離柵格數據__5_, "PLANAR", "", 輸出反向柵格__3_)
# Process: 歐氏距離
arcpy.gp.EucDistance_sa("", 輸出距離柵格數據__6_, "", "", 輸出方向柵格數據__3_, "PLANAR", "", 輸出反向柵格__4_)
# Process: 路徑距離
arcpy.gp.PathDistance_sa("", 輸出距離柵格數據__7_, "", "", "", "BINARY 1 45", "", "BINARY 1 -30 30", "", 輸出回溯鏈接柵格數據__4_, "", "", "", "", "")
# Process: 路徑距離分配
arcpy.gp.PathAllocation_sa("", 輸出分配柵格數據__3_, "", "", "", "BINARY 1 45", "", "BINARY 1 -30 30", "", "", "", 輸出距離柵格數據__8_, 輸出回溯鏈接柵格數據__5_, "", "", "", "", "")
# Process: 路徑距離回溯鏈接
arcpy.gp.PathBackLink_sa("", 輸出回溯鏈接柵格數據__6_, "", "", "", "BINARY 1 45", "", "BINARY 1 -30 30", "", 輸出距離柵格數據__9_, "", "", "", "", "")
19、鄰域分析
# Process: 塊統計
arcpy.gp.BlockStatistics_sa("", 輸出柵格, "Rectangle 3 3 CELL", "MEAN", "DATA")
# Process: 濾波器
arcpy.gp.Filter_sa("", 輸出柵格__2_, "LOW", "DATA")
# Process: 點統計
arcpy.gp.PointStatistics_sa("", "", 輸出柵格__3_, "", "Rectangle 3 3 CELL", "MEAN")
# Process: 焦點流
arcpy.gp.FocalFlow_sa("", 輸出柵格__4_, "0")
# Process: 焦點統計
arcpy.gp.FocalStatistics_sa("", 輸出柵格__5_, "Rectangle 3 3 CELL", "MEAN", "DATA", "90")
# Process: 線統計
arcpy.gp.LineStatistics_sa("", "", 輸出柵格__6_, "", "", "MEAN")
20、重分類
# Process: 使用 ASCII 文件重分類
arcpy.gp.ReclassByASCIIFile_sa("", "", 輸出柵格, "DATA")
# Process: 使用表重分類
arcpy.gp.ReclassByTable_sa("", "", "", "", "", 輸出柵格__2_, "DATA")
# Process: 分割
arcpy.gp.Slice_sa("", 輸出柵格__3_, "", "EQUAL_INTERVAL", "1")
# Process: 按函數重設比例
arcpy.gp.RescaleByFunction_sa("", 輸出柵格__4_, "MSSMALL # # # # # #", "1", "10")
# Process: 查找表
arcpy.gp.Lookup_sa("", "", 輸出柵格__5_)
# Process: 重分類
arcpy.gp.Reclassify_sa("", "", "", 輸出柵格__6_, "DATA")
