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index 0000000..225a55b Binary files /dev/null and b/Index/ClusterAnalysis_temp/segments.gen differ diff --git a/Index/ClusterAnalysis_temp/segments_2 b/Index/ClusterAnalysis_temp/segments_2 new file mode 100644 index 0000000..fb0cc52 Binary files /dev/null and b/Index/ClusterAnalysis_temp/segments_2 differ diff --git a/README.txt b/README.txt new file mode 100644 index 0000000..3fe4b55 --- /dev/null +++ b/README.txt @@ -0,0 +1,21 @@ +R Example Tools +=============== + + + +Dimistry Pavlushko, Mark Janikas, Konstantin Krivoruchko, Shaun Walbridge and Steve Kopp, Esri Inc + + +Sample ArcMap MXD and ArcGIS Pro Project + +Add .tbx files + +Look at R code to see how it works. + + +Requirements +------------ + +ArcGIS R bridge +R Statistical Computing Software +Geostatistical Analyst Extension for ArcGIS diff --git a/RTools.tbx b/RTools.tbx new file mode 100644 index 0000000..b56a7b8 Binary files /dev/null and b/RTools.tbx differ diff --git a/Scripts/Rplots.pdf b/Scripts/Rplots.pdf new file mode 100644 index 0000000..7a7a68a Binary files /dev/null and b/Scripts/Rplots.pdf differ diff --git a/Scripts/cluster_analysis_as_py.py b/Scripts/cluster_analysis_as_py.py new file mode 100644 index 0000000..93e290e --- /dev/null +++ b/Scripts/cluster_analysis_as_py.py @@ -0,0 +1,71 @@ +# -*- coding: utf-8 -*- +# --------------------------------------------------------------------------- +# cluster_analysis_as_py.py +# Created on: 2015-06-19 16:38:15.00000 +# (generated by ArcGIS/ModelBuilder) +# Usage: cluster_analysis_as_py +# Description: +# --------------------------------------------------------------------------- + +# Set the necessary product code +# import arcinfo + + +# Import arcpy module +import arcpy + +# Load required toolboxes +arcpy.ImportToolbox("Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx") + +# Script arguments +Input_Point_Features = arcpy.GetParameterAsText(0) + +Clipping_Dataset = arcpy.GetParameterAsText(1) + +Output_Uncertancy_Dataset = arcpy.GetParameterAsText(2) + +Output_Classification_Dataset = arcpy.GetParameterAsText(3) +if Output_Classification_Dataset == '#' or not Output_Classification_Dataset: + Output_Classification_Dataset = "%scratchGDB%\\tmp2_Dissolve_Clip" # provide a default value if unspecified + +Output_Density_Raster = arcpy.GetParameterAsText(4) +if Output_Density_Raster == '#' or not Output_Density_Raster: + Output_Density_Raster = "%scratchGDB%\\dens_raster" # provide a default value if unspecified + +Output_Superimposing_Ellipses_Dataset = arcpy.GetParameterAsText(5) + +# Local variables: +tmp2 = "%scratchGDB%\\tmp2" +tmp_dis = "%scratchGDB%\\tmp2_Dissolve" +cn_density = "%scratchGDB%\\cn_density" +dens_clip = "%scratchGDB%\\dens_clip" +Output_geostatistical_layer = "" + +# Set Geoprocessing environments +arcpy.env.extent = Clipping Dataset + +# Process: Cluster Analysis +arcpy.gp.toolbox = "Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx"; +# Warning: the toolbox Z:/projects/r/example-tools/r-sample-tools/RToolbox.tbx DOES NOT have an alias. +# Please assign this toolbox an alias to avoid tool name collisions +# And replace arcpy.gp.clustering(...) with arcpy.clustering_ALIAS(...) +arcpy.gp.clustering(Input_Point_Features, "", Output_Uncertancy_Dataset, Output_Superimposing_Ellipses_Dataset, cn_density) + +# Process: Create Thiessen Polygons +arcpy.CreateThiessenPolygons_analysis(Output_Uncertancy_Dataset, tmp2, "ALL") + +# Process: Dissolve +arcpy.Dissolve_management(tmp2, tmp_dis, "cluster", "", "MULTI_PART", "DISSOLVE_LINES") + +# Process: Clip +arcpy.Clip_analysis(tmp_dis, Clipping_Dataset, Output_Classification_Dataset, "") + +# Process: Clip (2) +arcpy.Clip_analysis(cn_density, Clipping_Dataset, dens_clip, "") + +# Process: Kernel Interpolation With Barriers +tempEnvironment0 = arcpy.env.extent +arcpy.env.extent = Clipping Dataset +arcpy.KernelInterpolationWithBarriers_ga(dens_clip, "density", Output_geostatistical_layer, Output_Density_Raster, "0.263200383398682", "", "POLYNOMIAL5", "", "1", "50", "PREDICTION") +arcpy.env.extent = tempEnvironment0 + diff --git a/Scripts/clustering.R b/Scripts/clustering.R new file mode 100644 index 0000000..4957329 --- /dev/null +++ b/Scripts/clustering.R @@ -0,0 +1,132 @@ +# case study using mclust package +tool_exec <- function(in_params, out_params) { + message(getOption("repos")) + if (!requireNamespace("sp", quietly = TRUE)) + install.packages("sp") + if (!requireNamespace("mclust", quietly = TRUE)) + install.packages("mclust") + require(mclust) + require(sp) + + source_dataset <- in_params[[1]] + nclust <- in_params[[2]] + out_table <- out_params[[1]] + out_ellipses <- out_params[[2]] + out_dens <- out_params[[3]] + + ### read data + arc.progress_label("Loading Dataset") + data <- arc.select(arc.open(source_dataset)) + data_shp <- arc.shape(data) + + #data.xy <- data.frame(x=data_shp$x - x_min, y=data_shp$y - y_min) + # alternatively: data.frame(x=data_shp$x, y=data_shp$y) + data.xy <- cbind(data_shp$x, data_shp$y) + + ######### + arc.progress_label("BIC for Model-Based Clustering...") + treesBIC <- mclustBIC(data.xy, G = nclust) + treesModel <- summary(treesBIC, data.xy) + print(treesModel) + + ### save to shapefile + # treesModel$classification # cluster number + # treesModel$uncertainty # uncertainty of classification + # treesModel$z # conditional probabilities + n <- treesModel$G + cond_probs <- lapply(1:n, function(i) treesModel$z[,i]) + names(cond_probs) <- paste0("cond_prob", 1:n) + + q <- quantile(treesModel$uncertainty, probs = c(0.75, 0.95)) + q7595 <- sapply(treesModel$uncertainty, function(u) { + r <- if (u <= q[1]) 1 else 0 + if (u <= q[2] && r == 0) r <- 2 + if (u > q[2] && r == 0) r <- 3 + stopifnot(r != 0) + r - 1 + }) + + result <- data.frame(data, + cluster = treesModel$classification, + class_uncert = treesModel$uncertainty, + sym_ucert=q7595, + cond_probs) + + #print(head(result)) + arc.progress_label("Writing result dataset...") + arc.write(out_table, result, coords = data_shp) + + ### automatic mapping + arc.progress_label("Best model based on BIC...") + bestModel <- mclustModel(data.xy, treesBIC) + + ### create ellipses + if (!is.null(out_ellipses)) { + arc.progress_label("Writing ellipses dataset...") + polygons <- create.elipses(bestModel) + mu <- bestModel$parameters$mean + sp.df <- SpatialPolygonsDataFrame(polygons, + data=data.frame(clust_n=1:n, + mu_x=mu[1, 1:n], + mu_y=mu[2, 1:n]), + match.ID = F) + ## keep original SpatialReference + shape_info <- list(type="Polygon", WKT=arc.shapeinfo(data_shp)$WKT) + #shape_info <- arc.shapeinfo(sp.df) + arc.write(out_ellipses, sp.df, shape_info=shape_info) + } + + ### create density grid + if (!is.null(out_dens)) { + arc.progress_label("Calculating density...") + options(device="pdf") + options(onefile=FALSE) + grid.n <- 100 + grid <- surfacePlot(verbose=T, data.xy, parameters = bestModel$parameters, + type = "image", what = "density", grid = grid.n, + nlevels = 8, transformation = "none", ask=F) + dev.off() + options(device="windows") + options(onefile=NULL) + xy <- list(x = unlist(lapply(grid$x, function(x) rep(x, grid.n))), + y = rep(grid$y, grid.n)) + arc.progress_label("Writing density dataset...") + arc.write(out_dens, + data=list(density = as.vector(grid$z)), + xy, + list(type="Point", WKT=arc.shapeinfo(data_shp)$WKT)) + } + arc.progress_label("Done") + return(out_params) +} + +create.elipses <- function(bestModel) { + n <- bestModel$G + mu <- bestModel$parameters$mean + sigma <- bestModel$parameters$variance$sigma + pp <- lapply(1:n, function(i) { + xy <- make.ellipse(mu = mu[, i], sigma = sigma[, , i]) + name <- paste0("ellipse", i) + Polygons(list(Polygon(xy)), name) + }) + #p4str <- arc.fromWktToP4(arc.shapeinfo(trees_shp)$WKT) + #SpatialPolygons(pp, 1:n, proj4string=sp::CRS(p4str)) + SpatialPolygons(pp, 1:n, proj4string=sp::CRS()) +} + +make.ellipse <- function(mu, sigma, k = 60) { + p <- length(mu) + if (p != 2) + stop("only two-dimensional case is available") + if (any(unique(dim(sigma)) != p)) + stop("mu and sigma are incompatible") + ev <- eigen(sigma, symmetric = TRUE) + s <- sqrt(rev(sort(ev$values))) + V <- t(ev$vectors[, rev(order(ev$values))]) + theta <- (0:k) * (2 * pi / k) + x <- s[1] * cos(theta) + y <- s[2] * sin(theta) + xy <- cbind(x, y) + xy <- xy %*% V + cbind(xy[,1] + mu[1], xy[,2] + mu[2]) +} diff --git a/Scripts/logreg.R b/Scripts/logreg.R new file mode 100644 index 0000000..0954be0 --- /dev/null +++ b/Scripts/logreg.R @@ -0,0 +1,39 @@ +tool_exec <- function(in_params, out_params) +{ +### handle input gp tool paramaters + input_dataset_name <- in_params[[1]] + primary_field <- in_params[[2]] + fields <- in_params[[3]] + +# --- import dataset to dataframe --- + fc <- arc.open(input_dataset_name) + df <- arc.select(fc, c(primary_field, fields)) + +#------- begin common R script ---------- +# Make Formula + form <- as.formula(paste(primary_field, "~", paste(fields, collapse="+"))) + cat("formula = ") + print(form, showEnv=FALSE) + + m <- glm(form, data=df, family=binomial("logit")) + +# display summary as GP Messages + print(summary(m)) + +# append predicted and residual + + df["predicted"] <- fitted(m) + r <- fitted(m) - df[primary_field] + df["residual"] <- r + +#------- end + +### handle gp tool outputs + output_dataset_name <- out_params[[1]] +# export result dataframe to FeatureClass + arc.write(output_dataset_name, df) + +# update derived parameter + out_params[[2]] <- m$aic + return(out_params) +} diff --git a/Scripts/semi_par_regression.R b/Scripts/semi_par_regression.R new file mode 100644 index 0000000..af14be1 --- /dev/null +++ b/Scripts/semi_par_regression.R @@ -0,0 +1,87 @@ +make_knots <- function(x1, x2, num.knots) { + if (missing(num.knots)) + num.knots <- max(10, min(50, round(length(x1)/4))) + X <- cbind(x1, x2) + dup.inds <- (1:nrow(X))[dup.matrix(X) == T] + if (length(dup.inds) > 0) + X <- X[-dup.inds, ] + knots <- cluster::clara(X, num.knots)$medoids +return(knots) +} + +tool_exec <- function(in_params, out_params) { + #### Load Library for Analysis #### + if (!requireNamespace("SemiPar", quietly = TRUE)) + install.packages("SemiPar") + + library(SemiPar) + + #### Get Input Parameters #### + input_features <- in_params[[1]] + input_predictions <- in_params[[2]] + dep_variable <- in_params[[3]] + nonlin_variables <- in_params[[4]] + lin_variables <- in_params[[5]] + input_knots <- in_params[[6]] + + + #### Import Dataset to Dataframe #### + fc <- arc.open(input_features) + df <- arc.select(fc, c(dep_variable, nonlin_variables, lin_variables)) + df['x'] <- arc.shape(df)$x + df['y'] <- arc.shape(df)$y + + #### Import Knots to DataFrame #### + if (is.null(input_knots)) + { + knots.est <- make_knots(df$x, df$y) + } + else + { + knots_df <- arc.select(arc.open(input_knots)) + knots.est <- make_knots(arc.shape(knots_df)$x, arc.shape(knots_df)$y) + } + + #### Create Spatial Effect #### + fxy <- 'f(x, y, knots=knots.est)' + + #### Create Non-Linear Params #### + nonlin_params <- nonlin_variables + for (i in 1:length(nonlin_params)){ + nonlin_params[i] <- paste("f(", nonlin_params[i], ")", sep = "") + } + nonlin_params <- paste(nonlin_params, collapse = " + ") + + #### Create Linear Params #### + lin_params <- paste(lin_variables, collapse = " + ") + + #### Create Formula and Fit SemiPar #### + e <- as.list(df) + e$knots.est <- knots.est + all_params <- paste(fxy, nonlin_params, lin_params, sep = " + ") + all_params <- paste(dep_variable, " ~ ", all_params) + tryCatch({ + attach(e) + form <- as.formula(all_params) + fit <- spm(form, family="binomial") + }, finally <- detach(e)) + + #### Prediction #### + pred_fc <- arc.open(input_predictions) + pred_df <- arc.select(pred_fc, c(nonlin_variables, lin_variables)) + pred_df['x'] <- arc.shape(pred_df)$x + pred_df['y'] <- arc.shape(pred_df)$y + pred <- with(pred_df, predict.spm(fit, newdata = pred_df, se = TRUE)) + + #### Write Output #### + pred_df['pred_1'] <- pred$fit + pred_df['back_pred_1'] <- 1 / (1+exp(-pred$fit)) + pred_df['stderr_1'] <- pred$se + pred_df['back_se1l'] <- 1 / (1+exp(-pred$fit+1.96*pred$se)) + pred_df['back_se1u'] <- 1/(1+exp(-pred$fit-1.96*pred$se)) + + output_features <- out_params[[1]] + arc.write(output_features, pred_df) + + return(out_params) +} diff --git a/SemiParametricRegression.aprx b/SemiParametricRegression.aprx new file mode 100644 index 0000000..d6a70c8 Binary files /dev/null and b/SemiParametricRegression.aprx differ diff --git a/Symbology/classes_symbology.lyrx b/Symbology/classes_symbology.lyrx new file mode 100644 index 0000000..3aa9c09 --- /dev/null +++ b/Symbology/classes_symbology.lyrx @@ -0,0 +1,890 @@ +{ + "type" : "CIMLayerDocument", + "version" : "1.1.0", + "build" : 3183, + "layers" : [ + "CIMPATH=map/spruce_trees_clustering.xml" + ], + "layerDefinitions" : [ + { + "type" : "CIMFeatureLayer", + "name" : "spruce_trees_clustering", + "uRI" : 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