From 2a052030538bc86a717111d9194baf81e8aa0695 Mon Sep 17 00:00:00 2001 From: Heather Savoy <47045484+HeatherSavoy-USDA@users.noreply.github.com> Date: Mon, 11 Sep 2023 00:27:16 -0600 Subject: [PATCH 1/4] Add first visualization tutorial draft --- Visualizations/GRWG_Maps_R.md | 340 +++++++++++++++++ Visualizations/Visualizations-LandingPage.md | 13 + Visualizations/assets/R_background-1.png | Bin 0 -> 66260 bytes Visualizations/assets/R_elev-1.png | Bin 0 -> 117337 bytes Visualizations/assets/R_final-1.png | Bin 0 -> 88982 bytes .../assets/R_forest_study_area-1.png | Bin 0 -> 61266 bytes Visualizations/assets/R_forests-1.png | Bin 0 -> 42689 bytes .../assets/R_forests_study_area_clear-1.png | Bin 0 -> 56552 bytes Visualizations/assets/R_raster-1.png | Bin 0 -> 87145 bytes .../assets/R_raster_bad_transform-1.png | Bin 0 -> 114091 bytes Visualizations/assets/R_raster_binned-1.png | Bin 0 -> 30354 bytes .../assets/R_raster_manual_transform-1.png | Bin 0 -> 99768 bytes .../assets/R_raster_with_vector-1.png | Bin 0 -> 69975 bytes Visualizations/assets/R_study_area_4326-1.png | Bin 0 -> 39064 bytes Visualizations/assets/R_study_area_5070-1.png | Bin 0 -> 47119 bytes .../assets/R_study_area_sf_plot-1.png | Bin 0 -> 43155 bytes ...mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg | Bin 0 -> 1337590 bytes assets/images/sources.md | 1 + index.md | 6 + tutorials/GRWG_Maps.Rmd | 351 ++++++++++++++++++ 20 files changed, 711 insertions(+) create mode 100644 Visualizations/GRWG_Maps_R.md create mode 100644 Visualizations/Visualizations-LandingPage.md create mode 100644 Visualizations/assets/R_background-1.png create mode 100644 Visualizations/assets/R_elev-1.png create mode 100644 Visualizations/assets/R_final-1.png create mode 100644 Visualizations/assets/R_forest_study_area-1.png create mode 100644 Visualizations/assets/R_forests-1.png create mode 100644 Visualizations/assets/R_forests_study_area_clear-1.png create mode 100644 Visualizations/assets/R_raster-1.png create mode 100644 Visualizations/assets/R_raster_bad_transform-1.png create mode 100644 Visualizations/assets/R_raster_binned-1.png create mode 100644 Visualizations/assets/R_raster_manual_transform-1.png create mode 100644 Visualizations/assets/R_raster_with_vector-1.png create mode 100644 Visualizations/assets/R_study_area_4326-1.png create mode 100644 Visualizations/assets/R_study_area_5070-1.png create mode 100644 Visualizations/assets/R_study_area_sf_plot-1.png create mode 100644 assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg create mode 100644 tutorials/GRWG_Maps.Rmd diff --git a/Visualizations/GRWG_Maps_R.md b/Visualizations/GRWG_Maps_R.md new file mode 100644 index 0000000..3f92766 --- /dev/null +++ b/Visualizations/GRWG_Maps_R.md @@ -0,0 +1,340 @@ +--- +title: Making publication-quality maps in R +layout: single +author: Heather Savoy +author_profile: true +header: + overlay_color: "444444" + overlay_image: /assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg +--- + +**Last Update:** 11 September 2023
**Download RMarkdown**: +[GRWG\_Maps.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_Maps.Rmd) + +## Overview + +This tutorial covers creating maps in R using the `sf` and `ggplot2` +packages. + +To download the Rmarkdown file for this tutorial to your local machine +(or any SCINet cluster within OoD), you can use the following lines: + + library(httr) + tutorial_name <- 'GRWG_Maps.Rmd' + GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name), + write_disk(tutorial_name)) + +*Language:* `R` + +*Primary Libraries/Packages:* + +| Name | Description | Link | +|:--|:--|:--| +| sf | Simple Features for R | https://r-spatial.github.io/sf/ | +| ggplot2 | Create Elegant Data Visualizations Using the Grammar of Graphics | https://ggplot2.tidyverse.org/ | + +## Nomenclature + +- *Raster:* Data defined on a grid of geospatial coordinates +- *CRS:* Coordinate Reference System, also known as a spatial + reference system. A system for defining geospatial coordinates. + +## Data Details + +- Data: +- Link: []() +- Other Details: + +## Steps + +- Acquire data: + - Access informational boundaries. + - Define study area and points. + - Download raster to fill study area. +- Incrementally plot: + - Map background + - Raster + - Study area + - Points + - Annotations +- Format legends, axes, and map scale + +## Step 0: Import Libraries/Packages + + library(httr) # Access data from online resources + library(sf) # Handle vector data + library(terra) # Handle raster data + library(USAboundaries) # US boundaries vector data + library(dplyr) # General data manipulation + library(ggplot2) # Visualizations + library(ggspatial) # Extra geospatial annotations for maps + +## Step 1: Acquire data + +We can use the `us_states()` function from the `USABoundaries` packages +to easily define a study area based on states. This function returns a +`sf` object. For `sf` objects, there is a default plotting method that +can be used. + + # Pull the boundaries for some select states for our study area + my_area <- us_states(states = c('LA','MS','AL','GA','FL')) + + # Save the bounding box for later use + my_bbox <- my_area %>% + st_bbox() + + # Plot using the default plotting method for `sf` objects + plot(my_area['name']) + +![](../Visualizations/assets/R_study_area_sf_plot-1.png) + +But we will use the `ggplot2` functions to build more complicated maps. +With both the `ggplot2` and `sf` packages, we can take advantage of the +`geom_sf()` function to quickly plot `sf` objects in the `ggplot2` +framework. Unlike most `ggplot()` uses, you don’t have to specify the +`x` and `y` mapping aesthetic for `geom_sf()` since that will be assumed +from the geometry column. + + ggplot() + + geom_sf(data=my_area) + +![](../Visualizations/assets/R_study_area_4326-1.png) + +Say you want to use a certain CRS in the project for which you are +making your map. This can affect the plotted map. + + # Specify the target CRS + target_crs <- 5070 + target_crs_str <- paste0('EPSG:',target_crs) + + # Transform area to that CRS and save the bounding box in the CRS for later use + my_area_tcrs <- my_area %>% + st_transform(target_crs) + my_bbox_tcrs <- my_area_tcrs %>% + st_bbox() + + # Plot the transformed area + ggplot() + + geom_sf(data=my_area_tcrs) + +![](../Visualizations/assets/R_study_area_5070-1.png) + +The plot +coordinate system depends on the order of the `geom_sf()` calls, or +what’s specifically stated in `coord_sf()`. + +Let’s now consider another vector dataset we’d like to plot within our +study area. We can use the GeoPlatform API to download a subset of the +*FS National Forests Dataset (US Forest Service Proclaimed Forests)* +dataset from the US Forest Service. + + + # GeoPlatform API url + api_url <- 'https://geoapi.geoplatform.gov' + + # Read collections catalog + collections_url <- paste0(api_url,'/collections') + collections_content <- GET(collections_url) %>% + content() + collections_md <- collections_content$collections + + # Get collection ID for collection with 'forest' in the title + search_term <- 'forest' + collection_titles <- sapply(collections_md, function(col) tolower(col$title)) + collection_id <- collections_md[[grep(search_term,collection_titles)]]$id + + # Download data within bounding box from collection + bbox_str <- paste0(my_bbox,collapse = ',') + data_url <- paste0(collections_url, "/" ,collection_id, + "/items?limit=10000&bbox=", bbox_str) + tmp_file <- 'temp.zip' + GET(data_url, write_disk(tmp_file,overwrite=TRUE)) + + collection_sf <- st_read(tmp_file) + + # View shape + ggplot() + + geom_sf(data=collection_sf) + +![](../Visualizations/assets/R_forests-1.png) + +Now let’s plot both this +forest dataset and our study area. + + ggplot() + + geom_sf(data=my_area_tcrs) + + geom_sf(data=collection_sf) + +![](../Visualizations/assets/R_forest_study_area-1.png) + +First, it is +hard to distinguish the two vector datasets with the default coloring. +Second, we requested forests within a bounding box of our study area. So +let’s find the intersection of our forests and study area, and color the +forests polygons. + + + my_forests_tcrs <- collection_sf %>% + st_transform(target_crs) %>% + st_buffer(1) %>% + st_intersection(my_area_tcrs) + #> Warning: attribute variables are assumed to be spatially constant throughout all geometries + + + ggplot() + + geom_sf(data=my_area_tcrs) + + geom_sf(data=my_forests_tcrs, + fill = 'forestgreen') + +![](../Visualizations/assets/R_forests_study_area_clear-1.png) + +And let’s also get a raster dataset to fill in our study area. We will +grab some elevation from The National Map. Again, we will use our study +area bounding box to define our subset. + + tnm_url <- 'https://elevation.nationalmap.gov/arcgis/rest/services/' + my_raster <- 'elev.tiff' + GET(paste0(tnm_url,'3DEPElevation/ImageServer/exportImage?bbox=',bbox_str, + '&bboxSR=4326&format=tiff&f=image'), + write_disk(my_raster,overwrite=TRUE)) + + elev <- rast(my_raster) + + # Visualize with default terra plotting function for rasters + plot(elev) + +![](../Visualizations/assets/R_elev-1.png) + +We will reproject this raster +into our target CRS and apply a mask defined by our study area. Then, we +will also convert this `terra` object into a `data.frame` to help with +our `ggplot`ting. + + elev_masked <- elev %>% + project(target_crs_str) %>% + mask(my_area_tcrs) + + elev_df <- elev_masked %>% + as.data.frame(xy=TRUE) + + elev_df %>% + ggplot(aes(x,y,fill=elev)) + + geom_raster() + +![](../Visualizations/assets/R_raster-1.png) + + elev_df %>% + ggplot(aes(x,y,fill=elev)) + + geom_raster() + + scale_fill_continuous(trans = 'sqrt') + #> Warning in self$trans$transform(x): NaNs produced + #> Warning: Transformation introduced infinite values in discrete y-axis + +![](../Visualizations/assets/R_raster_bad_transform-1.png) + + elev_df %>% + ggplot(aes(x,y,fill=sqrt(elev+50))) + + geom_raster() + +![](../Visualizations/assets/R_raster_manual_transform-1.png) + + ggplot() + + geom_raster(aes(x,y,fill=elev), + data = elev_df) + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) + +![](../Visualizations/assets/R_raster_binned-1.png) + + ggplot() + + geom_raster(aes(x,y,fill=elev), + data = elev_df) + + geom_sf(data=my_area_tcrs, + fill = NA) + + geom_sf(data=my_forests_tcrs, + fill = 'red') + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) + +![](../Visualizations/assets/R_raster_with_vector-1.png) + +Now, we can add some nice map background layers and annotations. First, +let’s build a background to show the context of neighboring states and +water. + + all_states <- us_states() %>% + st_transform(target_crs_str) + + ggplot() + + geom_sf(data = all_states, #base color for all land + fill = "#e3f7dc", + col = "gray75", + linewidth = 0.15) + + geom_sf(data=my_area_tcrs, #our study area states + fill = NA, + linewidth = 0.5) + + scale_x_continuous(name = 'Longitude', # limit axes to study area bounding box + limits = range(my_bbox_tcrs[c(1,3)])) + + scale_y_continuous(name = 'Latitude', + limits = range(my_bbox_tcrs[c(2,4)])) + + theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), # water! + panel.grid.major = element_line(color = 'white', linewidth = 0.3)) + +![](../Visualizations/assets/R_background-1.png) + +Everything together, plus some annotations! + + + # Define points between which to draw an arrow + arrow_pts <- data.frame(x = c(-82,-89.25), + y = c(29.5, 32.5)) %>% + st_as_sf(coords = c('x','y'), + crs = 4326) %>% + st_transform(target_crs) %>% + st_coordinates() %>% + as_tibble() + + # Draw the complete map + ggplot() + + geom_sf(data = all_states, #base color for all land + fill = "#e3f7dc", + col = "gray75", + linewidth = 0.15) + + geom_raster(aes(x,y,fill=elev), # elevation raster + data = elev_df) + + geom_sf(data=my_area_tcrs, # our study area states + fill = NA, + linewidth = 0.5) + + geom_sf(data=my_area_tcrs %>% # highlight boundary of the study area + st_union(), + fill = NA, + color = 'black', + linewidth = 0.5) + + geom_sf(data=my_forests_tcrs, # forest polygons + fill = 'red', + alpha = 0.5, + color = 'firebrick', + linewidth = 0.25) + + geom_curve(aes(x=arrow_pts$X[1], y = arrow_pts$Y[1], # curved arrow + xend = arrow_pts$X[2], yend = arrow_pts$Y[2]), + curvature = 0.25, + arrow = arrow(length = unit(2,'mm'), + type = 'closed'), + color = 'red') + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) + + scale_x_continuous(name = 'Longitude', + limits = range(my_bbox_tcrs[c(1,3)])) + + scale_y_continuous(name = 'Latitude', + limits = range(my_bbox_tcrs[c(2,4)])) + + annotation_scale(location = "tl") + + annotation_north_arrow(height = unit(1, "cm"), + width = unit(1, "cm")) + + theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), + panel.grid.major = element_line(color = 'white', linewidth = 0.3)) + #> Warning: Removed 23 rows containing missing values (`geom_raster()`). + +![](../Visualizations/assets/R_final-1.png) diff --git a/Visualizations/Visualizations-LandingPage.md b/Visualizations/Visualizations-LandingPage.md new file mode 100644 index 0000000..92a62f3 --- /dev/null +++ b/Visualizations/Visualizations-LandingPage.md @@ -0,0 +1,13 @@ +--- +title: "Geospatial Visualizations" +layout: single +header: + overlay_color: "444444" + overlay_image: assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg +--- + +## Index + +| Tutorial | Python | R | +|:--|:--|:--| +| Creating 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+**Download RMarkdown**: [GRWG_Maps.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_Maps.Rmd) + +## Overview + +This tutorial covers creating maps in R using the `sf` and `ggplot2` packages. + +To download the Rmarkdown file for this tutorial to your local machine (or any +SCINet cluster within OoD), you can use the following lines: + +```r +library(httr) +tutorial_name <- 'GRWG_Maps.Rmd' +GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name), + write_disk(tutorial_name)) +``` + +*Language:* `R` + +*Primary Libraries/Packages:* + +| Name | Description | Link | +|:--|:--|:--| +| sf | Simple Features for R | https://r-spatial.github.io/sf/ | +| ggplot2 | Create Elegant Data Visualizations Using the Grammar of Graphics | https://ggplot2.tidyverse.org/ | + +## Nomenclature + +* *Raster:* Data defined on a grid of geospatial coordinates +* *CRS:* Coordinate Reference System, also known as a spatial reference system. A + system for defining geospatial coordinates. + + +## Data Details + +* Data: +* Link: []() +* Other Details: + + +## Steps + +* Acquire data: + * Access informational boundaries. + * Define study area and points. + * Download raster to fill study area. +* Incrementally plot: + * Map background + * Raster + * Study area + * Points + * Annotations +* Format legends, axes, and map scale + + +## Step 0: Import Libraries/Packages + + +```{r libraries} +library(httr) # Access data from online resources +library(sf) # Handle vector data +library(terra) # Handle raster data +library(USAboundaries) # US boundaries vector data +library(dplyr) # General data manipulation +library(ggplot2) # Visualizations +library(ggspatial) # Extra geospatial annotations for maps +``` + + + +## Step 1: Acquire data + +We can use the `us_states()` function from the `USABoundaries` packages to +easily define a study area based on states. This function returns a `sf` object. +For `sf` objects, there is a default plotting method that can be used. + +```{r study_area_sf_plot} +# Pull the boundaries for some select states for our study area +my_area <- us_states(states = c('LA','MS','AL','GA','FL')) + +# Save the bounding box for later use +my_bbox <- my_area %>% + st_bbox() + +# Plot using the default plotting method for `sf` objects +plot(my_area['name']) +``` + +But we will use the `ggplot2` functions to build more complicated maps. With both +the `ggplot2` and `sf` packages, we can take advantage of the `geom_sf()` function +to quickly plot `sf` objects in the `ggplot2` framework. Unlike most `ggplot()` +uses, you don't have to specify the `x` and `y` mapping aesthetic for `geom_sf()` +since that will be assumed from the geometry column. + +```{r study_area_4326} +ggplot() + + geom_sf(data=my_area) +``` + +Say you want to use a certain CRS in the project for which you are making your +map. This can affect the plotted map. + +```{r study_area_5070} +# Specify the target CRS +target_crs <- 5070 +target_crs_str <- paste0('EPSG:',target_crs) + +# Transform area to that CRS and save the bounding box in the CRS for later use +my_area_tcrs <- my_area %>% + st_transform(target_crs) +my_bbox_tcrs <- my_area_tcrs %>% + st_bbox() + +# Plot the transformed area +ggplot() + + geom_sf(data=my_area_tcrs) +``` +The plot coordinate system depends on the order of the `geom_sf()` calls, or +what's specifically stated in `coord_sf()`. + + +Let's now consider another vector dataset we'd like to plot within our study area. +We can use the GeoPlatform API to download a subset of the +*FS National Forests Dataset (US Forest Service Proclaimed Forests)* dataset from +the US Forest Service. + +```{r forests} + +# GeoPlatform API url +api_url <- 'https://geoapi.geoplatform.gov' + +# Read collections catalog +collections_url <- paste0(api_url,'/collections') +collections_content <- GET(collections_url) %>% + content() +collections_md <- collections_content$collections + +# Get collection ID for collection with 'forest' in the title +search_term <- 'forest' +collection_titles <- sapply(collections_md, function(col) tolower(col$title)) +collection_id <- collections_md[[grep(search_term,collection_titles)]]$id + +# Download data within bounding box from collection +bbox_str <- paste0(my_bbox,collapse = ',') +data_url <- paste0(collections_url, "/" ,collection_id, + "/items?limit=10000&bbox=", bbox_str) +tmp_file <- 'temp.zip' +GET(data_url, write_disk(tmp_file,overwrite=TRUE)) +collection_sf <- st_read(tmp_file) + +# View shape +ggplot() + + geom_sf(data=collection_sf) + + +``` +Now let's plot both this forest dataset and our study area. +```{r forest_study_area} +ggplot() + + geom_sf(data=my_area_tcrs) + + geom_sf(data=collection_sf) +``` +First, it is hard to distinguish the two vector datasets with the default coloring. +Second, we requested forests within a bounding box of our study area. So let's +find the intersection of our forests and study area, and color the forests polygons. +```{r forests_study_area_clear} + +my_forests_tcrs <- collection_sf %>% + st_transform(target_crs) %>% + st_buffer(1) %>% + st_intersection(my_area_tcrs) + + +ggplot() + + geom_sf(data=my_area_tcrs) + + geom_sf(data=my_forests_tcrs, + fill = 'forestgreen') +``` + +And let's also get a raster dataset to fill in our study area. We will grab some +elevation from The National Map. Again, we will use our study area bounding +box to define our subset. + +```{r elev} +tnm_url <- 'https://elevation.nationalmap.gov/arcgis/rest/services/' +my_raster <- 'elev.tiff' +GET(paste0(tnm_url,'3DEPElevation/ImageServer/exportImage?bbox=',bbox_str, + '&bboxSR=4326&format=tiff&f=image'), + write_disk(my_raster,overwrite=TRUE)) +elev <- rast(my_raster) + +# Visualize with default terra plotting function for rasters +plot(elev) +``` +We will reproject this raster into our target CRS and apply a mask defined by +our study area. Then, we will also convert this `terra` object into a +`data.frame` to help with our `ggplot`ting. + +```{r elev_table} +elev_masked <- elev %>% + project(target_crs_str) %>% + mask(my_area_tcrs) + +elev_df <- elev_masked %>% + as.data.frame(xy=TRUE) + +``` + +```{r raster} +elev_df %>% + ggplot(aes(x,y,fill=elev)) + + geom_raster() +``` + +```{r raster_bad_transform} +elev_df %>% + ggplot(aes(x,y,fill=elev)) + + geom_raster() + + scale_fill_continuous(trans = 'sqrt') +``` + + +```{r raster_manual_transform} +elev_df %>% + ggplot(aes(x,y,fill=sqrt(elev+50))) + + geom_raster() +``` + +```{r raster_binned} +ggplot() + + geom_raster(aes(x,y,fill=elev), + data = elev_df) + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) +``` + + +```{r raster_with_vector} +ggplot() + + geom_raster(aes(x,y,fill=elev), + data = elev_df) + + geom_sf(data=my_area_tcrs, + fill = NA) + + geom_sf(data=my_forests_tcrs, + fill = 'red') + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) +``` + +Now, we can add some nice map background layers and annotations. First, +let's build a background to show the context of neighboring states and water. + +```{r background} +all_states <- us_states() %>% + st_transform(target_crs_str) + +ggplot() + + geom_sf(data = all_states, #base color for all land + fill = "#e3f7dc", + col = "gray75", + linewidth = 0.15) + + geom_sf(data=my_area_tcrs, #our study area states + fill = NA, + linewidth = 0.5) + + scale_x_continuous(name = 'Longitude', # limit axes to study area bounding box + limits = range(my_bbox_tcrs[c(1,3)])) + + scale_y_continuous(name = 'Latitude', + limits = range(my_bbox_tcrs[c(2,4)])) + + theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), # water! + panel.grid.major = element_line(color = 'white', linewidth = 0.3)) +``` + +Everything together, plus some annotations! + +```{r final} + +# Define points between which to draw an arrow +arrow_pts <- data.frame(x = c(-82,-89.25), + y = c(29.5, 32.5)) %>% + st_as_sf(coords = c('x','y'), + crs = 4326) %>% + st_transform(target_crs) %>% + st_coordinates() %>% + as_tibble() + +# Draw the complete map +ggplot() + + geom_sf(data = all_states, #base color for all land + fill = "#e3f7dc", + col = "gray75", + linewidth = 0.15) + + geom_raster(aes(x,y,fill=elev), # elevation raster + data = elev_df) + + geom_sf(data=my_area_tcrs, # our study area states + fill = NA, + linewidth = 0.5) + + geom_sf(data=my_area_tcrs %>% # highlight boundary of the study area + st_union(), + fill = NA, + color = 'black', + linewidth = 0.5) + + geom_sf(data=my_forests_tcrs, # forest polygons + fill = 'red', + alpha = 0.5, + color = 'firebrick', + linewidth = 0.25) + + geom_curve(aes(x=arrow_pts$X[1], y = arrow_pts$Y[1], # curved arrow + xend = arrow_pts$X[2], yend = arrow_pts$Y[2]), + curvature = 0.25, + arrow = arrow(length = unit(2,'mm'), + type = 'closed'), + color = 'red') + + scale_fill_fermenter(palette = 'BrBG', + name = 'Elevation', + breaks = c(-50,0,50,250,500,750)) + + scale_x_continuous(name = 'Longitude', + limits = range(my_bbox_tcrs[c(1,3)])) + + scale_y_continuous(name = 'Latitude', + limits = range(my_bbox_tcrs[c(2,4)])) + + annotation_scale(location = "tl") + + annotation_north_arrow(height = unit(1, "cm"), + width = unit(1, "cm")) + + theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), + panel.grid.major = element_line(color = 'white', linewidth = 0.3)) + + +``` + From 307426eeb23b83f10aa94a867089bff4c08d4e54 Mon Sep 17 00:00:00 2001 From: Heather Savoy <47045484+HeatherSavoy-USDA@users.noreply.github.com> Date: Sun, 17 Sep 2023 14:28:41 -0600 Subject: [PATCH 2/4] Add file formats section with storage implications page --- FileFormats/FileFormats-LandingPage.md | 15 + FileFormats/GRWG_StorageImplications_gdal.md | 299 ++++++++++++++++++ ...isaacson-hAJhORQHk94-unsplash-1200_800.jpg | Bin 0 -> 537493 bytes assets/images/sources.md | 1 + index.md | 6 + 5 files changed, 321 insertions(+) create mode 100644 FileFormats/FileFormats-LandingPage.md create mode 100644 FileFormats/GRWG_StorageImplications_gdal.md create mode 100644 assets/images/hans-isaacson-hAJhORQHk94-unsplash-1200_800.jpg diff --git a/FileFormats/FileFormats-LandingPage.md b/FileFormats/FileFormats-LandingPage.md new file mode 100644 index 0000000..cd6a541 --- /dev/null +++ b/FileFormats/FileFormats-LandingPage.md @@ -0,0 +1,15 @@ +--- +title: "Geospatial File Formats" +layout: single +header: + overlay_color: "444444" + overlay_image: assets/images/hans-isaacson-hAJhORQHk94-unsplash-1200_800.jpg +--- + +## Index + +| Tutorial | GDAL | +|:--|:--| +| Raster file properties: computation time and storage implications | [view](GRWG_StorageImplications_gdal) | + + diff --git a/FileFormats/GRWG_StorageImplications_gdal.md b/FileFormats/GRWG_StorageImplications_gdal.md new file mode 100644 index 0000000..c81ce79 --- /dev/null +++ b/FileFormats/GRWG_StorageImplications_gdal.md @@ -0,0 +1,299 @@ +--- +title: "Raster file properties: computation time and storage implications" +layout: single +author: Heather Savoy +author_profile: true +header: + overlay_color: "444444" + overlay_image: /assets/images/margaret-weir-GZyjbLNOaFg-unsplash_dark.jpg +--- + +**Last Update:** 17 September 2023
+ +## Overview + +This tutorial explores raster file properties, how those properties affect file +sizes, and how they affect geospatial processing execution times. Commands shown +are for direct use of the Geospatial Data Abstraction Library (GDAL) which is a +library that is often used by other geospatial software including R packages, +Python packages, QGIS, and ESRI products. + +Both Atlas and Ceres have GDAL installed as a module. To start +using GDAL on either cluster, load the module with the following command: + +```bash +module load gdal +``` + +*Language:* `bash` + +*Primary Libraries/Packages:* + +| Name | Description | Link | +|:--|:--|:--| +| GDAL | Geospatial Data Abstraction Library | https://gdal.org/ | + +## Nomenclature + +* *Raster:* Data defined on a grid of geospatial coordinates +* *Block:* The configuration of how raster values are read from the file. +* *Data type:* If raster values are stored as integers or floating point numbers, + if they are signed, and how many bits are used to store a value. + + +## Sections + +* Print file properties of raster datasets +* Effects of data types on file sizes +* Effects of tiles on cropping execution time +* Effects of virtual rasters on cropping execution time + +## Section 1: Print file properties of raster datasets + +First, we will look into what kind of raster file properties can be provided by GDAL. +There is a command called `gdalinfo` which will print such information for whichever +raster filename you pass to it. + +```bash +gdalinfo +``` + +To illustrate a variety of raster file property combinations, we will look at the datasets +availabile in the Geospatial Common Data Library (GeoCDL) on Ceres (see +[this tutorial](../ExampleGeoWorkflows/GRWG22_GeoCDL_R.md) +to learn more). + +For example, calling `gdalinfo` on one of the source PRISM data files in the GeoCDL, +the output looks like this: + +```bash +gdalinfo prism/PRISM_ppt_stable_4kmM3_200807_bil.bil +``` + +``` +Driver: EHdr/ESRI .hdr Labelled +Files: prism/PRISM_ppt_stable_4kmM3_200807_bil.bil + prism/PRISM_ppt_stable_4kmM3_200807_bil.bil.aux.xml + prism/PRISM_ppt_stable_4kmM3_200807_bil.hdr + prism/PRISM_ppt_stable_4kmM3_200807_bil.stx + prism/PRISM_ppt_stable_4kmM3_200807_bil.prj +Size is 1405, 621 +Coordinate System is: +GEOGCRS["NAD83", + DATUM["North American Datum 1983", + ELLIPSOID["GRS 1980",6378137,298.257222101, + LENGTHUNIT["metre",1]], + ID["EPSG",6269]], + PRIMEM["Greenwich",0, + ANGLEUNIT["Degree",0.0174532925199433]], + CS[ellipsoidal,2], + AXIS["longitude",east, + ORDER[1], + ANGLEUNIT["Degree",0.0174532925199433]], + AXIS["latitude",north, + ORDER[2], + ANGLEUNIT["Degree",0.0174532925199433]]] +Data axis to CRS axis mapping: 1,2 +Origin = (-125.020833333333357,49.937499999999751) +Pixel Size = (0.041666666666700,-0.041666666666700) +Corner Coordinates: +Upper Left (-125.0208333, 49.9375000) (125d 1'15.00"W, 49d56'15.00"N) +Lower Left (-125.0208333, 24.0625000) (125d 1'15.00"W, 24d 3'45.00"N) +Upper Right ( -66.4791667, 49.9375000) ( 66d28'45.00"W, 49d56'15.00"N) +Lower Right ( -66.4791667, 24.0625000) ( 66d28'45.00"W, 24d 3'45.00"N) +Center ( -95.7500000, 37.0000000) ( 95d45' 0.00"W, 37d 0' 0.00"N) +Band 1 Block=1405x1 Type=Float32, ColorInterp=Undefined + Min=0.000 Max=556.523 + Minimum=0.000, Maximum=556.523, Mean=72.033, StdDev=61.444 + NoData Value=-9999 + Metadata: + STATISTICS_MAXIMUM=556.52301025391 + STATISTICS_MEAN=72.033263630928 + STATISTICS_MINIMUM=0 + STATISTICS_STDDEV=61.444120607318 +``` + +This output has some recognizable geospatial metadata such as the coordinate reference +system (`Coordinate System`) and the spatial resolution (`Pixel Size`). The raster file +properties discernable from this output that we will be focusing on are: + +* *File format* (`Driver`, `Files`): This is discerned by the file extension of the + raster file being described and potentially also the GDAL driver used to read + that type of file. Here, the PRISM file has an extension `.bil` for which GDAL + uses its `EHdr/ESRI .hdr Labelled` driver to read. The `.bil` format is developed + by ESRI, along with other similar raster file formats, so GDAL uses a combined + driver to read these formats. When reading the GDAL documentation about what + format-specific options there might be, + [the content will be organized by driver](https://gdal.org/drivers/raster/index.html). + For example, there is a [GeoTIFF driver](https://gdal.org/drivers/raster/gtiff.html). +* *Data type* (`Type`): If raster values are stored as integers or floating point numbers, + if they are signed, and how many bits are used to store a value. +* *X-Y dimensions* (`Size`): This is the number of columns and rows of pixels in the + image discretizing space (typically horizontal space). +* *Block size* (`Block`): The dimensions (columns and rows) of the tile or strip + (or scanline) that represent the file access chunk size. When blocks are configured + as square subsets of a raster, they are referred to as tiles. + +Here is a table summarizing these properties from several of the GeoCDL datasets: + +| Dataset | File format | Data type | X-Y dimensions | Block size | +|:-- |:-- |:-- |:-- |:-- | +| DaymetV4 | .nc | Float32 | 7814, 8075 | 1000x1000 | +| GTOPO | .dem | Int16 | 4800, 6000[^1] | 4800x1 | +| NASS CDL | .tif | Byte | 153811, 96523 | 512x512 | +| NLCD | .img | Byte | 161190, 104424 | 512x512 | +| PRISM | .bil | Float32 | 1405, 621 | 1405x1 | +| SMAP-HB 1km | .nc | Float32 | 6996, 3120 | 1400x624 | +| SRTM | .bil | Int16 | 3601,3601[^1] | 3601x1 | +| VIP | .hdf | Int16, UInt16, Int8 | 3600, 7200 | 512x512 | + + +[^1]: This large dataset is stored in multiple individual files covering different geographic areas. The properties listed here are for one of the individual files. + + +The following sections will explore some file storage implications for data types +and computation time implications for block sizes. + + +## Section 2: Effects of data types on file sizes + +Data types indicate the range and precision of values that can be stored in +the file. There are three main components to differentiate data types: + +* **Integer or floating point**: is it a whole number (integer) or a decimal + (floating point)? It takes more bits to store the extra precision of 1.0001 + than 1, and thus you can store a greater range of values as integers than + floats or consume less disk space storing similar ranges of values as integers + than floats. +* **Signed or unsigned**: do the values range from negative to positive, or + are they strictly non-negative? This effects the range of values that can be + stored. +* **Number of bits**: how many bits are used to store a value? This is typically + 8-bit to 64-bit and dictates the ultimate file size. + +The values that are able to be stored in a raster depend on the data type. +For example, the `UInt16` (unsigned 16-bit integer) data type can +hold a range of 2^16 integers within [0, 65535]. The `Int16` +(signed 16-bit integer) data type also covers 2^16 integer values, but within +[-32768, 32767]. + +The data types that GDAL supports in ascending order of size are: +`Byte`, `Int8` < `UInt16`, `Int16` < `UInt32`, `Int32`, `Float32` < `UInt64`, `Int64`, `Float64`. +The support for some of these data types will depend on the version of GDAL +installed. Based on our table of GeoCDL datasets, the `Byte`, `Int16`, and +`Float32` are common data types. Each band in a raster data file can have +its own data type, e.g., VIP has multiple data types in the table above. +Note that these data type names are from GDAL's naming scheme. Other +programs might have slightly different names, e.g. 'uint8' +instead of 'Byte' for an unsigned 8-bit integer. + +Some datasets, like [VIP](https://doi.org/10.5067/MEaSUREs/VIP/VIP01.004), +use scaling factors (e.g., dividing NDVI by 0.0001) +to help reduce file size with the use of integer instead of float data types. +We can explore this concept a bit further to see the effect of modifying a +raster's data type (with and without scaling) on the file size and data loss. + +For example, let's use our example PRISM data file again, which holds precipitation +(ppt) values as the `Float32` data type. Since precipitation is typically a measurement +of height and a continuous number, storing it as a floating point number makes sense. +However, depending on the precision needed, disk space could be saved by storing +these precipitation values as scaled integers. In the `gdalinfo` output printed above, +the range of precipitation values in the file is [0, 556.523] mm. If we scale those values +by 100 ([0, 55652.3]) and then save them as integers ([0, 55652]), we could store them as +`UInt16`, which would take up half the disk space (going from 32-bit to 16-bit data type) +while also only losing precision around 0.001 mm of precipitation. We could not, however, +successfully store all values as `Int16` since 55652 is outside the supported range +of [-32768, 32767] (pixels with scaled values >32767 would become no-data pixels). +Also, precipitation itself is non-negative, so an unsigned data type is sensible. + +The table below summarizes a range of scaling and data type conversion outcomes for +this PRISM file. Ultimately, scaling by 100 and saving the result as a `UInt16` has +the best balance of reducing file size and preventing data loss out of all scenarios +considered. + +| Data type | Writing error | File size (MB) | Scaling factor -> RMSE from
original file (% pixels lost) | +|:-- |:-- |:-- |:-- | +| `Byte` | Warning: detected values
outside of the limits of datatype
INT1U (for all scaling factors)| 0.873 | 1 (None) -> 0.56 (1.2%)
10 -> 0.054 (70%)
100 -> 0.0042 (92%) | +| `UInt16` | | 1.7 | 1 (None) -> 0.56
10 -> 0.056
100 -> 0.0057 | +| `Int16` | Warning: detected values
outside of the limits of datatype
INT2S (for scaling factor = 100) | 1.7 | 1 (None) -> 0.56
10 -> 0.056
100 -> 0.0057 (0.3%) | +| `UInt32` | | 3.5 | 1 (None) -> 0.56
10 -> 0.056
100 -> 0.0057 | +| `Int32` | | 3.5 | 1 (None) -> 0.56
10 -> 0.056
100 -> 0.0057 | +| `Float32`
(original data type) | | 3.5 | N/A | + + +## Section 3: Effects of tiles on cropping execution time + +Modifying the block size of a raster can improve the computation time of certain +geospatial analyses. For example, we can look at the effect of setting tiles for a +GeoTIFF so that values are read in by square (typically of dimensions that are +multiples of 16) chunks. + +In GDAL, you can create tiles or change tile sizes for a raster with the `gdal_translate` +command with the following parameters: + +```bash +gdal_translate -co TILED=YES -co BLOCKXSIZE=512 -co BLOCKYSIZE=512 input.tif interim.tif +``` + +Or remove or not create tiles: + +```bash +gdal_translate -co TILED=NO input.tif interim.tif +``` + +To illustrate the effect of tiles on computation time, lets take the NASS CDL dataset. +From our dataset table above, we see that it is already tiled with blocks of 512x512 pixels. +We can use the `gdal_translate -co TILED=NO` line above to make a copy that is not tiled, +which results in a block size of 153811x1 (one row of pixels across the whole image.) +This dataset covers CONUS and we will ask GDAL to crop it to some small county-sized area +specified by a shapefile: + +```bash +gdalwarp -crop_to_cutline -cutline my_county.shp interim.tif output.tif +``` + +The table below shows how long that cropping command took to execute with the without +the tiles. With tiles, it took half the time! This is because GDAL had to read in a smaller +amount of the entire raster to cover the small cropping area, a small number of +local 512x512 tiles instead of many 153811x1 strips across CONUS. So this computation time benefit +will depend on the relative sizes of the blocks, the cropping area, and the original raster extent. + +| Tile/block size | Computation time (s) | +|:-- |:-- | +| 512x512 | ~0.8 | +| 153811x1 | ~1.5 | + + +## Section 4: Effects of virtual rasters on cropping execution time + +Another raster file concept that can save computation time is the GDAL Virtual Format raster (VRT). +This is essentially a way to store interim raster operation *instructions*, as opposed to the +actualy interim raster *values*, which can save processing time if the total sequence of all +raster operations needed can be calculated in one combined GDAL operation. + +For example, let's revisit the process of creating tiled GeoTIFFs to improve cropping execution +times. If we request first a tiled geoTIFF, then crop it: + +```bash +gdal_translate -co TILED=YES -co BLOCKXSIZE=512 -co BLOCKYSIZE=512 input.tif interim.tif +gdalwarp -crop_to_cutline -cutline input.shp interim.tif output.tif +``` +that can still take several minutes to tile if that GeoTIFF is large, e.g. several GB. + +What we can do instead is request a tiled VRT and pass that (essentially instructions, not data) +to the cropping command: + +```bash +gdal_translate -co TILED=YES -co BLOCKXSIZE=512 -co BLOCKYSIZE=512 input.tif interim.vrt +gdalwarp -crop_to_cutline -cutline input.shp interim.vrt output.tif +``` + +This generates the same result as making the interim GeoTIFF instead of the VRT, but the +VRT method takes less than 1 second instead of minutes. + +| VRT | Task | Computation time (s) | +|:-- |:-- |:-- | +| N | Creating the tiled GeoTIFF (several GB) | ~ 4.5 min | +| N | Cropping the tiled GeoTIFF | < 1 sec | +| Y | Creating the tiled VRT (37 KB) | < 1 sec | +| Y | Cropping the 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assets/images/conny-schneider-pREq0ns_p_E-unsplash-1200_800.jpg create mode 100644 tutorials/GRWG_SpatialInterpolation.Rmd diff --git a/SpatialModeling/GRWG_SpatialInterpolation_R.md b/SpatialModeling/GRWG_SpatialInterpolation_R.md new file mode 100644 index 0000000..a0b79d2 --- /dev/null +++ b/SpatialModeling/GRWG_SpatialInterpolation_R.md @@ -0,0 +1,459 @@ +--- +title: "Spatial interpolation methods: geostatistics to machine learning" +layout: single +author: Heather Savoy +author_profile: true +header: + overlay_color: "444444" + overlay_image: /assets/images/margaret-weir-GZyjbLNOaFg-unsplash_dark.jpg +--- + +**Last Update:** 17 September 2023
    **Download RMarkdown**: +[GRWG\_SpatialInterpolation.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_SpatialInterpolation.Rmd) + +## Overview + +This tutorial compares two spatial interpolation techniques that predict +gridded maps of a target variable given point observations and, +potentially, gridded covariates. The content of this tutorial is +modified from the [tutorial](https://github.com/thengl/GeoMLA) that is +supplementary material to the scientific article: Hengl, T., Nussbaum, +M., Wright, M. and Heuvelink, G.B.M., 2018. [*“Random Forest as a +Generic Framework for Predictive Modeling of Spatial and Spatio-temporal +Variables”*](https://doi.org/10.7717/peerj.5518). Modifications include +limiting the number of examples and re-ordering examples, using more +up-to-date R packages, and cutting and supplementing the commentary +text. + +To download the Rmarkdown file for this tutorial to either Ceres or +Atlas within Open on Demand, you can use the following lines: + + library(httr) + tutorial_name <- 'GRWG_SpatialInterpolation.Rmd' + GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name), + write_disk(tutorial_name)) + +*Language:* `R` + +*Primary Libraries/Packages:* + +| Name | Description | Link | +|:--|:--|:--| +| geoR | Analysis of Geostatistical Data | https://cran.r-project.org/web/packages/geoR/index.html | +| ranger | A Fast Implementation of Random Forests | https://cran.r-project.org/web/packages/ranger/index.html | + +## Nomenclature + +- *(Spatial) Interpolation:* Using observations of dependent and + independent variables to estimate the value of the dependent + variable at unobserved independent variable values. For spatial + applications, this can be the case of having point observations + (i.e., variable observations at known x-y coordinates) and then + predicting a gridded map of the variable (i.e., estimating the + variable at the remaining x-y cells in the study area). +- *Geostatistics:* Statistics focusing on spatial datasets, + e.g. treating spatially heterogeneous variables as random variables + with covariance structures describing spatial dependence. +- *Kriging:* A geostatistical approach for spatial interpolatation. It + uses a variogram model fitted to point observations to define + spatial dependence. +- *Variogram:* A model describing the relationship between variance + between values of pairs of points as a function of the spatial + distance between those points. +- *Random Forest:* A machine learning algorithm that uses an ensemble + of decision trees. + +## Data Details + +- Data: `meuse` data set +- Link: [`sp` + package](https://cran.r-project.org/web/packages/sp/index.html) +- Other Details: This dataset gives locations and topsoil heavy metal + concentrations, along with a number of soil and landscape variables + at the observation locations, collected in a flood plain of the + river Meuse, near the village of Stein (NL). Heavy metal + concentrations are from composite samples of an area of + approximately 15 m x 15 m. + +## Analysis Steps + +- Pre-process the `meuse` data set to be `sf` and `raster` objects +- Use kriging to interpolate zinc concentrations across the study area +- Use Random Forest to interpolate zinc concentrations across the + study area +- Compare predictions and prediction error between the two + interpolation methods + +## Step 0: Import Libraries/Packages + +For this tutorial, we will use several packages for processing spatial +data. The `sf` and `raster` packages help us store the `meuse` +observations and grid in spatial data structures. `geoR` contains +functions for fitting variograms and performing kriging. `ranger` +contains functions to fit Random Forest models and make predictions from +them. + + # spatial data + library(sf) # vector data + library(raster) # raster data + + # geostatistics + library(geoR) + + # random forest + library(ranger) + + # general data manipulation + library(tidyr) + library(dplyr) + + # visualizations + library(ggplot2) + +## Step 1: Pre-process `meuse` dataset + +Our dataset is contained in the `sp` package. We can use the `demo` +function to load both the point observation data (new variable `meuse`) +and the study area’s grid (new variable `meuse.grid`) as `sp` objects. +Note that in most cases, defining the grid (e.g., determining the +appropriate spatial resolution and extent) would be a necessary initial +step based on the science question. + + # This dataset is from the `sp` package, which the `raster` package + # loaded for us above. + # point observations: meuse + # study area grid: meuse.grid + demo('meuse', echo = FALSE) + +We will translate these into data objects that are compatible with more +recent or widely-used R packages: zinc concentration point observations +into a `sf` object and the study area grid into a `raster` object. + + obs_sf <- meuse['zinc'] %>% + st_as_sf() + + grid_raster <- raster(meuse.grid['soil']) + +Let’s take a quick look at the dataset. Below is a map of the study area +grid and the observation locations, plus the location of the river Meuse +for reference (the `demo` call also created the `meuse.riv` variable, +though we will only use it for visualization purposes). + + grid_raster %>% + as.data.frame(xy = TRUE) %>% + filter(!is.na(soil_levels)) %>% # study area only + ggplot() + + geom_tile(aes(x,y), + fill = 'gray90', + color = 'black') + + geom_sf(data=obs_sf, + aes(color = zinc)) + + geom_sf(data = meuse.riv %>% + st_as_sf(), + fill = 'dodgerblue') + + theme_minimal() + + scale_y_continuous(limits = extent(grid_raster)[c(3,4)]) + + scale_color_viridis_c(trans = 'log1p') + +![](../SpatialModeling/assets/R_studyarea-1.png) + +## Step 2: Interpolate map from point observations using kriging + +The first step to perform the interpolation by kriging is to fit a +variogram to the point observations. Variograms are typically fit by +weighted ordinary least squares with higher weights on shorter +distances. Since the variogram is a non-linear function, non-linear +least squares is performed. Unlike simple linear regression, initial +values are required for parameters are required for non-linear least +squares. So we will first plot the empirical variogram +(semivariance-distance values based on observations) to inform our +initial values. The `geoR` package has a function `variog` to calculate +the empirical variogram and it comes with a general plotting function +for plotting that empirical variogram. + + variog(data = log1p(obs_sf$zinc), + coords = obs_sf %>% + st_coordinates()) %>% + plot() + + #> variog: computing omnidirectional variogram + +![](../SpatialModeling/assets/R_empirical-1.png) + +In the original tutorial, the initial value of the partial sill +parameter was taken from the sample variance of all natural +log-transformed zinc concentration observations and the initial values +of the range parameter was 500 m. We will use these values as well. + +The `likfit` function performs the variogram fitting based on the +initial values and the empirical variogram. Zinc concentration is +natural log-transformed to help meet the assumption of normality (and +this is why the Box-Cox parameter `lambda` is set to zero). + + initial_psill <- var(log1p(obs_sf$zinc)) + initial_range <- 500 # meters + ini.v <- c(initial_psill,initial_range) + zinc_vgm <- likfit(data = obs_sf$zinc, + coords = obs_sf %>% + st_coordinates(), + lambda=0, + ini=ini.v, + cov.model="exponential") + + #> kappa not used for the exponential correlation function + #> --------------------------------------------------------------- + #> likfit: likelihood maximisation using the function optim. + #> likfit: Use control() to pass additional + #> arguments for the maximisation function. + #> For further details see documentation for optim. + #> likfit: It is highly advisable to run this function several + #> times with different initial values for the parameters. + #> likfit: WARNING: This step can be time demanding! + #> --------------------------------------------------------------- + #> likfit: end of numerical maximisation. + + zinc_vgm + + #> likfit: estimated model parameters: + #> beta tausq sigmasq phi + #> " 6.1553" " 0.0164" " 0.5928" "500.0001" + #> Practical Range with cor=0.05 for asymptotic range: 1497.866 + #> + #> likfit: maximised log-likelihood = -1014 + +To generate predictions and kriging variance using `geoR` we run: + + zinc_ok <- krige.conv(data = obs_sf$zinc, + coords = obs_sf %>% + st_coordinates(), + locations=coordinates(grid_raster), + krige=krige.control(obj.model=zinc_vgm)) # our fitted variogram + + #> krige.conv: model with constant mean + #> krige.conv: performing the Box-Cox data transformation + #> krige.conv: back-transforming the predicted mean and variance + #> krige.conv: Kriging performed using global neighbourhood + + grid_raster$zinc_ok <- zinc_ok$predict + grid_raster$zinc_ok_range <- sqrt(zinc_ok$krige.var) + + grid_raster %>% + as.data.frame(xy = TRUE) %>% + filter(!is.na(soil_levels)) %>% # study area only + ggplot() + + geom_tile(aes(x,y,fill=zinc_ok)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5) + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + +![](../SpatialModeling/assets/R_krige-1.png) + +in this case geoR automatically back-transforms values to the original +scale, which is a recommended feature. + +## Step 3: Interpolate map from point observations using Random Forest + +We can derive buffer distances by generating N rasters of distances to +each i=1,..,N point observations. This can be coded in multiple +different ways, but the code chunk below takes the approach of using +`sf::st_distance()` to calculate the distances. + +First, we will create a list of grid cell coordinates into a `sf` +object. Then, we iterative fill out layers in a raster brick (one layer +for each observation) with the distances to each observation from every +cell in the raster. + + # Create a `sf` object from the raster cell center coordinates + grid_sf <- grid_raster %>% + coordinates() %>% # extract cell coordinates + as_tibble() %>% # or as.data.frame, because st_as_sf() doesn't like matrices + st_as_sf(coords = c('x','y'), + crs = st_crs(obs_sf)) # keep dataset's CRS + + + # Define an empty raster brick with N la + grid_distances <- brick(grid_raster, + values = FALSE, + nl = nrow(obs_sf)) + for(i in 1:nrow(obs_sf)){ + target_pt <- obs_sf[i,] + grid_distances[[i]] <- st_distance(grid_sf, target_pt) + } + + #> Warning in readAll(x): cannot read values; there is no file associated with this RasterBrick + +which takes few seconds as it generates 155 gridded maps. The value of +the target variable `zinc` can be now modeled as a function of buffer +distances: + + ### For each observation, what layer distance + dn0 <- paste(names(grid_distances), collapse="+") + fm0 <- as.formula(paste("zinc ~ ", dn0)) + fm0 + + #> zinc ~ layer.1 + layer.2 + layer.3 + layer.4 + layer.5 + layer.6 + + #> layer.7 + layer.8 + layer.9 + layer.10 + layer.11 + layer.12 + + #> layer.13 + layer.14 + layer.15 + layer.16 + layer.17 + layer.18 + + #> layer.19 + layer.20 + layer.21 + layer.22 + layer.23 + layer.24 + + #> layer.25 + layer.26 + layer.27 + layer.28 + layer.29 + layer.30 + + #> layer.31 + layer.32 + layer.33 + layer.34 + layer.35 + layer.36 + + #> layer.37 + layer.38 + layer.39 + layer.40 + layer.41 + layer.42 + + #> layer.43 + layer.44 + layer.45 + layer.46 + layer.47 + layer.48 + + #> layer.49 + layer.50 + layer.51 + layer.52 + layer.53 + layer.54 + + #> layer.55 + layer.56 + layer.57 + layer.58 + layer.59 + layer.60 + + #> layer.61 + layer.62 + layer.63 + layer.64 + layer.65 + layer.66 + + #> layer.67 + layer.68 + layer.69 + layer.70 + layer.71 + layer.72 + + #> layer.73 + layer.74 + layer.75 + layer.76 + layer.77 + layer.78 + + #> layer.79 + layer.80 + layer.81 + layer.82 + layer.83 + layer.84 + + #> layer.85 + layer.86 + layer.87 + layer.88 + layer.89 + layer.90 + + #> layer.91 + layer.92 + layer.93 + layer.94 + layer.95 + layer.96 + + #> layer.97 + layer.98 + layer.99 + layer.100 + layer.101 + + #> layer.102 + layer.103 + layer.104 + layer.105 + layer.106 + + #> layer.107 + layer.108 + layer.109 + layer.110 + layer.111 + + #> layer.112 + layer.113 + layer.114 + layer.115 + layer.116 + + #> layer.117 + layer.118 + layer.119 + layer.120 + layer.121 + + #> layer.122 + layer.123 + layer.124 + layer.125 + layer.126 + + #> layer.127 + layer.128 + layer.129 + layer.130 + layer.131 + + #> layer.132 + layer.133 + layer.134 + layer.135 + layer.136 + + #> layer.137 + layer.138 + layer.139 + layer.140 + layer.141 + + #> layer.142 + layer.143 + layer.144 + layer.145 + layer.146 + + #> layer.147 + layer.148 + layer.149 + layer.150 + layer.151 + + #> layer.152 + layer.153 + layer.154 + layer.155 + +Further analysis is similar to any regression analysis using the [ranger +package](https://github.com/imbs-hl/ranger). First we overlay points and +grids to create a regression matrix: + + ### What are those layer values at each of the observations + obs_dist <- raster::extract(grid_distances, obs_sf) + rm_zinc <- cbind(obs_sf["zinc"], obs_dist) %>% + as_tibble() + +To also estimate the prediction error variance i.e. prediction intervals +we set `quantreg=TRUE` which initiates the Quantile Regression RF +approach: + + m.zinc <- ranger(fm0, rm_zinc, quantreg=TRUE, num.trees=150, seed=1) + m.zinc + + #> Ranger result + #> + #> Call: + #> ranger(fm0, rm_zinc, quantreg = TRUE, num.trees = 150, seed = 1) + #> + #> Type: Regression + #> Number of trees: 150 + #> Sample size: 155 + #> Number of independent variables: 155 + #> Mtry: 12 + #> Target node size: 5 + #> Variable importance mode: none + #> Splitrule: variance + #> OOB prediction error (MSE): 68003.69 + #> R squared (OOB): 0.4953088 + +This shows that, only buffer distance explain almost 50% of variation in +the target variable. To generate prediction for the `zinc` variable and +using the RFsp model, we use: + + ## 1 s.d. quantiles + quantiles = c((1-.682)/2, 0.5, 1-(1-.682)/2) + zinc_rfd <- predict(m.zinc, + values(grid_distances), + quantiles=quantiles, + type="quantiles")$predictions + str(zinc_rfd) + + #> num [1:8112, 1:3] 281 281 281 281 281 281 281 281 281 281 ... + #> - attr(*, "dimnames")=List of 2 + #> ..$ : NULL + #> ..$ : chr [1:3] "quantile= 0.159" "quantile= 0.5" "quantile= 0.841" + +this will estimate 67% probability lower and upper limits and median +value. Note that “median” can often be different from the “mean”, so if +you prefer to derive mean, then the `quantreg=FALSE` needs to be used as +the Quantile Regression Forests approach can only derive median. + +## Step 4: Compare predictions and prediction error between the two interpolation methods + +Comparison of predictions and prediction error maps produced using geoR +(ordinary kriging) and RFsp (with buffer distances and by just using +coordinates) is given below. + + grid_raster$zinc_rfd = zinc_rfd[,2] + grid_raster$zinc_rfd_range = (zinc_rfd[,3]-zinc_rfd[,1])/2 + grid_raster <- mask(grid_raster, grid_raster[[1]]) + + grid_raster %>% + as.data.frame(xy=TRUE) %>% + dplyr::select(x,y,zinc_ok, zinc_rfd) %>% + pivot_longer(-c(x,y)) %>% + ggplot() + + geom_tile(aes(x,y,fill=value)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5, + color = 'black') + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + + facet_wrap(~name) + +![](../SpatialModeling/assets/R_final_estimates-1.png) + + grid_raster %>% + as.data.frame(xy=TRUE) %>% + dplyr::select(x,y,zinc_ok_range, zinc_rfd_range) %>% + pivot_longer(-c(x,y)) %>% + ggplot() + + geom_tile(aes(x,y,fill=value)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5, + color = 'black') + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + + facet_wrap(~name) + +![](../SpatialModeling/assets/R_final_variances-1.png) + +*Figure: Comparison of predictions based on ordinary kriging as +implemented in the geoR package (left) and random forest (right) for +Zinc concentrations, Meuse data set: (first row) predicted +concentrations in log-scale and (second row) standard deviation of the +prediction errors for OK and RF methods.* + +From the plot above, it can be concluded that RFsp gives very similar +results as ordinary kriging via geoR. The differences between geoR and +RFsp, however, are: + +- RF requires no transformation i.e. works equally good with skewed + and normally distributed variables; in general RF, has much less + statistical assumptions than model-based geostatistics, +- RF prediction error variance in average shows somewhat stronger + contrast than OK variance map i.e. it emphasizes isolated less + probable local points much more than geoR, +- RFsp is significantly more computational as distances need to be + derived from any sampling point to all new predictions locations, +- geoR uses global model parameters and as such also prediction + patterns are relatively uniform, RFsp on the other hand (being a + tree-based) will produce patterns that as much as possible match + data, + +## Further reading / more complicated examples + +Please see the [original tutorial](https://github.com/thengl/GeoMLA) if +you would like to see more coding examples of these two techniques that +include: + +- additional gridded covariates (e.g., elevation) +- categorical target variables +- temporally-variable target variables +- and more! diff --git a/SpatialModeling/SpatialModeling-LandingPage.md b/SpatialModeling/SpatialModeling-LandingPage.md new file mode 100644 index 0000000..e815e72 --- /dev/null +++ b/SpatialModeling/SpatialModeling-LandingPage.md @@ -0,0 +1,13 @@ +--- +title: "Spatial Modeling" +layout: single +header: + overlay_color: "444444" + overlay_image: assets/images/conny-schneider-pREq0ns_p_E-unsplash-1200_800.jpg +--- + +## Index + +| Tutorial | Python | R | +|:--|:--|:--| +| Spatial interpolation methods: geostatistics to machine learning | Coming soon | [view](GRWG_SpatialInterpolation_R) [download](../tutorials/GRWG_SpatialInterpolation.Rmd) | diff --git a/SpatialModeling/assets/R_empirical-1.png b/SpatialModeling/assets/R_empirical-1.png new file mode 100644 index 0000000000000000000000000000000000000000..ff38d7294f3d42524274377830b63d003ccdf2f8 GIT binary patch literal 23036 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    +**Download RMarkdown**: [GRWG_SpatialInterpolation.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_SpatialInterpolation.Rmd) + +## Overview + +This tutorial compares two spatial interpolation techniques that predict gridded +maps of a target variable given point observations and, potentially, gridded +covariates. The content of this tutorial is modified from the +[tutorial](https://github.com/thengl/GeoMLA) that +is supplementary material to the scientific article: Hengl, T., Nussbaum, M., +Wright, M. and Heuvelink, G.B.M., 2018. +[_"Random Forest as a Generic Framework for Predictive Modeling of Spatial and Spatio-temporal Variables"_](https://doi.org/10.7717/peerj.5518). +Modifications include limiting the +number of examples and re-ordering examples, using more up-to-date R packages, +and cutting and supplementing the commentary text. + + +To download the Rmarkdown file for this tutorial to either Ceres or Atlas within +Open on Demand, you can use the following lines: + +```r +library(httr) +tutorial_name <- 'GRWG_SpatialInterpolation.Rmd' +GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name), + write_disk(tutorial_name)) +``` + +*Language:* `R` + +*Primary Libraries/Packages:* + +| Name | Description | Link | +|:--|:--|:--| +| geoR | Analysis of Geostatistical Data | https://cran.r-project.org/web/packages/geoR/index.html | +| ranger | A Fast Implementation of Random Forests | https://cran.r-project.org/web/packages/ranger/index.html | + +## Nomenclature + +* *(Spatial) Interpolation:* Using observations of dependent and independent + variables to estimate the value of the dependent variable at unobserved + independent variable values. For spatial applications, this can be the case of + having point observations (i.e., variable observations at known x-y + coordinates) and then predicting a gridded map of the variable (i.e., estimating + the variable at the remaining x-y cells in the study area). +* *Geostatistics:* Statistics focusing on spatial datasets, e.g. treating spatially + heterogeneous variables as random variables with covariance structures describing + spatial dependence. +* *Kriging:* A geostatistical approach for spatial interpolatation. It uses a + variogram model fitted to point observations to define spatial dependence. +* *Variogram:* A model describing the relationship between variance between + values of pairs of points as a function of the spatial distance between those + points. +* *Random Forest:* A machine learning algorithm that uses an ensemble of decision + trees. + +## Data Details + +* Data: `meuse` data set +* Link: [ `sp` package](https://cran.r-project.org/web/packages/sp/index.html) +* Other Details: This dataset gives locations and topsoil heavy metal concentrations, + along with a number of soil and landscape variables at the observation locations, + collected in a flood plain of the river Meuse, near the village of Stein (NL). + Heavy metal concentrations are from composite samples of an area of approximately + 15 m x 15 m. + +## Analysis Steps + +* Pre-process the `meuse` data set to be `sf` and `raster` objects +* Use kriging to interpolate zinc concentrations across the study area +* Use Random Forest to interpolate zinc concentrations across the study area +* Compare predictions and prediction error between the two interpolation methods + +## Step 0: Import Libraries/Packages + +For this tutorial, we will use several packages for processing spatial data. The +`sf` and `raster` packages help us store the `meuse` observations and grid in +spatial data structures. `geoR` contains functions for fitting variograms and +performing kriging. `ranger` contains functions to fit Random Forest models and +make predictions from them. + + +```{r, echo=TRUE} +# spatial data +library(sf) # vector data +library(raster) # raster data + +# geostatistics +library(geoR) + +# random forest +library(ranger) + +# general data manipulation +library(tidyr) +library(dplyr) + +# visualizations +library(ggplot2) +``` + + +## Step 1: Pre-process `meuse` dataset + +Our dataset is contained in the `sp` package. We can use the `demo` function to +load both the point observation data (new variable `meuse`) and the study area's +grid (new variable `meuse.grid`) as `sp` objects. Note that in most cases, +defining the grid (e.g., determining the appropriate spatial resolution and +extent) would be a necessary initial step based on the science question. +```{r meuse} +# This dataset is from the `sp` package, which the `raster` package +# loaded for us above. +# point observations: meuse +# study area grid: meuse.grid +demo('meuse', echo = FALSE) +``` + + +We will translate these into data objects that are compatible with more recent +or widely-used R packages: zinc concentration point observations into a `sf` +object and the study area grid into a `raster` object. + +```{r preprocess} +obs_sf <- meuse['zinc'] %>% + st_as_sf() + +grid_raster <- raster(meuse.grid['soil']) + +``` + +Let's take a quick look at the dataset. Below is a map of the study area grid and +the observation locations, plus the location of the river Meuse for reference +(the `demo` call also created the `meuse.riv` variable, though we will only +use it for visualization purposes). + +```{r studyarea} +grid_raster %>% + as.data.frame(xy = TRUE) %>% + filter(!is.na(soil_levels)) %>% # study area only + ggplot() + + geom_tile(aes(x,y), + fill = 'gray90', + color = 'black') + + geom_sf(data=obs_sf, + aes(color = zinc)) + + geom_sf(data = meuse.riv %>% + st_as_sf(), + fill = 'dodgerblue') + + theme_minimal() + + scale_y_continuous(limits = extent(grid_raster)[c(3,4)]) + + scale_color_viridis_c(trans = 'log1p') +``` + + + + +## Step 2: Interpolate map from point observations using kriging + +The first step to perform the interpolation by kriging is to fit a variogram to +the point observations. Variograms are typically fit by weighted ordinary least +squares with higher weights on shorter distances. Since the variogram is a +non-linear function, non-linear least squares is performed. Unlike simple linear +regression, initial values are required for parameters are required for non-linear +least squares. So we will first plot the empirical variogram (semivariance-distance +values based on observations) to inform our initial values. The `geoR` package +has a function `variog` to calculate the empirical variogram and it comes with +a general plotting function for plotting that empirical variogram. + +```{r empirical} +variog(data = log1p(obs_sf$zinc), + coords = obs_sf %>% + st_coordinates()) %>% + plot() +``` + + +In the original tutorial, the initial value of the partial sill parameter was taken +from the sample variance of all natural log-transformed zinc concentration +observations and the initial values of the range parameter was 500 m. We will use +these values as well. + +The `likfit` function performs the variogram fitting based on the initial values +and the empirical variogram. Zinc concentration is natural log-transformed to help +meet the assumption of normality (and this is why the Box-Cox parameter `lambda` +is set to zero). + + +```{r} +initial_psill <- var(log1p(obs_sf$zinc)) +initial_range <- 500 # meters +ini.v <- c(initial_psill,initial_range) +zinc_vgm <- likfit(data = obs_sf$zinc, + coords = obs_sf %>% + st_coordinates(), + lambda=0, + ini=ini.v, + cov.model="exponential") +zinc_vgm + +``` + +To generate predictions and kriging variance using `geoR` we run: + +```{r krige} +zinc_ok <- krige.conv(data = obs_sf$zinc, + coords = obs_sf %>% + st_coordinates(), + locations=coordinates(grid_raster), + krige=krige.control(obj.model=zinc_vgm)) # our fitted variogram +grid_raster$zinc_ok <- zinc_ok$predict +grid_raster$zinc_ok_range <- sqrt(zinc_ok$krige.var) + +grid_raster %>% + as.data.frame(xy = TRUE) %>% + filter(!is.na(soil_levels)) %>% # study area only + ggplot() + + geom_tile(aes(x,y,fill=zinc_ok)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5) + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + + +``` + +in this case geoR automatically back-transforms values to the original scale, +which is a recommended feature. + + +## Step 3: Interpolate map from point observations using Random Forest + +We can derive buffer distances by generating N rasters of distances to each +i=1,..,N point observations. This can be coded in multiple different ways, +but the code chunk below takes the approach of using `sf::st_distance()` to +calculate the distances. + +First, we will create a list of grid cell coordinates into a `sf` object. Then, +we iterative fill out layers in a raster brick (one layer for each observation) +with the distances to each observation from every cell in the raster. + +```{r bufferdist} + +# Create a `sf` object from the raster cell center coordinates +grid_sf <- grid_raster %>% + coordinates() %>% # extract cell coordinates + as_tibble() %>% # or as.data.frame, because st_as_sf() doesn't like matrices + st_as_sf(coords = c('x','y'), + crs = st_crs(obs_sf)) # keep dataset's CRS + + +# Define an empty raster brick with N la +grid_distances <- brick(grid_raster, + values = FALSE, + nl = nrow(obs_sf)) +for(i in 1:nrow(obs_sf)){ + target_pt <- obs_sf[i,] + grid_distances[[i]] <- st_distance(grid_sf, target_pt) +} +``` + +which takes few seconds as it generates 155 gridded maps. The value of the target +variable `zinc` can be now modeled as a function of buffer distances: + +```{r} +### For each observation, what layer distance +dn0 <- paste(names(grid_distances), collapse="+") +fm0 <- as.formula(paste("zinc ~ ", dn0)) +fm0 +``` + +Further analysis is similar to any regression analysis using the +[ranger package](https://github.com/imbs-hl/ranger). First we overlay points and +grids to create a regression matrix: + +```{r} +### What are those layer values at each of the observations +obs_dist <- raster::extract(grid_distances, obs_sf) +rm_zinc <- cbind(obs_sf["zinc"], obs_dist) %>% + as_tibble() +``` + +To also estimate the prediction error variance i.e. prediction intervals we set +`quantreg=TRUE` which initiates the Quantile Regression RF approach: + +```{r} +m.zinc <- ranger(fm0, rm_zinc, quantreg=TRUE, num.trees=150, seed=1) +m.zinc +``` + +This shows that, only buffer distance explain almost 50% of variation in the +target variable. To generate prediction for the `zinc` variable and using the +RFsp model, we use: + +```{r} +## 1 s.d. quantiles +quantiles = c((1-.682)/2, 0.5, 1-(1-.682)/2) +zinc_rfd <- predict(m.zinc, + values(grid_distances), + quantiles=quantiles, + type="quantiles")$predictions +str(zinc_rfd) +``` + +this will estimate 67% probability lower and upper limits and median value. Note +that "median" can often be different from the "mean", so if you prefer to derive +mean, then the `quantreg=FALSE` needs to be used as the Quantile Regression +Forests approach can only derive median. + +## Step 4: Compare predictions and prediction error between the two interpolation methods + +Comparison of predictions and prediction error maps produced using geoR (ordinary +kriging) and RFsp (with buffer distances and by just using coordinates) is given +below. + + +```{r final_estimates} +grid_raster$zinc_rfd = zinc_rfd[,2] +grid_raster$zinc_rfd_range = (zinc_rfd[,3]-zinc_rfd[,1])/2 +grid_raster <- mask(grid_raster, grid_raster[[1]]) + +grid_raster %>% + as.data.frame(xy=TRUE) %>% + dplyr::select(x,y,zinc_ok, zinc_rfd) %>% + pivot_longer(-c(x,y)) %>% + ggplot() + + geom_tile(aes(x,y,fill=value)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5, + color = 'black') + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + + facet_wrap(~name) +``` + + +```{r final_variances} +grid_raster %>% + as.data.frame(xy=TRUE) %>% + dplyr::select(x,y,zinc_ok_range, zinc_rfd_range) %>% + pivot_longer(-c(x,y)) %>% + ggplot() + + geom_tile(aes(x,y,fill=value)) + + geom_sf(data=obs_sf, + shape = 3, + size = 0.5, + color = 'black') + + scale_fill_distiller(palette = 'RdYlBu', + trans = 'log1p', + na.value = NA) + + theme_minimal() + + facet_wrap(~name) +``` + + +_Figure: Comparison of predictions based on ordinary kriging as implemented in the geoR package (left) and random forest (right) for Zinc concentrations, Meuse data set: (first row) predicted concentrations in log-scale and (second row) standard deviation of the prediction errors for OK and RF methods._ + +From the plot above, it can be concluded that RFsp gives very similar results as ordinary kriging via geoR. The differences between geoR and RFsp, however, are: + +- RF requires no transformation i.e. works equally good with skewed and normally distributed variables; in general RF, has much less statistical assumptions than model-based geostatistics, +- RF prediction error variance in average shows somewhat stronger contrast than OK variance map i.e. it emphasizes isolated less probable local points much more than geoR, +- RFsp is significantly more computational as distances need to be derived from any sampling point to all new predictions locations, +- geoR uses global model parameters and as such also prediction patterns are relatively uniform, RFsp on the other hand (being a tree-based) will produce patterns that as much as possible match data, + + +## Further reading / more complicated examples + +Please see the [original tutorial](https://github.com/thengl/GeoMLA) if you would +like to see more coding examples of these two techniques that include: + +* additional gridded covariates (e.g., elevation) +* categorical target variables +* temporally-variable target variables +* and more! + + + + From a13077c7a68abc8c9a3a2aec97feeaaa5bc69ef1 Mon Sep 17 00:00:00 2001 From: Heather Savoy <47045484+HeatherSavoy-USDA@users.noreply.github.com> Date: Sun, 17 Sep 2023 21:20:04 -0600 Subject: [PATCH 4/4] Update maps in R tutorials --- Visualizations/GRWG_Maps_R.md | 100 ++++++++++++++++++++++------------ tutorials/GRWG_Maps.Rmd | 61 +++++++++++++++++---- 2 files changed, 115 insertions(+), 46 deletions(-) diff --git a/Visualizations/GRWG_Maps_R.md b/Visualizations/GRWG_Maps_R.md index 3f92766..ab193ac 100644 --- a/Visualizations/GRWG_Maps_R.md +++ b/Visualizations/GRWG_Maps_R.md @@ -8,7 +8,7 @@ header: overlay_image: /assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg --- -**Last Update:** 11 September 2023
    **Download RMarkdown**: +**Last Update:** 17 September 2023
    **Download RMarkdown**: [GRWG\_Maps.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_Maps.Rmd) ## Overview @@ -41,16 +41,25 @@ To download the Rmarkdown file for this tutorial to your local machine ## Data Details -- Data: -- Link: []() -- Other Details: +* Data: FS National Forests Dataset (US Forest Service Proclaimed Forests) +* Link: [https://www.geoplatform.gov/metadata/bd04f8d8-96cc-5eac-96af-566bea975ba3](https://www.geoplatform.gov/metadata/bd04f8d8-96cc-5eac-96af-566bea975ba3) +* Other Details: The FS National Forests Dataset (US Forest Service Proclaimed + Forests) is a depiction of the boundaries encompassing the National Forest + System (NFS) lands. + +* Data: 3DEP Bare Earth Digital Elevation Model +* Link [https://www.usgs.gov/3d-elevation-program](https://www.usgs.gov/3d-elevation-program) +* Other Details: To respond to growing needs for high-quality elevation data, + the goal of 3DEP is to complete acquisition of nationwide lidar (IfSAR in AK) + to provide the first-ever national baseline of consistent high-resolution + topographic elevation data – both bare earth and 3D point clouds. ## Steps - Acquire data: - - Access informational boundaries. - - Define study area and points. - - Download raster to fill study area. + - Access informational boundaries + - Define study area and points + - Download raster to fill study area - Incrementally plot: - Map background - Raster @@ -117,18 +126,16 @@ making your map. This can affect the plotted map. ggplot() + geom_sf(data=my_area_tcrs) -![](../Visualizations/assets/R_study_area_5070-1.png) +![](../Visualizations/assets/R_study_area_5070-1.png) -The plot -coordinate system depends on the order of the `geom_sf()` calls, or -what’s specifically stated in `coord_sf()`. +The plot coordinate system depends on the order of the `geom_sf()` +calls, or what’s specifically stated in `coord_sf()`. Let’s now consider another vector dataset we’d like to plot within our study area. We can use the GeoPlatform API to download a subset of the *FS National Forests Dataset (US Forest Service Proclaimed Forests)* dataset from the US Forest Service. - # GeoPlatform API url api_url <- 'https://geoapi.geoplatform.gov' @@ -156,30 +163,25 @@ dataset from the US Forest Service. ggplot() + geom_sf(data=collection_sf) -![](../Visualizations/assets/R_forests-1.png) +![](../Visualizations/assets/R_forests-1.png) -Now let’s plot both this -forest dataset and our study area. +Now let’s plot both this forest dataset and our study area. ggplot() + geom_sf(data=my_area_tcrs) + geom_sf(data=collection_sf) -![](../Visualizations/assets/R_forest_study_area-1.png) - -First, it is -hard to distinguish the two vector datasets with the default coloring. -Second, we requested forests within a bounding box of our study area. So -let’s find the intersection of our forests and study area, and color the -forests polygons. +![](../Visualizations/assets/R_forest_study_area-1.png) +First, it is hard to distinguish the two vector datasets with the +default coloring. Second, we requested forests within a bounding box of +our study area. So let’s find the intersection of our forests and study +area, and color the forests polygons. my_forests_tcrs <- collection_sf %>% st_transform(target_crs) %>% st_buffer(1) %>% st_intersection(my_area_tcrs) - #> Warning: attribute variables are assumed to be spatially constant throughout all geometries - ggplot() + geom_sf(data=my_area_tcrs) + @@ -189,8 +191,8 @@ forests polygons. ![](../Visualizations/assets/R_forests_study_area_clear-1.png) And let’s also get a raster dataset to fill in our study area. We will -grab some elevation from The National Map. Again, we will use our study -area bounding box to define our subset. +download some elevation from The National Map. Again, we will use our +study area bounding box to define our subset. tnm_url <- 'https://elevation.nationalmap.gov/arcgis/rest/services/' my_raster <- 'elev.tiff' @@ -203,12 +205,11 @@ area bounding box to define our subset. # Visualize with default terra plotting function for rasters plot(elev) -![](../Visualizations/assets/R_elev-1.png) +![](../Visualizations/assets/R_elev-1.png) -We will reproject this raster -into our target CRS and apply a mask defined by our study area. Then, we -will also convert this `terra` object into a `data.frame` to help with -our `ggplot`ting. +We will reproject this raster into our target CRS and apply a mask +defined by our study area. Then, we will also convert this `terra` +object into a `data.frame` to help with our `ggplot`ting. elev_masked <- elev %>% project(target_crs_str) %>% @@ -217,27 +218,49 @@ our `ggplot`ting. elev_df <- elev_masked %>% as.data.frame(xy=TRUE) +With the default color scale, the elevation is difficult to distinguish +across the study area. + elev_df %>% ggplot(aes(x,y,fill=elev)) + geom_raster() ![](../Visualizations/assets/R_raster-1.png) +There is an option to apply transformations within the fill color scale, +`scale_fill_continuous`, e.g., the square-root transform which can +increase the visual contrast in the elevation raster. However, there are +areas below sea level in our study area so these pixels will become `NA` +after the square-root transform. + elev_df %>% ggplot(aes(x,y,fill=elev)) + geom_raster() + scale_fill_continuous(trans = 'sqrt') + #> Warning in self$trans$transform(x): NaNs produced + #> Warning: Transformation introduced infinite values in discrete y-axis ![](../Visualizations/assets/R_raster_bad_transform-1.png) +You can also manually modify your raster variable being plotted to +perform similar transformations, including more flexibility like an +offset for our case to deal with the below sea level values. The +down-side is you would also have to manually edit the legend to make it +more interpretable. + elev_df %>% ggplot(aes(x,y,fill=sqrt(elev+50))) + geom_raster() ![](../Visualizations/assets/R_raster_manual_transform-1.png) +Another option is to break up your fill color scale into bins. One +option for binning continous values in `ggplot2` is the +`scale_*_fermenter` function that relies on the `RColorBrewer` package. +Here, we will use this option and set manual breaks in our elevation. + ggplot() + geom_raster(aes(x,y,fill=elev), data = elev_df) + @@ -247,6 +270,9 @@ our `ggplot`ting. ![](../Visualizations/assets/R_raster_binned-1.png) +So here is the current state of our map with the elevation raster and +the study area and forest polygons: + ggplot() + geom_raster(aes(x,y,fill=elev), data = elev_df) + @@ -262,7 +288,10 @@ our `ggplot`ting. Now, we can add some nice map background layers and annotations. First, let’s build a background to show the context of neighboring states and -water. +water. We use the `USABoundaries` package again to create a US states +background and fill those states with a light green color (`#e3f7dc`). +We also set the panel background fill color with a light blue +(`#afdbed`) since this represents the water areas in the map. all_states <- us_states() %>% st_transform(target_crs_str) @@ -284,8 +313,9 @@ water. ![](../Visualizations/assets/R_background-1.png) -Everything together, plus some annotations! - +Now, let’s combine all of our incremental steps into one plot. We can +also add some annotations like a north arrow, a scale bar, and some +arrows. # Define points between which to draw an arrow arrow_pts <- data.frame(x = c(-82,-89.25), @@ -335,6 +365,6 @@ Everything together, plus some annotations! width = unit(1, "cm")) + theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), panel.grid.major = element_line(color = 'white', linewidth = 0.3)) - #> Warning: Removed 23 rows containing missing values (`geom_raster()`). + ![](../Visualizations/assets/R_final-1.png) diff --git a/tutorials/GRWG_Maps.Rmd b/tutorials/GRWG_Maps.Rmd index 21190b7..acf04ce 100644 --- a/tutorials/GRWG_Maps.Rmd +++ b/tutorials/GRWG_Maps.Rmd @@ -1,7 +1,7 @@ ```{r setup, include=FALSE} knitr::opts_chunk$set( echo = TRUE, - collapse = TRUE, + collapse = FALSE, comment = "#>", fig.path = "../Visualizations/assets/R_" ) @@ -19,7 +19,7 @@ header: --- --> -**Last Update:** 11 September 2023
    +**Last Update:** 17 September 2023
    **Download RMarkdown**: [GRWG_Maps.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_Maps.Rmd) ## Overview @@ -54,17 +54,26 @@ GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name), ## Data Details -* Data: -* Link: []() -* Other Details: +* Data: FS National Forests Dataset (US Forest Service Proclaimed Forests) +* Link: [https://www.geoplatform.gov/metadata/bd04f8d8-96cc-5eac-96af-566bea975ba3](https://www.geoplatform.gov/metadata/bd04f8d8-96cc-5eac-96af-566bea975ba3) +* Other Details: The FS National Forests Dataset (US Forest Service Proclaimed + Forests) is a depiction of the boundaries encompassing the National Forest + System (NFS) lands. + +* Data: 3DEP Bare Earth Digital Elevation Model +* Link [https://www.usgs.gov/3d-elevation-program](https://www.usgs.gov/3d-elevation-program) +* Other Details: To respond to growing needs for high-quality elevation data, + the goal of 3DEP is to complete acquisition of nationwide lidar (IfSAR in AK) + to provide the first-ever national baseline of consistent high-resolution + topographic elevation data – both bare earth and 3D point clouds. ## Steps * Acquire data: - * Access informational boundaries. - * Define study area and points. - * Download raster to fill study area. + * Access informational boundaries + * Define study area and points + * Download raster to fill study area * Incrementally plot: * Map background * Raster @@ -136,6 +145,7 @@ my_bbox_tcrs <- my_area_tcrs %>% ggplot() + geom_sf(data=my_area_tcrs) ``` + The plot coordinate system depends on the order of the `geom_sf()` calls, or what's specifically stated in `coord_sf()`. @@ -175,15 +185,19 @@ ggplot() + ``` + Now let's plot both this forest dataset and our study area. + ```{r forest_study_area} ggplot() + geom_sf(data=my_area_tcrs) + geom_sf(data=collection_sf) ``` + First, it is hard to distinguish the two vector datasets with the default coloring. Second, we requested forests within a bounding box of our study area. So let's find the intersection of our forests and study area, and color the forests polygons. + ```{r forests_study_area_clear} my_forests_tcrs <- collection_sf %>% @@ -198,8 +212,8 @@ ggplot() + fill = 'forestgreen') ``` -And let's also get a raster dataset to fill in our study area. We will grab some -elevation from The National Map. Again, we will use our study area bounding +And let's also get a raster dataset to fill in our study area. We will download +some elevation from The National Map. Again, we will use our study area bounding box to define our subset. ```{r elev} @@ -213,6 +227,7 @@ elev <- rast(my_raster) # Visualize with default terra plotting function for rasters plot(elev) ``` + We will reproject this raster into our target CRS and apply a mask defined by our study area. Then, we will also convert this `terra` object into a `data.frame` to help with our `ggplot`ting. @@ -227,12 +242,20 @@ elev_df <- elev_masked %>% ``` +With the default color scale, the elevation is difficult to distinguish across +the study area. + ```{r raster} elev_df %>% ggplot(aes(x,y,fill=elev)) + geom_raster() ``` +There is an option to apply transformations within the fill color scale, +`scale_fill_continuous`, e.g., the square-root transform which can increase the +visual contrast in the elevation raster. However, there are areas below sea level +in our study area so these pixels will become `NA` after the square-root transform. + ```{r raster_bad_transform} elev_df %>% ggplot(aes(x,y,fill=elev)) + @@ -240,6 +263,10 @@ elev_df %>% scale_fill_continuous(trans = 'sqrt') ``` +You can also manually modify your raster variable being plotted to perform similar +transformations, including more flexibility like an offset for our case to deal +with the below sea level values. The down-side is you would also have to manually +edit the legend to make it more interpretable. ```{r raster_manual_transform} elev_df %>% @@ -247,6 +274,11 @@ elev_df %>% geom_raster() ``` +Another option is to break up your fill color scale into bins. One option for +binning continous values in `ggplot2` is the `scale_*_fermenter` function that +relies on the `RColorBrewer` package. Here, we will use this option and set +manual breaks in our elevation. + ```{r raster_binned} ggplot() + geom_raster(aes(x,y,fill=elev), @@ -256,6 +288,8 @@ ggplot() + breaks = c(-50,0,50,250,500,750)) ``` +So here is the current state of our map with the elevation raster and the +study area and forest polygons: ```{r raster_with_vector} ggplot() + @@ -272,6 +306,10 @@ ggplot() + Now, we can add some nice map background layers and annotations. First, let's build a background to show the context of neighboring states and water. +We use the `USABoundaries` package again to create a US states background and +fill those states with a light green color (`#e3f7dc`). We also set the panel +background fill color with a light blue (`#afdbed`) since this represents the +water areas in the map. ```{r background} all_states <- us_states() %>% @@ -293,7 +331,8 @@ ggplot() + panel.grid.major = element_line(color = 'white', linewidth = 0.3)) ``` -Everything together, plus some annotations! +Now, let's combine all of our incremental steps into one plot. We can also add +some annotations like a north arrow, a scale bar, and some arrows. ```{r final}

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