Following my Bob Ross “Paint the landscape” metaphor, there are a few choice brushes I reach for when you ask me to paint a picture.
See also: data visualization & color
Source Files from, for example, 3DEP.
I’m fond of the wkp/country/wiki-scotland palette here.
surface <- terra::rast(file.path(path_to_basedata,"demo/HUC6_120901_dem.tif",fsep = .Platform$file.sep))
ggplot2::ggplot() +
tidyterra::geom_spatraster(data = surface) +
tidyterra::scale_fill_whitebox_c(name = "Elevation (m)") +
ggplot2::theme_void()<SpatRaster> resampled to 501380 cells.
Landscape processing to arrive at these: Terrain Analysis
surface <- terra::rast(file.path(path_to_basedata,"demo/HUC6_120901_dem.tif",fsep = .Platform$file.sep))
## DEM
ggplot2::ggplot() +
tidyterra::geom_spatraster(data = surface) +
tidyterra::scale_fill_whitebox_c(name = "Elevation (m)") +
ggplot2::theme_void()<SpatRaster> resampled to 501380 cells.
## SLOPE
# slope <- terra::terrain(surface, "slope", unit = "degrees")
# slope_pal <- RColorBrewer::brewer.pal(9, "YlGnBu")
# ggplot2::ggplot() +
# tidyterra::geom_spatraster(data = slope) +
# ggplot2::scale_fill_gradientn(colors = slope_pal, na.value = NA, name = "Slope (°)") +
# ggplot2::theme_void()
## Combined
slope <- terra::terrain(surface, "slope", unit="radians")
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=========================================
aspect <- terra::terrain(surface, "aspect", unit="radians")
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=========================================
hillshade <- terra::shade(slope, aspect, 40, 270)
|---------|---------|---------|---------|
=========================================
ggplot2::ggplot() +
tidyterra::geom_spatraster(data = hillshade) +
ggplot2::scale_fill_gradient(low = "black", high = "white", na.value = NA, name = "Hillshade") +
ggnewscale::new_scale_fill() +
tidyterra::geom_spatraster(data = surface, alpha = 0.5) +
tidyterra::scale_fill_whitebox_c(name = "Elevation") +
ggplot2::theme_void()<SpatRaster> resampled to 501380 cells.
<SpatRaster> resampled to 501380 cells.
```{r}
pkgset <- '102702'
DEM <- glue::glue("~/data/raw/FIM4/inputs/dems/3dep_dems/10m_5070/20250320/HUC6_{substr(pkgset, 1, 6)}_dem.tif") |> terra::rast()
slope_rad <- terra::terrain(DEM, "slope", unit = "radians")
slope_pct <- tan(slope_rad) * 100
m <- c(0, 5, 10,
5, 20, 20,
20, 40, 30,
40, max(terra::values(slope_pct),na.rm = T), 40)
rclmat <- matrix(m, ncol=3, byrow=TRUE)
slope_rcl <- terra::classify(slope_pct, rclmat, include.lowest=TRUE)
asp <- terra::terrain(DEM, v = "aspect", unit = "degrees")
m <- c(0,22.5,1,
22.5,67.5,2,
67.5,112.5,3,
112.5,157.5,4,
157.5,202.5,5,
202.5,247.5,6,
247.5,292.5,7,
292.5,337.5,8,
337.5,max(terra::values(asp),na.rm = T),1)
rclmat <- matrix(m, ncol=3, byrow=TRUE)
asp_rcl <- terra::classify(asp, rclmat, include.lowest=TRUE)
aspect_slope <- asp_rcl + slope_rcl
aspect_slope_colors <- c(
"11"="#E1E1E1", "12"="#E1E1E1", "13"="#E1E1E1", "14"="#E1E1E1",
"15"="#E1E1E1", "16"="#E1E1E1", "17"="#E1E1E1", "18"="#E1E1E1", "19"="#E1E1E1", "20"="#E1E1E1",
"21"="#ADC0A8", "22"="#ABD0D1", "23"="#B0C8D6", "24"="#B5BDD6", "25"="#B7B1C9", "26"="#C9B1B9", "27"="#D6B8B0", "28"="#D4C4AA",
"31"="#6DA368", "32"="#68B1B6", "33"="#74A1C2", "34"="#7F8DC2", "35"="#8674A6", "36"="#A67484", "37"="#C28674", "38"="#BBA168",
"41"="#24821E", "42"="#1D8E94", "43"="#317AA8", "44"="#435AA8", "45"="#543180", "46"="#80314C", "47"="#A84C31", "48"="#9E7A1D"
)
levels_df <- data.frame(ID = as.numeric(names(aspect_slope_colors)),
category = names(aspect_slope_colors))
# 2. Set the raster as categorical without copying the whole object
levels(aspect_slope) <- levels_df
# 2. Use as.factor() inside the aes() call to fix the discrete vs continuous error
ggplot2::ggplot() +
tidyterra::geom_spatraster(data = aspect_slope) +
ggplot2::scale_fill_manual(
values = aspect_slope_colors,
na.value = "transparent",
guide = "none"
) +
ggplot2::theme_void() +
ggplot2::coord_sf(expand = FALSE)
```
See FIM for more details.
|---------|---------|---------|---------|
=========================================
|---------|---------|---------|---------|
=========================================
<SpatRaster> resampled to 500916 cells.
Coordinate system already present.
ℹ Adding new coordinate system, which will replace the existing one.
```{r}
#// Setup
#///////////////////////////////////////////////////////////////////////////////////////////////////////
base_width_in <- 10
base_title_size_pt <- 14
base_subtitle_size_pt <- 10
base_general_text_size_pt <- 12
get_aspect_ratio <- function(plot) {
# Build the plot and extract the aspect ratio from the coordinate system
p_build <- ggplot2::ggplot_build(plot); aspect_ratio <- p_build$layout$coord$aspect(p_build$layout$panel_params[[1]])
# coord_sf returns NULL if the ratio is 1, so we'll default to 1
if (is.null(aspect_ratio)) { return(1) } else { return(aspect_ratio) }
}
#// Each round
#///////////////////////////////////////////////////////////////////////////////////////////////////////
# export_plot <- basemaps::basemap_ggplot(CONUS_ext,map_service = "osm", map_type = "streets") +
ggplot2::theme(text = ggplot2::element_text(family = "Helvetica", size = base_general_text_size_pt),
plot.title = ggplot2::element_text(size = base_title_size_pt, hjust = 0.5),
plot.subtitle = ggplot2::element_text(family = "mono", size = base_subtitle_size_pt, hjust = 0.5))
#// And Export
#///////////////////////////////////////////////////////
target_width_in <- 2 # I've designed these for a full page but want to stuff them into a 2 colum so 10 /
plot_aspect_ratio <- get_aspect_ratio(export_plot)
scale_factor <- target_width_in / base_width_in
target_height_in <- target_width_in / plot_aspect_ratio
ggplot2::ggsave(filename = "_osm_streets_bm230.jpg", plot =
export_plot +
ggplot2::theme(
text = ggplot2::element_text(size = base_general_text_size_pt * scale_factor),
plot.title = ggplot2::element_text(size = base_title_size_pt * scale_factor),
plot.subtitle = ggplot2::element_text(size = base_subtitle_size_pt * scale_factor)
)
,path = file.path(temp_path,"sizefuck",fsep = .Platform$file.sep),
width = target_width_in,height = target_height_in, units = "in", dpi=300)
knitr::plot_crop("./_bm_carto_voyage_ul.jpg")
#///////////////////////////////////////////////////////////////////////////////////////////////////////
``````{r}
#// Setup
#///////////////////////////////////////////////////////////////////////////////////////////////////////
base_width_px <- 1920
base_height_px <- 1080
base_title_size_pt <- 14
base_subtitle_size_pt <- 10
base_general_text_size_pt <- 12
get_aspect_ratio <- function(plot) {
# Build the plot and extract the aspect ratio from the coordinate system
p_build <- ggplot2::ggplot_build(plot)
aspect_ratio <- p_build$layout$coord$aspect(p_build$layout$panel_params[[1]])
# coord_sf returns NULL if the ratio is 1, so we'll default to 1
if (is.null(aspect_ratio)) { return(1) } else { return(aspect_ratio) }
}
#// Each round
#///////////////////////////////////////////////////////////////////////////////////////////////////////
# export_plot <- basemaps::basemap_ggplot(CONUS_ext,map_service = "carto", map_type = "voyager_labels_under") +
ggplot2::labs(title = "Carto - Voyager under labels", subtitle = 'basemaps::basemap_ggplot(CONUS_ext,map_service = "carto", map_type = "voyager_labels_under")') +
ggplot2::theme_void() +
ggplot2::theme(text = ggplot2::element_text(family = "Helvetica", size = base_general_text_size_pt),
plot.title = ggplot2::element_text(size = base_title_size_pt, hjust = 0.5),
plot.subtitle = ggplot2::element_text(family = "mono", size = base_subtitle_size_pt, hjust = 0.5))
#// And Export
#///////////////////////////////////////////////////////
target_width_px <- 1920 # This is the full screen width in pixels
plot_aspect_ratio <- get_aspect_ratio(export_plot)
scale_factor <- target_width_px / base_width_px
target_height_px <- target_width_px / plot_aspect_ratio
ggplot2::ggsave(filename = "_bm_carto_voyage_ul.jpg", plot =
export_plot +
ggplot2::theme(
text = ggplot2::element_text(size = base_general_text_size_pt * scale_factor),
plot.title = ggplot2::element_text(size = base_title_size_pt * scale_factor),
plot.subtitle = ggplot2::element_text(size = base_subtitle_size_pt * scale_factor)
)
,path = ".",
width = target_width_px, height = target_height_px, units = "px", dpi = 300)
knitr::plot_crop("./_bm_carto_voyage_ul.jpg")
#///////////////////////////////////////////////////////////////////////////////////////////////////////
```From FINNSTATS
```{r}
p +
ggplot2::theme(
panel.background = ggplot2::element_rect(fill='transparent'), #transparent panel bg
plot.background = ggplot2::element_rect(fill='transparent', color=NA), #transparent plot bg
panel.grid.major = ggplot2::element_blank(), #remove major gridlines
panel.grid.minor = ggplot2::element_blank(), #remove minor gridlines
legend.background = ggplot2::element_rect(fill='transparent'), #transparent legend bg
legend.box.background = ggplot2::element_rect(fill='transparent') #transparent legend panel
)
```Make sure to indicate that the backdrop should be translucent when exporting the plot with ggsave().
```{r}
ggplot2::ggsave('myplot.png', p, bg='transparent')
``````{r}
#// Image control setup
#///////////////////////////////////////////////////////////////////////////////////////////////////////
presentation_prepend <- '_testdeck'
base_width_px <- 1920
base_height_px <- 1080
base_title_size_pt <- 14
base_subtitle_size_pt <- 10
base_general_text_size_pt <- 12
get_aspect_ratio <- function(plot) {
# Build the plot and extract the aspect ratio from the coordinate system
p_build <- ggplot2::ggplot_build(plot)
aspect_ratio <- p_build$layout$coord$aspect(p_build$layout$panel_params[[1]])
# coord_sf returns NULL if the ratio is 1, so we'll default to 1
if (is.null(aspect_ratio)) { return(1) } else { return(aspect_ratio) }
}
#///////////////////////////////////////////////////////////////////////////////////////////////////////
``````{r}
#// And Export
#///////////////////////////////////////////////////////
filename <- paste0(presentation_prepend,"_stepimage.jpg")
target_width_px <- 1920
if(!file.exists(filename)) {
#//////////////////////////////////////////////////////////
existing_plot <- basemaps::basemap_ggplot(CONUS_ext,map_service = "carto", map_type = "voyager_labels_under") +
ggplot2::labs(title = "Carto - Voyager under labels", subtitle = 'basemaps::basemap_ggplot(CONUS_ext,map_service = "carto", map_type = "voyager_labels_under")') +
ggplot2::theme_void() +
ggplot2::theme(text = ggplot2::element_text(family = "Helvetica", size = base_general_text_size_pt),
plot.title = ggplot2::element_text(size = base_title_size_pt, hjust = 0.5),
plot.subtitle = ggplot2::element_text(family = "mono", size = base_subtitle_size_pt, hjust = 0.5))
#existing_plot
#/////////////////////////////////////////////////////////
plot_aspect_ratio <- get_aspect_ratio(existing_plot)
scale_factor <- target_width_px / base_width_px
target_height_px <- target_width_px / plot_aspect_ratio
ggplot2::ggsave(filename = filename, plot =
existing_plot +
ggplot2::theme(
text = ggplot2::element_text(size = base_general_text_size_pt * scale_factor),
plot.title = ggplot2::element_text(size = base_title_size_pt * scale_factor),
plot.subtitle = ggplot2::element_text(size = base_subtitle_size_pt * scale_factor)
)
,path = ".",
width = target_width_px, height = target_height_px, units = "px", dpi = 300)
knitr::plot_crop(filename)
}
#///////////////////////////////////////////////////////////////////////////////////////////////////////
```{.absolute left="7%" bottom="0%" height="90%"}
```{r}
unlink(file.path(output_folder_name,"SCHISM_based_FIM_domain_files"),recursive=TRUE)
unlink(file.path(output_folder_name,"SCHISM_based_FIM_domain.html"))
# m = leaflet(height=1000, width=1000) %>%
m = leaflet::leaflet(options = leafletOptions(preferCanvas = TRUE)) %>%
leaflet::addProviderTiles("OpenStreetMap",group = "OpenStreetMap") %>%
leaflet::addProviderTiles("Stamen.Toner",group = "Stamen.Toner") %>%
leaflet::addProviderTiles("Stamen.Terrain",group = "Stamen.Terrain") %>%
leaflet::addProviderTiles("Esri.WorldStreetMap",group = "Esri.WorldStreetMap") %>%
leaflet::addProviderTiles("Wikimedia",group = "Wikimedia") %>%
leaflet::addProviderTiles("CartoDB.Positron",group = "CartoDB.Positron") %>%
leaflet::addProviderTiles("Esri.WorldImagery",group = "Esri.WorldImagery") %>%
leafgl::addGlPoints(unprocessed_nodes,fillColor = 'red',group = "pts") %>%
leafgl::addGlPoints(processed_nodes,fillColor = 'green', group = "pts") %>%
leafem::addFeatures(ahulls,opacity = 1,fillOpacity = 0,weight = 2,color = 'black', group = "huc") %>%
# leafgl::addGlPolygons(ahulls,fillOpacity = 0,stroke = TRUE,color = 'black',popup="huc10", group = "huc") %>%
leaflet::addLegend("bottomright",colors = c("green","red"),
labels = c(paste0("SCHISM nodes: n=",processed_nodes_count), paste0("outside of huc bounds: n=",unprocessed_nodes_count)),
title = "SCHISM Domain (thinned)",opacity = 1) %>%
leaflet::addLayersControl(
baseGroups = c(
"OpenStreetMap", "Stamen.Toner",
"Stamen.Terrain", "Esri.WorldStreetMap",
"Wikimedia", "CartoDB.Positron", "Esri.WorldImagery"
),
position = "topleft",
overlayGroups = c("pts","huc"))
mapview::mapshot(m,file.path(output_folder_name,'SCHISM_based_FIM_domain.html'))
```If you’ve spent any time in this domain, you might find it becomes pretty easy to guess what tool was used to make a graph/map. Tools like matplotlib or ggplot are extensible but the styles tend to be pretty cookie-cutter. The following are my own cookie cutters that (should hopefully?) make slapping a map together using a standardized design language easier.
That’s exactly what COG is; it has overviews built in and acts as a tile cache. COG has grown wildly in the last few years. COG use range requests to extract part of a raster file out of cloud storage, this includes those small overviews that act as a tile cache. The above map uses a 1.5GB National Land Cover raster and the associated color table. Only overviews or small sections are used to draw the map layer depending on zoom and extent. The entire file is never downloaded. The initial load of the above map downloads < 1M of raster data.
https://tomjenkins.netlify.app/tutorials/r-leaflet-introduction/
https://rpubs.com/ials2un/dem_analysis
From the USGS map store.
https://nationalequityatlas.org/lab/get-started-data-visualization https://handsondataviz.org/ https://blog.mapbox.com/7-best-practices-for-mapping-a-pandemic-9f203576a132 https://rpubs.com/KingaHill/947744 https://environmentalinformatics-marburg.github.io/mapview/popups/html/popups.html https://flatgeobuf.org/examples/leaflet/filtered.html https://stackoverflow.com/questions/50111566/applying-leaflet-map-bounds-to-filter-data-within-shiny https://plugins.qgis.org/planet/user/29/tag/uncategorized/#:~:text=From%20the%20Print%20Layout%20toolbar,to%20a%20circular%20shape!).
Brutalist Cartography is a term of my own making which I use to describe the way I prepare the QGIS window for a screenshot. Even if you’ve mastered the toolset and have the eye for aesthetics, good cartography can take quite a while to assemble and present. That artistic flair is usually appreciated by all, but can slow the iteration process down quite substantially and is secondary to the main task of that map (for most of my use cases: see [[20240508224456]] Defining roles and goals]), which is to place a tractable example of a theoretical state of the data in front of someone else with the goal of exampling problems, placing extra geographic context on a piece of data, or simply adding a better looking (in my opinion) screenshot.
To accomplish the Brutalist cartography Aesthetic, one simply turns off most of the QGIS user interface while more prominently accentuation an organized legend, an overview window, and some of the more common map decoration items like the corner attribution image and the scale bar.
As a helpful note to future me, the panels that are typically turned on include:
Panels:
Toolbars:
Go to settings and turn font size to something larger like 24, close and reopen. The downside is that you have to close and reopen to change this back again, and unfortunately this makes many of the menus unusable since the monitor resolution remains fixed.
Although we could spend the day dissecting all the minute cartographic and stylistic errors the authors may have made in there initial map, the larger issue is the disingenuous presentation of the data. The map in the initial assessment, shown below and recreated for demonstration purposes, was the map provided for the impact assessment.
The assessment makes it appear that only 1 census block of predominantly minority populations are affected, when looking at the breakdown of impacted population as a total of the study domain we see those are far closer to a 50/50 distribution.