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https://github.com/kidwellj/mapping_environmental_action.git
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fixed crs issues with sf and final draft of inset map for urbanrural
This commit is contained in:
Jeremy Kidwell
parent
756489ac15
commit
de4a4afb51
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@ -64,6 +64,7 @@ require(rgeos) # deprecated by sf()
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require(maptools)
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require(ggplot2)
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require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots
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require(tmaptools) # for get_asp_ratio below
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require(grid) # using for inset maps on tmap
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require(broom) # required for tidying SPDF to data.frame for ggplot2
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require(tidyr) # using for grouped bar plot
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@ -119,8 +120,8 @@ ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#")
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# unnecessary with advent of sf (above)
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coordinates(ecs) <- c("X", "Y")
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# Modified to use EPSG code directly 27 Feb 2019
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proj4string(ecs) = CRS("+init=epsg:27700")
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ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=27700)
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proj4string(ecs) <- bng
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ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700))
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```
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There are `r length(ecs)` eco-congregations in Scotland. By some measurements, particularly in terms of individual sites and possibly also with regards to volunteers, this makes Eco-Congregation Scotland one of the largest environmental third-sector groups in Scotland.[^159141043]
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@ -174,7 +175,7 @@ download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebu
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unzip("data/Scotland_ca_2010.zip", exdir = "data")
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}
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admin_lev1 <- readOGR("./data", "scotland_ca_2010")
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admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(27700)
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admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(paste0("+init=epsg:",27700))
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# read in polygon for intermediate admin boundary layers
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if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) {
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@ -183,11 +184,11 @@ download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/
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unzip("data/Scotland_parlcon_2011.zip", exdir = "data")
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}
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admin_lev2 <- readOGR("./data", "scotland_parlcon_2011")
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admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(27700)
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admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(paste0("+init=epsg:",27700))
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# Set CRS using epsg code on spdf for symmetry with datasets below
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proj4string(admin_lev1) <- CRS("+init=epsg:27700")
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proj4string(admin_lev2) <- CRS("+init=epsg:27700")
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proj4string(admin_lev1) <- bng
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proj4string(admin_lev2) <- bng
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# Generate new sf shape using bounding box for central belt for map insets below
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# Note: coordinates use BNG as CRS (EPSG: 27700)
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@ -197,14 +198,13 @@ scotland <- st_bbox(c(xmin = 5513.0000, xmax = 470332.0000,
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crs = st_crs("+init=epsg:27700")) %>%
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st_as_sfc()
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centralbelt_region <- st_bbox(c(xmin = 224479.2, xmax = 642963.5,
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ymin = 347475.0, ymax = 711014.5),
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centralbelt_region <- st_bbox(c(xmin = 234841, xmax = 346309,
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ymin = 653542, ymax = 686722),
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crs = st_crs("+init=epsg:27700")) %>%
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st_as_sfc()
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st_crs(centralbelt_region) <- st_transform(centralbelt_region, 27700)
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centralbelt_ratio <- get_asp_ratio(centralbelt_region)
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scotland_ratio<- get_asp_ratio(scotland)
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```
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```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE}
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@ -485,7 +485,7 @@ tm_shape(admin_lev2) +
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tm_borders(alpha=.5, lwd=0.1) +
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tm_shape(admin_lev1) +
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tm_borders(lwd=0.6) +
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tm_shape(ecs_sf_centralbelt) +
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tm_shape(ecs_sf) +
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tm_dots("red", size = .02, alpha = .2) +
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tm_scale_bar(position = c("right", "bottom")) +
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tm_style("gray") +
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@ -619,9 +619,9 @@ unzip("data/SG_UrbanRural_2016.zip", exdir = "data")
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}
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# Todo: remove sp datasets when sf revisions are complete. Currently running in parallel
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urbanrural <- readOGR("./data", "SG_UrbanRural_2016")
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proj4string(urbanrural) <- CRS("+init=epsg:27700")
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urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(27700)
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urbanrural_sf_simplified <- st_simplify(urbanrural_sf) %>% st_transform(27700)
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proj4string(urbanrural) <- bng
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urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(paste0("+init=epsg:",27700))
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urbanrural_sf_simplified <- st_simplify(urbanrural_sf)
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# TODO: worth considering uploading data to zenodo for long-term reproducibility as ScotGov shuffles this stuff around periodically breaking URLs
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# This code will generate a table of frequencies for each spatialpointsdataframe in urbanrural
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@ -678,13 +678,8 @@ ggplot(urbanrural_gathered,
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```{r create_urbanrural_ecs_chart_choropleth, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 8"}
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# Using tmap as an alternative to ggplot and base R graphics
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# TODO: Clip shapes to buildings shapefile (use OSM or OS?), using st_difference
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# TODO: Double check data licenses for tm_credits
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# TODO: Add inset with zoom-in for central belt to bottom of map; cf. https://github.com/mtennekes/tmap/tree/master/demo/USChoropleth and https://geocompr.robinlovelace.net/adv-map.html section 8.2.7
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# Generate static plot for printing
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# Generate code for inset map of central belt
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# First build large plot using National level view
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@ -708,48 +703,41 @@ urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_sf_simplified) +
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title.size = .7,
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legend.title.size = .7,
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# values are bottom, left, top, right, modified here to make space for inset
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inner.margins = c(0.5, 0.1, 0.05, 0.05)
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inner.margins = c(0.1, 0.1, 0.05, 0.05),
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outer.margins = c(0.2, 0.01, 0.01, 0.01)
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)
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# Next build smaller central belt plot for inset:
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# TODO: fix issue here with mismatching CRS cf stackexchange
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urbanrural_sf_simplified_centralbelt <- st_crop(urbanrural_sf_simplified, centralbelt_region)
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st_crs(urbanrural_sf_simplified)
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st_crs(urbanrural_sf)
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proj4string(urbanrural)
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st_crs(centralbelt_region)
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urbanrural_centralbelt_ecs_choropleth_plot <-
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tm_shape(urbanrural_sf_simplified_centralbelt) +
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tm_polygons(col = "UR8FOLD", palette = "BrBG") +
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tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4)
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tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) +
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tm_legend(show=FALSE)
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# Stitch together maps using grid()
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# Note: viewport values are X, Y, width and height
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print(urbanrural_centralbelt_ecs_choropleth_plot, vp = viewport(0.8, 0.27, width = 0.5, height = 0.5))
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# Test by saving to file
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vp_urbanrural_centralbelt_ecs_choropleth_plot <- viewport(x = 1.5, y = 0.15, width = 5.5, height = 1.5)
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# todo: extrploate and use scale of inset bbox (centralbelt_ratio) to configure dimensions above for inset plots
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tmap_mode("plot")
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urbanrural_uk_ecs_choropleth_plot
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print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
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vp_urbanrural_centralbelt_ecs_choropleth_plot <- viewport(x = 0.5, y = 0.1, height = 6.0/centralbelt_ratio)
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# output inset map separately first for testing
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save_tmap(urbanrural_centralbelt_ecs_choropleth_plot, "urbanrural_test.png", scale = 1, width = 6.125)
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# todo: refine bounding box, implement clipping on inset map for polygon layers, calc scale of inset map, extrploate and use to configure dimensions above for inset plots
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# centralbelt_ratio
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# Save to file
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save_tmap(urbanrural_uk_ecs_choropleth_plot, "urbanrural_test.png", scale = 0.7, width = 6.125,
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insets_tm = urbanrural_centralbelt_ecs_choropleth_plot,
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insets_vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
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# plot full map with inset
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tmap_mode("plot")
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urbanrural_uk_ecs_choropleth_plot
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print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
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# Generate dynamic plot for exploring
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# TODO: change basemap
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@ -902,7 +890,7 @@ if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) {
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unzip("data/SSSI_SCOTLAND_ESRI.zip", exdir = "data")
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}
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sssi <- st_read("data/SSSI_SCOTLAND.shp") %>% st_transform(27700)
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sssi <- st_read("data/SSSI_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700))
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sssi_sp <- readOGR("./data", "SSSI_SCOTLAND")
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# Generate simplified polygon for plots below
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sssi_simplified <- st_simplify(sssi)
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@ -918,7 +906,7 @@ if (file.exists("data/WILDLAND_SCOTLAND.shp") == FALSE) {
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unzip("data/WILDLAND_SCOTLAND_ESRI.zip", exdir = "data")
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}
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wildland <- st_read("data/WILDLAND_SCOTLAND.shp") %>% st_transform(27700)
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wildland <- st_read("data/WILDLAND_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700))
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wildland_sp <- readOGR("./data", "WILDLAND_SCOTLAND")
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# Generate simplified polygon for plots below
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wildland_simplified <- st_simplify(wildland)
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@ -934,7 +922,7 @@ download.file("https://opendata.arcgis.com/datasets/3cb1abc185a247a48b9d53e4c4a8
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unzip("data/National_Forest_Inventory_Woodland_Scotland_2017.zip", exdir = "data")
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}
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forestinv <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") %>% st_transform(27700)
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forestinv <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") %>% st_transform(paste0("+init=epsg:",27700))
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forestinv_sp <- readOGR("./data", "National_Forest_Inventory_Woodland_Scotland_2017")
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# Generate simplified polygon for plots below
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forestinv_simplified <- st_simplify(forestinv)
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