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fixed crs issues with sf and final draft of inset map for urbanrural

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Jeremy Kidwell 2019-02-28 10:53:30 +00:00
parent 756489ac15
commit de4a4afb51
1 changed files with 31 additions and 43 deletions
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mapping_draft.Rmd
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@ -64,6 +64,7 @@ require(rgeos) # deprecated by sf()
require(maptools) require(maptools)
require(ggplot2) require(ggplot2)
require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots
require(tmaptools) # for get_asp_ratio below
require(grid) # using for inset maps on tmap require(grid) # using for inset maps on tmap
require(broom) # required for tidying SPDF to data.frame for ggplot2 require(broom) # required for tidying SPDF to data.frame for ggplot2
require(tidyr) # using for grouped bar plot require(tidyr) # using for grouped bar plot
@ -119,8 +120,8 @@ ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#")
# unnecessary with advent of sf (above) # unnecessary with advent of sf (above)
coordinates(ecs) <- c("X", "Y") coordinates(ecs) <- c("X", "Y")
# Modified to use EPSG code directly 27 Feb 2019 # Modified to use EPSG code directly 27 Feb 2019
proj4string(ecs) = CRS("+init=epsg:27700") proj4string(ecs) <- bng
ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=27700) ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700))
``` ```
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] 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]
@ -174,7 +175,7 @@ download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebu
unzip("data/Scotland_ca_2010.zip", exdir = "data") unzip("data/Scotland_ca_2010.zip", exdir = "data")
} }
admin_lev1 <- readOGR("./data", "scotland_ca_2010") admin_lev1 <- readOGR("./data", "scotland_ca_2010")
admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(27700) admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(paste0("+init=epsg:",27700))
# read in polygon for intermediate admin boundary layers # read in polygon for intermediate admin boundary layers
if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) { if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) {
@ -183,11 +184,11 @@ download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/
unzip("data/Scotland_parlcon_2011.zip", exdir = "data") unzip("data/Scotland_parlcon_2011.zip", exdir = "data")
} }
admin_lev2 <- readOGR("./data", "scotland_parlcon_2011") admin_lev2 <- readOGR("./data", "scotland_parlcon_2011")
admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(27700) admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(paste0("+init=epsg:",27700))
# Set CRS using epsg code on spdf for symmetry with datasets below # Set CRS using epsg code on spdf for symmetry with datasets below
proj4string(admin_lev1) <- CRS("+init=epsg:27700") proj4string(admin_lev1) <- bng
proj4string(admin_lev2) <- CRS("+init=epsg:27700") proj4string(admin_lev2) <- bng
# Generate new sf shape using bounding box for central belt for map insets below # Generate new sf shape using bounding box for central belt for map insets below
# Note: coordinates use BNG as CRS (EPSG: 27700) # Note: coordinates use BNG as CRS (EPSG: 27700)
@ -197,14 +198,13 @@ scotland <- st_bbox(c(xmin = 5513.0000, xmax = 470332.0000,
crs = st_crs("+init=epsg:27700")) %>% crs = st_crs("+init=epsg:27700")) %>%
st_as_sfc() st_as_sfc()
centralbelt_region <- st_bbox(c(xmin = 224479.2, xmax = 642963.5, centralbelt_region <- st_bbox(c(xmin = 234841, xmax = 346309,
ymin = 347475.0, ymax = 711014.5), ymin = 653542, ymax = 686722),
crs = st_crs("+init=epsg:27700")) %>% crs = st_crs("+init=epsg:27700")) %>%
st_as_sfc() st_as_sfc()
st_crs(centralbelt_region) <- st_transform(centralbelt_region, 27700)
centralbelt_ratio <- get_asp_ratio(centralbelt_region) centralbelt_ratio <- get_asp_ratio(centralbelt_region)
scotland_ratio<- get_asp_ratio(scotland)
``` ```
```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE} ```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE}
@ -485,7 +485,7 @@ tm_shape(admin_lev2) +
tm_borders(alpha=.5, lwd=0.1) + tm_borders(alpha=.5, lwd=0.1) +
tm_shape(admin_lev1) + tm_shape(admin_lev1) +
tm_borders(lwd=0.6) + tm_borders(lwd=0.6) +
tm_shape(ecs_sf_centralbelt) + tm_shape(ecs_sf) +
tm_dots("red", size = .02, alpha = .2) + tm_dots("red", size = .02, alpha = .2) +
tm_scale_bar(position = c("right", "bottom")) + tm_scale_bar(position = c("right", "bottom")) +
tm_style("gray") + tm_style("gray") +
@ -619,9 +619,9 @@ unzip("data/SG_UrbanRural_2016.zip", exdir = "data")
} }
# Todo: remove sp datasets when sf revisions are complete. Currently running in parallel # Todo: remove sp datasets when sf revisions are complete. Currently running in parallel
urbanrural <- readOGR("./data", "SG_UrbanRural_2016") urbanrural <- readOGR("./data", "SG_UrbanRural_2016")
proj4string(urbanrural) <- CRS("+init=epsg:27700") proj4string(urbanrural) <- bng
urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(27700) urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(paste0("+init=epsg:",27700))
urbanrural_sf_simplified <- st_simplify(urbanrural_sf) %>% st_transform(27700) urbanrural_sf_simplified <- st_simplify(urbanrural_sf)
# TODO: worth considering uploading data to zenodo for long-term reproducibility as ScotGov shuffles this stuff around periodically breaking URLs # TODO: worth considering uploading data to zenodo for long-term reproducibility as ScotGov shuffles this stuff around periodically breaking URLs
# This code will generate a table of frequencies for each spatialpointsdataframe in urbanrural # This code will generate a table of frequencies for each spatialpointsdataframe in urbanrural
@ -678,13 +678,8 @@ ggplot(urbanrural_gathered,
```{r create_urbanrural_ecs_chart_choropleth, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 8"} ```{r create_urbanrural_ecs_chart_choropleth, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 8"}
# Using tmap as an alternative to ggplot and base R graphics
# TODO: Clip shapes to buildings shapefile (use OSM or OS?), using st_difference # TODO: Clip shapes to buildings shapefile (use OSM or OS?), using st_difference
# TODO: Double check data licenses for tm_credits # TODO: Double check data licenses for tm_credits
# 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
# Generate static plot for printing
# Generate code for inset map of central belt # Generate code for inset map of central belt
# First build large plot using National level view # First build large plot using National level view
@ -708,48 +703,41 @@ urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_sf_simplified) +
title.size = .7, title.size = .7,
legend.title.size = .7, legend.title.size = .7,
# values are bottom, left, top, right, modified here to make space for inset # values are bottom, left, top, right, modified here to make space for inset
inner.margins = c(0.5, 0.1, 0.05, 0.05) inner.margins = c(0.1, 0.1, 0.05, 0.05),
outer.margins = c(0.2, 0.01, 0.01, 0.01)
) )
# Next build smaller central belt plot for inset: # Next build smaller central belt plot for inset:
# TODO: fix issue here with mismatching CRS cf stackexchange
urbanrural_sf_simplified_centralbelt <- st_crop(urbanrural_sf_simplified, centralbelt_region) urbanrural_sf_simplified_centralbelt <- st_crop(urbanrural_sf_simplified, centralbelt_region)
st_crs(urbanrural_sf_simplified)
st_crs(urbanrural_sf)
proj4string(urbanrural)
st_crs(centralbelt_region)
urbanrural_centralbelt_ecs_choropleth_plot <- urbanrural_centralbelt_ecs_choropleth_plot <-
tm_shape(urbanrural_sf_simplified_centralbelt) + tm_shape(urbanrural_sf_simplified_centralbelt) +
tm_polygons(col = "UR8FOLD", palette = "BrBG") + tm_polygons(col = "UR8FOLD", palette = "BrBG") +
tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) +
tm_legend(show=FALSE)
# Stitch together maps using grid() # Stitch together maps using grid()
# Note: viewport values are X, Y, width and height # Note: viewport values are X, Y, width and height
print(urbanrural_centralbelt_ecs_choropleth_plot, vp = viewport(0.8, 0.27, width = 0.5, height = 0.5))
# Test by saving to file # todo: extrploate and use scale of inset bbox (centralbelt_ratio) to configure dimensions above for inset plots
vp_urbanrural_centralbelt_ecs_choropleth_plot <- viewport(x = 1.5, y = 0.15, width = 5.5, height = 1.5)
tmap_mode("plot") vp_urbanrural_centralbelt_ecs_choropleth_plot <- viewport(x = 0.5, y = 0.1, height = 6.0/centralbelt_ratio)
urbanrural_uk_ecs_choropleth_plot
print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
# output inset map separately first for testing # Save to file
save_tmap(urbanrural_centralbelt_ecs_choropleth_plot, "urbanrural_test.png", scale = 1, width = 6.125)
# 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
# centralbelt_ratio
save_tmap(urbanrural_uk_ecs_choropleth_plot, "urbanrural_test.png", scale = 0.7, width = 6.125, save_tmap(urbanrural_uk_ecs_choropleth_plot, "urbanrural_test.png", scale = 0.7, width = 6.125,
insets_tm = urbanrural_centralbelt_ecs_choropleth_plot, insets_tm = urbanrural_centralbelt_ecs_choropleth_plot,
insets_vp = vp_urbanrural_centralbelt_ecs_choropleth_plot) insets_vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
# plot full map with inset
tmap_mode("plot")
urbanrural_uk_ecs_choropleth_plot
print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt_ecs_choropleth_plot)
# Generate dynamic plot for exploring # Generate dynamic plot for exploring
# TODO: change basemap # TODO: change basemap
@ -902,7 +890,7 @@ if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) {
unzip("data/SSSI_SCOTLAND_ESRI.zip", exdir = "data") unzip("data/SSSI_SCOTLAND_ESRI.zip", exdir = "data")
} }
sssi <- st_read("data/SSSI_SCOTLAND.shp") %>% st_transform(27700) sssi <- st_read("data/SSSI_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700))
sssi_sp <- readOGR("./data", "SSSI_SCOTLAND") sssi_sp <- readOGR("./data", "SSSI_SCOTLAND")
# Generate simplified polygon for plots below # Generate simplified polygon for plots below
sssi_simplified <- st_simplify(sssi) sssi_simplified <- st_simplify(sssi)
@ -918,7 +906,7 @@ if (file.exists("data/WILDLAND_SCOTLAND.shp") == FALSE) {
unzip("data/WILDLAND_SCOTLAND_ESRI.zip", exdir = "data") unzip("data/WILDLAND_SCOTLAND_ESRI.zip", exdir = "data")
} }
wildland <- st_read("data/WILDLAND_SCOTLAND.shp") %>% st_transform(27700) wildland <- st_read("data/WILDLAND_SCOTLAND.shp") %>% st_transform(paste0("+init=epsg:",27700))
wildland_sp <- readOGR("./data", "WILDLAND_SCOTLAND") wildland_sp <- readOGR("./data", "WILDLAND_SCOTLAND")
# Generate simplified polygon for plots below # Generate simplified polygon for plots below
wildland_simplified <- st_simplify(wildland) wildland_simplified <- st_simplify(wildland)
@ -934,7 +922,7 @@ download.file("https://opendata.arcgis.com/datasets/3cb1abc185a247a48b9d53e4c4a8
unzip("data/National_Forest_Inventory_Woodland_Scotland_2017.zip", exdir = "data") unzip("data/National_Forest_Inventory_Woodland_Scotland_2017.zip", exdir = "data")
} }
forestinv <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") %>% st_transform(27700) forestinv <- st_read("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") %>% st_transform(paste0("+init=epsg:",27700))
forestinv_sp <- readOGR("./data", "National_Forest_Inventory_Woodland_Scotland_2017") forestinv_sp <- readOGR("./data", "National_Forest_Inventory_Woodland_Scotland_2017")
# Generate simplified polygon for plots below # Generate simplified polygon for plots below
forestinv_simplified <- st_simplify(forestinv) forestinv_simplified <- st_simplify(forestinv)