diff --git a/mapping_draft.Rmd b/mapping_draft.Rmd index 3580917..3518003 100644 --- a/mapping_draft.Rmd +++ b/mapping_draft.Rmd @@ -64,7 +64,7 @@ require(rgeos) # deprecated by sf() require(maptools) require(ggplot2) require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots -# require(ggmap) +require(grid) # using for inset maps on tmap require(broom) # required for tidying SPDF to data.frame for ggplot2 require(tidyr) # using for grouped bar plot require(plyr) @@ -93,16 +93,9 @@ if (dir.exists("derivedData") == FALSE) { # it is falling out of use in many cases, so will be defaulting to WGS84 in future # data-sets and papers. -# TODO: make canonical CRS definitions and use consistently; remove proj4string(admin_lev1) and other similar instances below. -wgs84 <- "+proj=longlat +datum=WGS84" -bng_old <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs +ellps=airy +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894" -bng <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs" -osgb36 <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs +ellps=airy +towgs84=446.448,-125.157,542.060,0.1502,0.2470,0.8421,-20.4894" - -# Note, shifting to EPSG codes given the usage of this approach with sf() -bng_epsg <- CRS("+init=epsg:27700") -osgb36 <- CRS("+init=epsg:7405") -wgs84_epsg <- CRS("+init=epsg:4326") +# Working with EPSG codes for spatialfeature CRS given the usage of this approach with sf() +bng <- CRS("+init=epsg:27700") +wgs84 <- CRS("+init=epsg:4326") # Configure fonts for plots below @@ -123,10 +116,11 @@ Until recently, environmentalism has been treated by governments and environment # ...turn it into a SpatialPointsDataFrame--------------------- # TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository, i.e. ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#") -ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=27700) # unnecessary with advent of sf (above) coordinates(ecs) <- c("X", "Y") -proj4string(ecs) = CRS(bng) +# Modified to use EPSG code directly 27 Feb 2019 +proj4string(ecs) = CRS("+init=epsg:27700") +ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=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] @@ -179,8 +173,8 @@ download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebu destfile = "data/Scotland_ca_2010.zip") unzip("data/Scotland_ca_2010.zip", exdir = "data") } -admin_lev1 <- readOGR("./data", "scotland_ca_2010") -admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") +admin_lev1 <- readOGR("./data", "scotland_ca_2010") +admin_lev1_sf <- st_read("data/scotland_ca_2010.shp") %>% st_transform(27700) # read in polygon for intermediate admin boundary layers if (file.exists("data/scotland_parlcon_2011.shp") == FALSE) { @@ -189,14 +183,33 @@ download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/ unzip("data/Scotland_parlcon_2011.zip", exdir = "data") } admin_lev2 <- readOGR("./data", "scotland_parlcon_2011") -admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") +admin_lev2_sf <- st_read("data/scotland_parlcon_2011.shp") %>% st_transform(27700) + +# Set CRS using epsg code on spdf for symmetry with datasets below +proj4string(admin_lev1) <- CRS("+init=epsg:27700") +proj4string(admin_lev2) <- CRS("+init=epsg:27700") + +# Generate new sf shape using bounding box for central belt for map insets below +# Note: coordinates use BNG as CRS (EPSG: 27700) + +scotland <- st_bbox(c(xmin = 5513.0000, xmax = 470332.0000, + ymin = 530249.0000, ymax = 1220301.5000), + crs = st_crs("+init=epsg:27700")) %>% + st_as_sfc() + +centralbelt_region <- st_bbox(c(xmin = 224479.2, xmax = 642963.5, + ymin = 347475.0, ymax = 711014.5), + crs = st_crs("+init=epsg:27700")) %>% + st_as_sfc() +st_crs(centralbelt_region) <- st_transform(centralbelt_region, 27700) + ``` ```{r import_groups_data, message=FALSE, warning=FALSE, include=FALSE} # read in Transition Towns data and turn it into a SpatialPointsDataFrame transition_wgs <- read.csv(text=getURL("https://zenodo.org/record/165519/files/SCCAN_1.4.csv")) coordinates(transition_wgs) <- c("X", "Y") -proj4string(transition_wgs) <- CRS(wgs84) +proj4string(transition_wgs) <- CRS("+init=epsg:4326") transition <- spTransform(transition_wgs, bng) transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700) @@ -215,20 +228,28 @@ transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700) pow_pointX <- read.csv("./data/poi_2015_12_scot06340459.csv", sep="|") coordinates(pow_pointX) <- c("feature_easting", "feature_northing") # TODO: need to alter to draw from wgs84 or bng defined in preamble above -proj4string(pow_pointX) <- proj4string(admin_lev1) +proj4string(pow_pointX) <- CRS("+init=epsg:27700") pow_pointX_sf <- st_as_sf(pow_pointX, coords = c("X", "Y"), crs=27700) # read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame dtas <- read.csv("data/community-dev-trusts-2.6.csv") coordinates(dtas) <- c("X", "Y") -proj4string(dtas) <- proj4string(admin_lev1) +proj4string(dtas) <- CRS("+init=epsg:27700") dtas_sf <- st_as_sf(dtas, coords = c("X", "Y"), crs=27700) # read in permaculture data and turn it into a SpatialPointsDataFrame permaculture <- read.csv("data/permaculture_scot-0.8.csv") coordinates(permaculture) <- c("X", "Y") -proj4string(permaculture) <- proj4string(admin_lev1) +proj4string(permaculture) <- CRS("+init=epsg:27700") permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700) + +# subset point datasets for inset maps below +ecs_sf_centralbelt <- st_intersection(ecs_sf, centralbelt_region) +dtas_sf_centralbelt <- st_intersection(dtas_sf, centralbelt_region) +transition_sf_centralbelt <- st_intersection(transition_sf, centralbelt_region) +pow_sf_centralbelt <- st_intersection(pow_pointX_sf, centralbelt_region) +permaculture_sf_centralbelt <- st_intersection(permaculture_sf, centralbelt_region) + ``` ```{r process_admin_data} @@ -240,8 +261,9 @@ permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700) # calculate count of ECS for fields in admin and provide percentages # JK Note: need to convert from poly.counts, which uses sp() to st_covers() which uses sf() - cf. https://stackoverflow.com/questions/45314094/equivalent-of-poly-counts-to-count-lat-long-pairs-falling-inside-of-polygons-w#45337050 -ecs <- spTransform(ecs, proj4string(admin_lev1)) -transition <- spTransform(transition, proj4string(admin_lev1)) +# Commenting out 27 Feb 2019 in light of revisions to CRS handling above +# ecs <- spTransform(ecs, proj4string(admin_lev1)) +# transition <- spTransform(transition, proj4string(admin_lev1)) admin_lev1$ecs_count <- poly.counts(ecs,admin_lev1) admin_lev1$ecs_percent<- prop.table(admin_lev1$ecs_count) @@ -331,7 +353,7 @@ Though there are too few eco-congregations and transition groups for a numerical # TODO: clip choropleth polygons to buildings shapefile (possble superceded by pverlay on lev2) # Draw initial choropleth map of ECS concentration (using tmap and sf below by default) - +# Revising re: CRS inset maps complete to here tm_shape(admin_lev2) + tm_fill(col = "ecs_count", palette = "Oranges", title = "Concentration of ECS groups") + tm_borders(alpha=.5, lwd=0.1) + @@ -461,7 +483,7 @@ tm_shape(admin_lev2) + tm_borders(alpha=.5, lwd=0.1) + tm_shape(admin_lev1) + tm_borders(lwd=0.6) + - tm_shape(ecs_sf) + + tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .02, alpha = .2) + tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + @@ -595,8 +617,9 @@ unzip("data/SG_UrbanRural_2016.zip", exdir = "data") } # Todo: remove sp datasets when sf revisions are complete. Currently running in parallel urbanrural <- readOGR("./data", "SG_UrbanRural_2016") -urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") -urbanrural_sf_simplified <- st_simplify(urbanrural_sf) +proj4string(urbanrural) <- CRS("+init=epsg:27700") +urbanrural_sf <- st_read("data/SG_UrbanRural_2016.shp") %>% st_transform(27700) +urbanrural_sf_simplified <- st_simplify(urbanrural_sf) %>% st_transform(27700) # 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 @@ -661,7 +684,10 @@ ggplot(urbanrural_gathered, # Generate static plot for printing -tm_shape(urbanrural_sf_simplified) + +# Generate code for inset map of central belt +# First build large plot using National level view + +urbanrural_uk_ecs_choropleth_plot <- tm_shape(urbanrural_sf_simplified) + tm_polygons(col = "UR8FOLD", palette = "BrBG", lwd=0.001, n=9, title = "UrbanRural 8 Fold Scale") + tm_shape(ecs_sf) + @@ -678,29 +704,39 @@ tm_shape(urbanrural_sf_simplified) + frame = FALSE, title.size = .7, legend.title.size = .7, - inner.margins = c(0.1, 0.1, 0.05, 0.05) + # values are bottom, left, top, right, modified here to make space for inset + inner.margins = c(0.5, 0.1, 0.05, 0.05) ) -# Generate new sf shape using bounding box and subset of urbanrural for central belt inset below -# Note: coordinates use BNG as CRS (EPSG: 27700) +# Next build smaller central belt plot for inset: -centralbelt_region = st_bbox(c(xmin = 224479.2, xmax = 642963.5, - ymin = 347475.0, ymax = 711014.5), - crs = st_crs(urbanrural_sf)) %>% - st_as_sfc() +urbanrural_sf_simplified_centralbelt <- st_crop(urbanrural_sf_simplified, centralbelt_region) -# Generate code for inset map of central belt +st_crs(urbanrural_sf_simplified) +st_crs(urbanrural_sf) +proj4string(urbanrural) +st_crs(centralbelt_region) + +urbanrural_centralbelt_ecs_choropleth_plot <- + tm_shape(urbanrural_sf_simplified_centralbelt) + + tm_polygons(col = "UR8FOLD", palette = "BrBG") + + tm_shape(ecs_sf_centralbelt) + tm_dots("red", size = .05, alpha = .4) -# centralbelt_map = tm_shape(centralbelt_region) + tm_polygons() + -# tm_shape(st_within(ecs_sf, centralbelt_region)) + -# tm_dots("red", size = .05, alpha = .4) + -# tm_shape(nz_region) + tm_borders(lwd = 3) -# Still need to mod above line # Stitch together maps using grid() -# library(grid) -# nz_height_map -# print(nz_map, vp = viewport(0.8, 0.27, width = 0.5, height = 0.5)) +# 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 +vp_urbanrural_centralbelt_ecs_choropleth_plot <- viewport(x = 1.5, y = 0.15, width = 5.5, height = 1.5) + +tmap_mode("plot") +urbanrural_uk_ecs_choropleth_plot +print(urbanrural_centralbelt_ecs_choropleth_plot, vp = vp_urbanrural_centralbelt_ecs_choropleth_plot) + +save_tmap(urbanrural_uk_ecs_choropleth_plot, "urbanrural_test.png", scale = 0.7, width = 6.125, + insets_tm = urbanrural_centralbelt_ecs_choropleth_plot, + insets_vp = vp_urbanrural_centralbelt_ecs_choropleth_plot) # Generate dynamic plot for exploring # TODO: change basemap @@ -727,6 +763,7 @@ unzip("data/simd2016_withgeog.zip", exdir = "data", junkpaths = TRUE) simd_shapes <- readOGR("./data", "sc_dz_11") simd_indicators <- read.csv("./data/simd2016_withinds.csv") simd_indicators_min <- simd_indicators[c(1,6:17)] +# Original data is in wgs, so need to convert from wgs to bng to work with analysis across data sets simd_wgs <- merge(x=simd_shapes, y=simd_indicators_min, by.x = "DataZone", by.y = "Data_Zone") simd <- spTransform(simd_wgs, bng) @@ -901,13 +938,12 @@ forestinv_simplified <- st_simplify(forestinv) # scenicareas_sp <- readOGR("./data", "ScenicAreas") # Set symmetrical CRS for analysis below (inserted here in order to correct errors, may be deprecated later) -st_crs(sssi) <- 27700 -st_crs(ecs_sf) <- 27700 -st_crs(pow_pointX_sf) <- 27700 -st_crs(dtas_sf) <- 27700 -st_crs(transition_sf) <- 27700 -st_crs(permaculture_sf) <- 27700 - +# st_crs(sssi) <- 27700 +# st_crs(ecs_sf) <- 27700 +# st_crs(pow_pointX_sf) <- 27700 +# st_crs(dtas_sf) <- 27700 +# st_crs(transition_sf) <- 27700 +# st_crs(permaculture_sf) <- 27700 # Define buffer and measure number of ECS groups within 0.5 miles of all SSSI # CRS uses meters for units, so a buffer for 0.5 miles would use 805 meters) @@ -1125,18 +1161,19 @@ write.csv(forestinv_counts_merged, "derivedData/forestinv_counts_merged.csv", ro ```{r sssi_ecs_buffer_plot, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 11"} # Plot SSSI polygons (showing buffers) with ECS points (coloured by location) +# TODO set bounding box to clip all polygons (or identify offending layer) -tm_shape(sssi_simplified) + +tm_shape(sssi_simplified, bbox = scotland) + tm_fill(col = "green", alpha = 0.3, lwd=0.001, title = "Sites of Special Scientific Interest and ECS Groups") + # tm_shape(sssi_buf50) + tm_borders(lwd=0.001) + # tm_shape(sssi_buf500) + tm_borders(lwd=0.001) - tm_shape(admin_lev1_sf) + tm_borders(lwd=0.1) + - tm_shape(ecs_sf) + tm_dots("red", size = .05, alpha = .4) + + tm_shape(admin_lev1_sf) + tm_borders(lwd=0.01) + + tm_shape(ecs_sf) + tm_dots("red", size = .02, alpha = .4) + # tm_shape(ecs_sf_sssi50m) + tm_dots("yellow", size = .5, alpha = .4) + # tm_shape(ecs_sf_sssi500m) + tm_dots("orange", size = .5, alpha = .4) + # tm_shape(ecs_sf_sssibeyond500m) + tm_dots("red", size = .5, alpha = .4) - tm_scale_bar(position = c("right", "bottom")) + +# tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + tm_credits("Data: UK Data Service (OGL)\n& Jeremy H. Kidwell,\nGraphic is CC-by-SA 4.0", size = 0.4, @@ -1155,13 +1192,14 @@ tm_shape(sssi_simplified) + ```{r all_wilderness_ecs_plot, message=FALSE, warning=FALSE, fig.width=4, fig.cap="Figure 12"} # Plot map with all wilderness shapes on it - -tm_shape(sssi_simplified) + tm_fill(col = "blue", alpha = 0.3, lwd=0.001, title = "Wilderness Areas") + - tm_shape(wildland_simplified) + tm_fill(col = "green", alpha = 0.3, lwd=0.001) + - tm_shape(forestinv_simplified) + tm_fill(col = "orange", alpha = 0.3, lwd=0.001) + - tm_scale_bar(breaks = c(0, 100, 200), size = 1) + - tm_shape(ecs_sf) + tm_dots("red", size = .05, alpha = .4) + - tm_scale_bar(position = c("right", "bottom")) + +# TODO set bounding box to clip all polygons (or identify offending layer) +tm_shape(sssi_simplified, bbox = scotland) + tm_fill(col = "blue", alpha = 0.4, lwd=0.01, title = "Wilderness Areas") + + tm_shape(wildland_simplified, bbox = scotland) + tm_fill(col = "green", alpha = 0.4, lwd=0.01) + + tm_shape(forestinv_simplified, bbox = scotland) + tm_fill(col = "orange", alpha = 0.4, lwd=0.01) + + tm_shape(admin_lev1) + tm_borders(lwd=0.01) + +# tm_scale_bar(breaks = c(0, 100, 200), size = 1) + + tm_shape(ecs_sf) + tm_dots("red", size = .02, alpha = .4) + +# tm_scale_bar(position = c("right", "bottom")) + tm_style("gray") + tm_credits("Data: UK Data Service (OGL)\n& Jeremy H. Kidwell,\nGraphic is CC-by-SA 4.0", size = 0.4, diff --git a/mapping_draft.html b/mapping_draft.html index 594968e..3238660 100644 --- a/mapping_draft.html +++ b/mapping_draft.html @@ -11,7 +11,7 @@ - + Mapping Environmental Action in Scotland @@ -205,7 +205,7 @@ $(document).ready(function () {

Mapping Environmental Action in Scotland

Jeremy H. Kidwell

-

2019-02-22

+

2019-02-26

@@ -968,11 +968,11 @@ permaculture_forestinv_row
-Figure 11 +Figure 11

Figure 11

-Figure 12 +Figure 12

Figure 12

diff --git a/mapping_draft.md b/mapping_draft.md index 3c3b6dd..b4d6764 100644 --- a/mapping_draft.md +++ b/mapping_draft.md @@ -1,13 +1,13 @@ --- title: "Mapping Environmental Action in Scotland" abstract: -# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 22, 2019 +# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 26, 2019 style: jeremy1 author: "[Jeremy H. Kidwell](http://jeremykidwell.info)" affiliation: University of Birmingham institute: University of Birmingham e-mail: "[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)" -date: "2019-02-22" +date: "2019-02-26" bibliography: biblio.bib linkcolor: black geometry: margin=1in