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final fixes on CRS, completed inset maps
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Jeremy Kidwell
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15c4c480c3
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2acd7999fd
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@ -930,14 +930,12 @@ if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) {
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# download.file("", destfile = "data/SSSI_SCOTLAND_ESRI.zip")
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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(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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# sssi_simplified_sp <- rgeos::gSimplify(sssi_sp, tol=3)
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# 2. Download wild land areas:
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if (file.exists("data/WILDLAND_SCOTLAND.shp") == FALSE) {
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@ -948,11 +946,9 @@ 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(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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# 3. Download data for National Forest Inventory:
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# Note: UK-wide data is here: https://opendata.arcgis.com/datasets/bcd6742a2add4b68962aec073ab44138_0.zip?outSR=%7B%22wkid%22%3A27700%2C%22latestWkid%22%3A27700%7D
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@ -964,7 +960,6 @@ 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(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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@ -1024,16 +1019,7 @@ forestinv_buf500 <- st_buffer(forestinv_simplified, dist = 500)
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# Calculate number of groups within polygons
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# calculate coincidence of ecs points within each polygons and buffers for each
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# todo, possibly use st_difference(sssi_buf50, sssi)
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# TODO: sort out why these layers aren't inheriting crs
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st_crs(wildland) <- 27700
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st_crs(wildland_buf50) <- 27700
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st_crs(wildland_buf500) <- 27700
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st_crs(forestinv) <- 27700
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st_crs(forestinv_buf50) <- 27700
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st_crs(forestinv_buf500) <- 27700
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# TODO: possibly use st_difference(sssi_buf50, sssi)
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# TODO: consider subsetting instead here, e.g.:
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# plot(lnd, col = "lightgrey") # plot the london_sport object
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@ -1080,6 +1066,7 @@ sssi_counts <- as.data.frame(sssi_counts)
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colnames(sssi_counts) <- c("Within SSSIs", "...50m", "...500m")
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# Generate dataframe from rows based on percentages of totals
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# TODO: Work with ecs_sf etc., but first sort out why length is different between shapefiles
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ecs_sssi_row_pct <- ecs_sssi_row/length(ecs)
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pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX)
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dtas_sssi_row_pct <- dtas_sssi_row/length(dtas)
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@ -1095,7 +1082,6 @@ colnames(sssi_counts_pct) <- c("% Within SSSIs", "% within 50m", "% within 500m"
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# Merge into larger dataframe
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sssi_counts_merged <- cbind(sssi_counts, sssi_counts_pct)
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# Generate dataframe based on wildland buffers
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ecs_wildland_row <- c(sum(apply(st_within(ecs_sf, wildland, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(ecs_sf, wildland_buf500, sparse=FALSE), 1, any)))
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File diff suppressed because one or more lines are too long
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@ -1,13 +1,13 @@
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---
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title: "Mapping Environmental Action in Scotland"
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abstract:
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# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 26, 2019
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# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: March 01, 2019
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style: jeremy1
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author: "[Jeremy H. Kidwell](http://jeremykidwell.info)"
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affiliation: University of Birmingham
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institute: University of Birmingham
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e-mail: "[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)"
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date: "2019-02-26"
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date: "2019-03-01"
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bibliography: biblio.bib
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linkcolor: black
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geometry: margin=1in
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@ -89,11 +89,11 @@ Though there are too few eco-congregations and transition groups for a numerical
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## Eco-Congregation Scotland groups shown by concentration in administrative regions (NUTS3)
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Given the way population and places of worship are unevenly distributed across Scotland it is important to represent data in terms of relative distribution. For this study, we attempted to "normalise" our data in two different ways, (1) as shown by Figure 2 above, by taking population figures from the 2011 census (see data sheet in Appendix A) and (2) by adjusting relative to the number of places of worship in each council region.[^15914204] The latter of these two can yield particularly unexpected results. Thus, of the 4048 "places of worship" in Scotland, the highest concentration is actually the region, with 435, second is 329 (). Rank of Council Areas by population and number of places of worship is also included in Appendix A.
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@ -105,11 +105,11 @@ Turning to the total of 4048 "places of worship" in Scotland, we find a slightly
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Whereas our initial measurements indicated a prominent lead for Edinburgh, by normalising our data in this way we can highlight the stronger-than-expected presence of several others that might otherwise escape notice because they lie in a region with significantly lower population or numerically less places of worship. Taking the PointX data on "places of worship" in Scotland, we find a less dramatic picture, but also a slightly different one. The positive outliers include East Renfrewshire (3.4x) Edinburgh (2.9x), Stirling (2.2), West Lothian (1.9x) and Aberdeen (1.5x). Again, negative outliers are far less dramatic, with only Midlothian possessing a ratio of more than 100% negative difference from the number of "places of worship" at 1.5x *fewer*.
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## Other environmental groups shown by concentration in administrative regions (NUTS)
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## Cartogram Comparisons
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@ -235,10 +235,10 @@ The key question which this analysis seeks to answer is whether ECS, or the othe
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Of all the groups surveyed in this study, Eco-Congregation Scotland is the most heavily concentrated in large urban areas (33.53%), exceeding by almost 50% the rate for all places of worship (22.96% in large urban areas). Transition is a much more modest 20% and development trusts a bit lower at 15%. It is interesting to note that the rate of ECS concentration in these large urban areas matches the level of overall population distribution (34.5%). On the other end of the scale, Eco-Congregation Scotland is the least concentrated in remote rural areas (with 3.93% on level 7 and 5.44% on level 8 on the urban-rural scale), though again, they correlate roughly to the general population distribution (3.2% and 2.9% respectively). Places of worship outpace both the population of Scotland and the footprint of Eco-Congregation Scotland, with 14.98% in very remote rural areas, but this is exceeded by transition at 16.47% and both by Scottish community development trusts at 32.14%. So while Eco-Congregation Scotland correlates roughly with Scottish population distribution across the urban-rural scale, it has a considerably more urban profile than either of the other two groups surveyed.
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## Eco-Congregation Scotland\nconcentrations in Urban Rural 8-fold classifications
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# Wealth, Employment, and Literacy
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@ -259,7 +259,7 @@ Of all the groups surveyed in this study, Eco-Congregation Scotland is the most
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### Barplot
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### Boxplot
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@ -303,13 +303,6 @@ We can find divergence between transition communities and eco-congregation when
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## proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
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```
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```
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## OGR data source with driver: ESRI Shapefile
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## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "WILDLAND_SCOTLAND"
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## with 42 features
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## It has 3 fields
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```
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```
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## Reading layer `National_Forest_Inventory_Woodland_Scotland_2017' from data source `/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data/National_Forest_Inventory_Woodland_Scotland_2017.shp' using driver `ESRI Shapefile'
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## Simple feature collection with 199698 features and 7 fields
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@ -320,14 +313,6 @@ We can find divergence between transition communities and eco-congregation when
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## proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
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```
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```
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## OGR data source with driver: ESRI Shapefile
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## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "National_Forest_Inventory_Woodland_Scotland_2017"
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## with 199698 features
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## It has 7 fields
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## Integer64 fields read as strings: OBJECTID
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```
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# Proximity to "Wilderness"
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Chasing down a curiosity, I decided to try and calculate whether proximity to "wilderness" or "scenic nature" or just trees might have some impact on generating more mobilised communities. I realised that there would be several problems with this kind of calculation up front, first being that "nature" is a deeply problematic construct, reviled by geographers and philosophers alike. With this in mind, I identified several different ways of reckoning wilderness, starting with the highly anachronistic "Scenic Land" designation from the 1970s. Then I pursued the more carefully calculated "core wild areas" generated by SNH just a few years ago. However, even the core wile areas concept has been criticised heavily, so I also expanded out my search to include all sites of special scientific interest and then went even wider to include the Scottish Forestry Service's "Native Woodland" and finally, the most generic possible measurement, any land identified as forested at the last Forest Inventory.
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@ -523,9 +508,9 @@ So what did I discover? The results were inconclusive. First, it is important to
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</table>
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# Appendix A
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