commit d02c16652eab22a0f219339e6cb5147a6ce282c0 Author: Jeremy Kidwell Date: Mon May 21 15:56:24 2018 +0100 first commit diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..94a9ed0 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/README.md b/README.md new file mode 100644 index 0000000..0b3f07d --- /dev/null +++ b/README.md @@ -0,0 +1,30 @@ +# Development Trust Association Scotland Analysis + +This R application represents an ongoing research collaboration between Jeremy Kidwell (UOB) and DTAS. The application takes a series of points representing the whole DTAS network and conducts a series of analyses, illuminating a series of research questions generated by myself and DTAS staff / board members. + +I am working here within the style commended by Hadley Wickham in his Advanced R "Style guide" and some of the parameters described in Karl Broman's guide, "[Initial Steps Toward Reproducible Research](http://kbroman.org/steps2rr/)" + +# Installation requirements: + +Though I am trying very hard to make this reproducible research, there are some datasets which don't have tidy download links. The following must be downloaded from the repositories indicated and placed in the "data" subdirectory + +- "data/poi.csv" POI data from digimap. Most academic researchers should be able to aquire it through an institutional subscription from Digimap, [Ordnance Survey Data Download](https://digimap.edina.ac.uk/datadownload/osdownload). The POI set is located under "Boundary and Location Data" under "Points of Interest," "select visible area" for whole UK then download. +- "data/retailpoints_version11_dec17.txt" Retail POI data packaged by GeoLytix, download from here: (https://geolytix.co.uk/?retail_points) +- "data/scotland_laulevel1_2011" from the [Edina Census Easy Download Service](https://borders.ukdataservice.ac.uk/easy_download.html) under Scotland, "Scottish Local Administrative Units Level 1 2011". Click on "Download features in Shapefile format as ZIP file" + + +# Files: +- community_anchors.R is the "canonical" R markdown script for this project; community_anchors.Rnw is updated based on changes to the former and is meant to be used with knitr to export to elegant .tex. This is especially important for tables. +- community_anchors_2018May19.Rpres is a presentation delivered on May 19 2018 in which I set out some initial provocations. +- community_anchors.qgs is a working document for use in QGIS to conduct analysis on these data sets + +Paths in this folder are used mostly for R processing. 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+ +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +towgs84=446.448,-125.157,542.06,0.15,0.247,0.842,-20.489 +units=m +no_defs + 1 + EPSG:27700 + + + + true + 2 + MU + + false + + 255 + 255 + 255 + 255 + 255 + 255 + 0 + + + + + + 90 + + meters + m2 + + + + + + + + + + diff --git a/community_anchors_2018May19.Rpres b/community_anchors_2018May19.Rpres new file mode 100644 index 0000000..5b3f180 --- /dev/null +++ b/community_anchors_2018May19.Rpres @@ -0,0 +1,454 @@ +Analysing the Development Trust Association Scotland footprint +======================================================== +author: Jeremy H. Kidwell, PhD +date: 18 May 2018 +autosize: true + +Provisional analysis compiled by Dr Jeremy Kidwell for DTAS. +Based on live, open, and reproducible code! + + +Outline for the day +======================================================== + +```{r, echo=FALSE, eval=TRUE, include=FALSE} +# Some opening notes for the stray reader of this code +# This is pretty messy stuff, pulled together in some haste. +# I'm aware that there are tons of places where repeated code +# should be set up as libraries - feel free to put in a +# pull request to this effect! +# +# I'm also aware that my method of creating a table of quantiles +# is massively inefficient. I'm sure there's a brisk way to +# do this, but couldn't find anything workable. +# As always, code review much appreciated and comments welcome! + +# load libraries needed for operations +require(sp) # required for rgdal +require(rgdal) # required for readOGR +require(GISTools) # required for poly.counts +require(maptools) # required for spTransform +require(RColorBrewer) # required for pretty colours +require(xtable) # required for xtables done below +require(RCurl) # required for dataset downloads using curl + +# Preliminaries------------------------------------------------ +# Define CRS for here and later-------------------------------- +wgs84 = '+proj=longlat +datum=WGS84' +bng = "+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" +setwd("~/Dropbox/writing_articles_chapters/201805_dtas") + +# Create data directory if needed: +if (dir.exists("data") == FALSE) { + dir.create("data") +} + +if (dir.exists("derivedData") == FALSE) { + dir.create("derivedData") +} + +if (dir.exists("figures") == FALSE) { + dir.create("figures") +} + +# 1. Load in datasets + +# read in polygon for UK admin boundaries---------------------- +admin <- readOGR("data", "scotland_laulevel1_2011") +# transform CRS to wgs84 for comparisons below +admin.wgs <- spTransform(admin, CRS("+init=epsg:4326")) + +# read in relevant polygons, Scottish Index of Multiple deprivation (already WGS84) +http://sedsh127.sedsh.gov.uk/Atom_data/ScotGov/ZippedShapefiles/SG_SIMD_2016.zip +simd <- readOGR("data", "sg_simd_2016") + +# Load dtas dataset------------------------------------ +dtas <- read.csv("data/dtas_4.0.csv") +coordinates(dtas) <- c("X", "Y") +proj4string(dtas) <- proj4string(simd) +# transform CRS to wgs84 for comparisons below +dtas.wgs <- spTransform(dtas, CRS("+init=epsg:4326")) + +# Load GWSF dataset ---------------------- +gwsf <- readOGR("data", "gwsf_groups_gb_sct") +# transform CRS on admin (from BNG) to wgs84 for comparisons below +gwsf.wgs <- spTransform(gwsf, CRS("+init=epsg:4326")) + + +# Simplify to third decimal point for accurate representation of coordinate precision +# JK Note: TBD! +``` + +- Representation in areas of deprivation +- Mapping community anchors +- Bonus: social media and DTAS + + +About the research +======================================================== + +- Begun in 2014 at Ediburgh Uni, now Birmingham +- Driving question: How do we improve visibility and efficacy of community-level work? + - Generating (open access) data sets + - Consultancy as participatory research + - Connecting researchers, policymakers, third sector + +Work to Date +======================================================== + +- Ongoing research partnerships + - Scottish Community Alliance + - DTAS (239 sites) + - Eco-Congregation Scotland (~400) and Eco-Church England & Wales (~2500) + - Christians Against Poverty (600) + - Ecolise (European network of networks) +- Additional data sets produced for + - GWSF, City Farms & Gardens, Community Land Scotland, Church(es) of Scotland, England & Wales... +- Digital "geospatial intelligence" Carto platform up and running +) +```{r, echo=FALSE, eval=TRUE, include=TRUE} + + +``` + +1. Focus: DTAS and Deprivation +======================================================== + +```{r, echo=FALSE, eval=TRUE, include=TRUE} +# Part I - Generate tables and plots based on SIMD ranks + +# Subset SIMD for bottom 5/10/15% by overall rank +simd.bottom15r <- simd[ which(simd$rank < 1047),] +simd.bottom10r <- simd[ which(simd$rank < 698),] +simd.bottom5r <- simd[ which(simd$rank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15incr <- simd[ which(simd$incrank < 1047),] +simd.bottom10incr <- simd[ which(simd$incrank < 698),] +simd.bottom5incr <- simd[ which(simd$incrank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15empr <- simd[ which(simd$emprank < 1047),] +simd.bottom10empr <- simd[ which(simd$emprank < 698),] +simd.bottom5empr <- simd[ which(simd$emprank < 349),] + +# Now generate subsetted SPDF which only includes dtas points which are +# located in remaining (subsetted) SIMD polygons +dtas.simd.bottom15r <- dtas[!is.na(over(dtas, geometry(simd.bottom15r))),] +dtas.simd.bottom10r <- dtas[!is.na(over(dtas, geometry(simd.bottom10r))),] +dtas.simd.bottom5r <- dtas[!is.na(over(dtas, geometry(simd.bottom5r))),] + +# Add data from relevant SIMD polygons for each row in dtas DF +dtas.simd.bottom15r@data <- cbind(dtas.simd.bottom15r@data,over(dtas.simd.bottom15r,simd.bottom15r)) +dtas.simd.bottom10r@data <- cbind(dtas.simd.bottom10r@data,over(dtas.simd.bottom10r,simd.bottom10r)) +dtas.simd.bottom5r@data <- cbind(dtas.simd.bottom5r@data,over(dtas.simd.bottom5r,simd.bottom5r)) + +# Convert to dataframe, strip out unnecessary columns, and sort by rank +dtas.simd.bottom15r.tidy <- data.frame("name"=dtas.simd.bottom15r$name,"rank"=dtas.simd.bottom15r$rank) +dtas.simd.bottom15r.tidy <- dtas.simd.bottom15r.tidy[with(dtas.simd.bottom15r.tidy, order(rank)), ] +dtas.simd.bottom10r.tidy <- data.frame("name"=dtas.simd.bottom10r$name,"rank"=dtas.simd.bottom10r$rank) +dtas.simd.bottom10r.tidy <- dtas.simd.bottom10r.tidy[with(dtas.simd.bottom10r.tidy, order(rank)), ] +dtas.simd.bottom5r.tidy <- data.frame("name"=dtas.simd.bottom5r$name,"rank"=dtas.simd.bottom5r$rank) +dtas.simd.bottom5r.tidy <- dtas.simd.bottom5r.tidy[with(dtas.simd.bottom5r.tidy, order(rank)), ] + +# plot(admin) +plot(dtas) +plot(dtas.simd.bottom15r, add = TRUE, col="Red") +plot(admin, border="grey", lwd=0.25, add = TRUE) + +# Export tables to PDF, with titles and plots ahead of each table (admin boundaries, add all points, add subset in red) +# End Part I +``` + +DTAS Groups Located within 15% most deprived areas +======================================================== + +```{r, echo=FALSE, eval=TRUE, include=TRUE} + +xt <- xtable(dtas.simd.bottom15r.tidy, caption = "table of DTAS groups in bottom 15%") + +print(xt, + type = "html", + tabular.environment = "longtable", + sanitize.colnames.function = function(x){x}, + include.rownames = FALSE, + floating = FALSE) +``` + +Running Comparisons +======================================================== + +It may be useful to know the number of groups present (as above), but we can illuminate this information by testing out how frequently different types of groups appear in these areas. + +```{r, echo=FALSE, eval=TRUE, include=FALSE} +# Part II - Calculate coincidence of other features + +# Load in POI table + +# I'm going to take some shortcuts here, preventing this code from being truly reproducible +# because the poi dataset is really huge - I've already downloaded and filtered to specific codes for features +# so code immediately below is untested. Will need to adjust below to the actual code I'm working with eventually. + +## Skip ==> +# poi <- read.csv("data/poi.csv", header = FALSE, sep = "|") +# coordinates(poi) <- c("Feature Easting", "Feature Northing") +# proj4string(poi) <- proj4string(CRS(bng)) +## transform CRS on churches (from BNG) to wgs84 for comparisons below +# poi.wgs <- spTransform(poi, CRS(wgs84)) + +## subsets +#poi.pubs <- poi[!is.na(poi[3] = "01020034")] +#poi.chequecashing <- poi[!is.na(poi[3] = "02090142")] +#poi.pawnbrokers <- poi[!is.na(poi[3] = "02090151")] +#poi.worship <- poi[!is.na(poi[3] = "06340459")] +# ==> to here for now + +# Load in retail data from geolytics dataset +# from here: https://geolytix.co.uk/?retail_points +poi.grocery <- read.csv("data/retailpoints_version11_dec17.txt", sep = "\t") +# select useful columns +poi.grocery <- subset(poi.grocery, select = c("retailer", "store_name", "long_wgs", "lat_wgs")) +# convert to spdf +coordinates(poi.grocery) <- c("long_wgs", "lat_wgs") +proj4string(poi.grocery) <- CRS("+init=epsg:4326") +# filter out non-Scottish data +poi.grocery <- poi.grocery[!is.na(over(poi.grocery, geometry(admin.wgs))),] + +# Load in British pubs from Ordnance survey dataset +poi.pubs <- read.csv("data/poi_pubs.csv", header = FALSE, sep = "|") +# select useful columns +poi.pubs <- subset(poi.pubs, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pubs) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pubs) <- c("x", "y") +proj4string(poi.pubs) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pubs.wgs <- spTransform(poi.pubs, CRS("+init=epsg:4326")) +# filter out non-Scottish pubs +poi.pubs.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(admin.wgs))),] + +# Load in check cashing from Ordnance survey dataset +poi.checkcashing <- read.csv("data/poi_chequecashing.csv", header = FALSE, sep = "|") +# select useful columns +poi.checkcashing <- subset(poi.checkcashing, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.checkcashing) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.checkcashing) <- c("x", "y") +proj4string(poi.checkcashing) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.checkcashing.wgs <- spTransform(poi.checkcashing, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.checkcashing.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(admin.wgs))),] + +# Load in pawnbrokers from Ordnance survey dataset +poi.pawnbrokers <- read.csv("data/poi_pawnbrokers.csv", header = FALSE, sep = "|") +# select useful columns +poi.pawnbrokers <- subset(poi.pawnbrokers, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pawnbrokers) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pawnbrokers) <- c("x", "y") +proj4string(poi.pawnbrokers) <- CRS("+init=epsg:27700") +# transform CRS on pawnbrokers (from BNG) to wgs84 for comparisons below +poi.pawnbrokers.wgs <- spTransform(poi.pawnbrokers, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) pawnbrokers +poi.pawnbrokers.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(admin.wgs))),] + +# Load in places of worship from Ordnance survey dataset +poi.pow <- read.csv("data/poi_pow.csv", header = FALSE, sep = "|") +# select useful columns +poi.pow <- subset(poi.pow, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pow) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pow) <- c("x", "y") +proj4string(poi.pow) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pow.wgs <- spTransform(poi.pow, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.pow.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(admin.wgs))),] + +# create WGS versions of SIMD subsets from above for comparisons below +simd.bottom15r.wgs <- spTransform(simd.bottom15r, CRS("+init=epsg:4326")) +simd.bottom10r.wgs <- spTransform(simd.bottom10r, CRS("+init=epsg:4326")) +simd.bottom5r.wgs <- spTransform(simd.bottom5r, CRS("+init=epsg:4326")) + +# Test it out with a quick plot +# plot(poi.pubs.wgs) + +# run further subsetting, as above, for specific bands of simd +grocery.simd.bottom15r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom15r.wgs))),] +grocery.simd.bottom10r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom10r.wgs))),] +grocery.simd.bottom5r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom5r.wgs))),] + +pubs.simd.bottom15r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom15r.wgs))),] +pubs.simd.bottom10r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom10r.wgs))),] +pubs.simd.bottom5r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom5r.wgs))),] + +pawnbrokers.simd.bottom15r.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(simd.bottom15r.wgs))),] +checkcashing.simd.bottom15r.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(simd.bottom15r.wgs))),] +pow.simd.bottom15r.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(simd.bottom15r.wgs))),] + +# Part II.2 Run some calculations on presence for various types of entities + +# Calculate total number of DTAS "community anchors" within bottom 15% +length(dtas.simd.bottom15r$rank) / length(dtas$name) + +``` + +Pubs +======================================================== + +We can compare this frequency against some other features. The ordnance survey tells us that total pubs in Scotland are: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(poi.pubs.wgs$name) +``` + +Pubs +======================================================== + +The total numnber of Pubs located within these deprived areas noted above is: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +# Calculate percentage of total pubs in Scotland located within bottom 15% of IMD by rank +length(pubs.simd.bottom15r.wgs$name) / length(poi.pubs.wgs$name) +``` + +Churches +======================================================== + +To take another example, there are (according to the Ordnance Survey), the following number of places of worship in Scotland: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(poi.pow.wgs$name) +``` + +Of all these, our deprived areas have: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +# places of worship +length(pow.simd.bottom15r.wgs$name) / length(poi.pow.wgs$name) +``` + + +Retail +======================================================== + +Another example we can look at is retail grocery stores. A geospatial company called GeoLytix publishes a complete set of British retailers on a regular basis. As of last November, they indicated that there were the following number of grocery stores in Scotland: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(poi.grocery$store_name) +``` + + +Within our deprived areas, this represents + +```{r echo=FALSE, eval=TRUE, include=TRUE} + +# Calculate percentage of total retail grocers in Scotland +# located within bottom 15% of IMD by rank +length(grocery.simd.bottom15r.wgs$store_name) / length(poi.grocery$store_name) +# drill a bit deeper - morrisons stores +morrisons.scotland <- poi.grocery[poi.grocery$retailer == "Morrisons", ] +morrisons.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Morrisons", ] +``` + +Retail, ctd. +======================================================== + +We can break this down by brand to get a more interesting indication. There are the following number of Morrison's brand stores in Scotland: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(morrisons.scotland) +``` + +Of these, the following percentage are located in deprived areas: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(morrisons.bottom15r.scotland$store_name) / length(morrisons.scotland) +# sainsburys +sainsburys.scotland <- poi.grocery[poi.grocery$retailer == "Sainsburys", ] +sainsburys.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Sainsburys", ] +``` + +Retail, ctd. +======================================================== + +We find the predictable contrast by turning to Sainsburys. There are the following number of Sainsburys stores in Scotland: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(sainsburys.scotland) +``` + +Represented in our deprived areas are just: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(sainsburys.bottom15r.scotland$store_name) / length(sainsburys.scotland) +``` + +Retail, ctd. +======================================================== + +Sadly, the best bellweather of a deprived area in Scotland isn't any of the above, but rather pawnbrokers and check cashing stores. There are the following number of pawnbrokers: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(poi.pawnbrokers.wgs$name) +``` + +...and check cashing stores: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(poi.checkcashing.wgs$name) +``` + + +Retail, ctd. +======================================================== + +Representation in our areas of of deprivation is, for check cashing establishments: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(checkcashing.simd.bottom15r.wgs$name) / length(poi.checkcashing.wgs$name) +``` + +and pawnbrokers: + +```{r echo=FALSE, eval=TRUE, include=TRUE} +length(pawnbrokers.simd.bottom15r.wgs$name) / length(poi.pawnbrokers.wgs$name) +``` + + +Pawnbrokers Mapped +======================================================== + +```{r echo=FALSE, eval=TRUE, include=TRUE} +plot(poi.checkcashing.wgs) +plot(checkcashing.simd.bottom15r.wgs, col="Red", add = TRUE) +plot(admin, border="grey", lwd=0.25, add = TRUE) +``` + + +Targetting possile community anchors +======================================================== + +I've put together two different resources for DTAS to access this data: + +- [On our Carto platform: a "slippy map""](https://carto.mapping.community/user/mapcomm-admin/builder/98dbb620-75d3-11e7-8dd5-005056372088/embed) +- A table of groups (forthcoming report!) + + +Discussion +======================================================== + +- What other kinds of data and analysis can you see being of value to you for strategic decision making? +- Possible demos: + - Common Ground Map + - Carto Platform + - QGIS + + +Bonus: Social Media Use +======================================================== + +- Research question: how do community groups use social media? diff --git a/dtas_analysis.R b/dtas_analysis.R new file mode 100644 index 0000000..dc2146c --- /dev/null +++ b/dtas_analysis.R @@ -0,0 +1,286 @@ +# Some opening notes for the stray reader of this code +# This is pretty messy stuff, pulled together in some haste. +# I'm aware that there are tons of places where repeated code +# should be set up as libraries - feel free to put in a +# pull request to this effect! +# +# I'm also aware that my method of creating a table of quantiles +# is massively inefficient. I'm sure there's a brisk way to +# do this, but couldn't find anything workable. +# As always, code review much appreciated and comments welcome! + +# load libraries needed for operations +require(sp) # required for rgdal +require(rgdal) # required for readOGR +require(GISTools) # required for poly.counts +require(maptools) # required for spTransform +require(xtable) # required for xtables done below +require(RColorBrewer) # required for pretty colours +require(RCurl) # required for dataset downloads using curl + +# Preliminaries------------------------------------------------ +# Define CRS for here and later-------------------------------- +wgs84 = '+proj=longlat +datum=WGS84' +bng = "+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" +setwd("~/Dropbox/writing_articles_chapters/201805_dtas") + +# Create data directory if needed: +if (dir.exists("data") == FALSE) { + dir.create("data") +} + +if (dir.exists("derivedData") == FALSE) { + dir.create("derivedData") +} + +# 1. Load in datasets + +# read in polygon for UK admin boundaries---------------------- +download.file("https://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_laulevel1_2011.zip", destfile = "data/Scotland_laulevel1_2011.zip") +unzip("data/Scotland_laulevel1_2011.zip", exdir = "data") +admin <- readOGR("./data", "scotland_laulevel1_2011") +# transform CRS to wgs84 for comparisons below +admin.wgs <- spTransform(admin, CRS("+init=epsg:4326")) + +# read in relevant polygons, Scottish Index of Multiple deprivation (already WGS84) +download.file("http://sedsh127.sedsh.gov.uk/Atom_data/ScotGov/ZippedShapefiles/SG_SIMD_2016.zip", destfile = "data/SG_SIMD_2016.zip") +unzip("data/SG_SIMD_2016.zip", exdir = "data") +simd <- readOGR("data", "SG_SIMD_2016") + + +# Load dtas dataset------------------------------------ +# dtas <- read.csv(text=getURL("https://zenodo.org/record/198703/files/dtas_3.2.csv")) +# note: need to upload version 4 to zenodo repository! +dtas <- read.csv("data/dtas_4.0.csv") +coordinates(dtas) <- c("X", "Y") +proj4string(dtas) <- proj4string(simd) +# transform CRS to wgs84 for comparisons below +dtas.wgs <- spTransform(dtas, CRS("+init=epsg:4326")) + +# note: need to upload data set to zenodo repository! Note: tbd; simplify to third decimal point for accurate representation of coordinate precision +# Load GWSF dataset ---------------------- +gwsf <- readOGR("data", "gwsf_groups_gb_sct") +# transform CRS on admin (from BNG) to wgs84 for comparisons below +gwsf.wgs <- spTransform(gwsf, CRS("+init=epsg:4326")) + +# Part I - Generate tables and plots based on SIMD ranks + +# Subset SIMD for bottom 5/10/15% by overall rank +# Note: using hardcoded numbers for 5/10/15% figures. This should probably be calculated using a length() routine +simd.bottom15r <- simd[ which(simd$rank < 1047),] +simd.bottom10r <- simd[ which(simd$rank < 698),] +simd.bottom5r <- simd[ which(simd$rank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15incr <- simd[ which(simd$incrank < 1047),] +simd.bottom10incr <- simd[ which(simd$incrank < 698),] +simd.bottom5incr <- simd[ which(simd$incrank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15empr <- simd[ which(simd$emprank < 1047),] +simd.bottom10empr <- simd[ which(simd$emprank < 698),] +simd.bottom5empr <- simd[ which(simd$emprank < 349),] + +# Now generate subsetted SPDF which only includes dtas points which are +# located in remaining (subsetted) SIMD polygons +dtas.simd.bottom15r <- dtas[!is.na(over(dtas, geometry(simd.bottom15r))),] +dtas.simd.bottom10r <- dtas[!is.na(over(dtas, geometry(simd.bottom10r))),] +dtas.simd.bottom5r <- dtas[!is.na(over(dtas, geometry(simd.bottom5r))),] +gwsf.simd.bottom15r <- gwsf[!is.na(over(gwsf, geometry(simd.bottom15r))),] + + +# Add data from relevant SIMD polygons for each row in dtas DF +dtas.simd.bottom15r@data <- cbind(dtas.simd.bottom15r@data,over(dtas.simd.bottom15r,simd.bottom15r)) +dtas.simd.bottom10r@data <- cbind(dtas.simd.bottom10r@data,over(dtas.simd.bottom10r,simd.bottom10r)) +dtas.simd.bottom5r@data <- cbind(dtas.simd.bottom5r@data,over(dtas.simd.bottom5r,simd.bottom5r)) + +gwsf.simd.bottom15r@data <- cbind(gwsf.simd.bottom15r@data,over(gwsf.simd.bottom15r,simd.bottom15r)) + +# Convert to dataframe, strip out unnecessary columns, and sort by rank +dtas.simd.bottom15r.tidy <- data.frame("name"=dtas.simd.bottom15r$name,"rank"=dtas.simd.bottom15r$rank) +dtas.simd.bottom15r.tidy <- dtas.simd.bottom15r.tidy[with(dtas.simd.bottom15r.tidy, order(rank)), ] +dtas.simd.bottom10r.tidy <- data.frame("name"=dtas.simd.bottom10r$name,"rank"=dtas.simd.bottom10r$rank) +dtas.simd.bottom10r.tidy <- dtas.simd.bottom10r.tidy[with(dtas.simd.bottom10r.tidy, order(rank)), ] +dtas.simd.bottom5r.tidy <- data.frame("name"=dtas.simd.bottom5r$name,"rank"=dtas.simd.bottom5r$rank) +dtas.simd.bottom5r.tidy <- dtas.simd.bottom5r.tidy[with(dtas.simd.bottom5r.tidy, order(rank)), ] + +gwsf.simd.bottom15r.tidy <- data.frame("name"=gwsf.simd.bottom15r$name,"rank"=gwsf.simd.bottom15r$rank) +gwsf.simd.bottom15r.tidy <- gwsf.simd.bottom15r.tidy[with(gwsf.simd.bottom15r.tidy, order(rank)), ] + + +# Export csv tables for use in other applications +write.csv(dtas.simd.bottom15r, "derivedData/dtas.simd.bottom15r.csv", row.names=FALSE) +write.csv(gwsf.simd.bottom15r, "derivedData/gwsf.simd.bottom15r.csv", row.names=FALSE) + + +# Generate table for DTAS using xtable for bottom 15% simd rank +xt <- xtable(dtas.simd.bottom15r.tidy, caption = "table of DTAS groups in bottom 15%") + +print(xt, + type = "html", + tabular.environment = "longtable", + sanitize.colnames.function = function(x){x}, + include.rownames = FALSE, + floating = FALSE) + +# Generate table for GWSF using xtable for bottom 15% simd rank + +xt <- xtable(gwsf.simd.bottom15r.tidy, caption = "table of GWSF groups in bottom 15%") + +print(xt, + type = "html", + tabular.environment = "longtable", + sanitize.colnames.function = function(x){x}, + include.rownames = FALSE, + floating = FALSE) + +# End Part I + +# Part II - Calculate coincidence of other features + +# Load in POI table + +# Sadly, this dataset is paywalled by digimap, though most academic researchers should be able to aquire it through +# an institutional subscription from Digimap, Ordnance Survey Data Download. The POI set is located under +# "Boundary and Location Data" under "Points of Interest," "select visible area" for whole UK then download. +# +# I'm going to take some shortcuts here, preventing this code from being truly reproducible +# because the poi dataset is really huge - I've already downloaded and filtered to specific codes for features +# so code immediately below is untested. Will need to adjust below to the actual code I'm working with eventually. + +## Skip ==> +# poi <- read.csv("data/poi.csv", header = FALSE, sep = "|") +# coordinates(poi) <- c("Feature Easting", "Feature Northing") +# proj4string(poi) <- proj4string(CRS(bng)) +## transform CRS on churches (from BNG) to wgs84 for comparisons below +# poi.wgs <- spTransform(poi, CRS(wgs84)) + +## subsets +#poi.pubs <- poi[!is.na(poi[3] = "01020034")] +#poi.chequecashing <- poi[!is.na(poi[3] = "02090142")] +#poi.pawnbrokers <- poi[!is.na(poi[3] = "02090151")] +#poi.worship <- poi[!is.na(poi[3] = "06340459")] +# ==> to here for now + +# Load in retail data from geolytics dataset +# from here: https://geolytix.co.uk/?retail_points +poi.grocery <- read.csv("data/retailpoints_version11_dec17.txt", sep = "\t") +# select useful columns +poi.grocery <- subset(poi.grocery, select = c("retailer", "store_name", "long_wgs", "lat_wgs")) +# convert to spdf +coordinates(poi.grocery) <- c("long_wgs", "lat_wgs") +proj4string(poi.grocery) <- CRS("+init=epsg:4326") +# filter out non-Scottish data +poi.grocery <- poi.grocery[!is.na(over(poi.grocery, geometry(admin.wgs))),] + +# Load in British pubs from Ordnance survey dataset +poi.pubs <- read.csv("data/poi_pubs.csv", header = FALSE, sep = "|") +# select useful columns +poi.pubs <- subset(poi.pubs, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pubs) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pubs) <- c("x", "y") +proj4string(poi.pubs) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pubs.wgs <- spTransform(poi.pubs, CRS("+init=epsg:4326")) +# filter out non-Scottish pubs +poi.pubs.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(admin.wgs))),] + +# Load in check cashing from Ordnance survey dataset +poi.checkcashing <- read.csv("data/poi_chequecashing.csv", header = FALSE, sep = "|") +# select useful columns +poi.checkcashing <- subset(poi.checkcashing, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.checkcashing) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.checkcashing) <- c("x", "y") +proj4string(poi.checkcashing) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.checkcashing.wgs <- spTransform(poi.checkcashing, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.checkcashing.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(admin.wgs))),] + +# Load in pawnbrokers from Ordnance survey dataset +poi.pawnbrokers <- read.csv("data/poi_pawnbrokers.csv", header = FALSE, sep = "|") +# select useful columns +poi.pawnbrokers <- subset(poi.pawnbrokers, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pawnbrokers) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pawnbrokers) <- c("x", "y") +proj4string(poi.pawnbrokers) <- CRS("+init=epsg:27700") +# transform CRS on pawnbrokers (from BNG) to wgs84 for comparisons below +poi.pawnbrokers.wgs <- spTransform(poi.pawnbrokers, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) pawnbrokers +poi.pawnbrokers.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(admin.wgs))),] + +# Load in places of worship from Ordnance survey dataset +poi.pow <- read.csv("data/poi_pow.csv", header = FALSE, sep = "|") +# select useful columns +poi.pow <- subset(poi.pow, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pow) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pow) <- c("x", "y") +proj4string(poi.pow) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pow.wgs <- spTransform(poi.pow, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.pow.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(admin.wgs))),] + + +# create WGS versions of SIMD subsets from above for comparisons below +simd.bottom15r.wgs <- spTransform(simd.bottom15r, CRS("+init=epsg:4326")) +simd.bottom10r.wgs <- spTransform(simd.bottom10r, CRS("+init=epsg:4326")) +simd.bottom5r.wgs <- spTransform(simd.bottom5r, CRS("+init=epsg:4326")) + + +# Test it out with a quick plot +plot(poi.pubs.wgs) + +# run further subsetting, as above, for specific bands of simd +grocery.simd.bottom15r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom15r.wgs))),] +grocery.simd.bottom10r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom10r.wgs))),] +grocery.simd.bottom5r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom5r.wgs))),] + +pubs.simd.bottom15r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom15r.wgs))),] +pubs.simd.bottom10r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom10r.wgs))),] +pubs.simd.bottom5r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom5r.wgs))),] + +pawnbrokers.simd.bottom15r.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(simd.bottom15r.wgs))),] +checkcashing.simd.bottom15r.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(simd.bottom15r.wgs))),] +pow.simd.bottom15r.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(simd.bottom15r.wgs))),] + + +# Part II.2 Run some calculations on presence for various types of entities + +# Calculate total number of DTAS "community anchors" within bottom 15% +length(dtas.simd.bottom15r$rank) / length(dtas$name) + +# Calculate percentage of total pubs in Scotland located within bottom 15% of IMD by rank +length(pubs.simd.bottom15r.wgs$name) / length(poi.pubs.wgs$name) + +# places of worship +length(pow.simd.bottom15r.wgs$name) / length(poi.pow.wgs$name) + +# Calculate percentage of total retail grocers in Scotland +# located within bottom 15% of IMD by rank +length(grocery.simd.bottom15r.wgs$store_name) / length(poi.grocery$store_name) + +# drill a bit deeper - morrisons stores +morrisons.scotland <- poi.grocery[poi.grocery$retailer == "Morrisons", ] +morrisons.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Morrisons", ] +length(morrisons.bottom15r.scotland$store_name) / length(morrisons.scotland) + +# sainsburys +sainsburys.scotland <- poi.grocery[poi.grocery$retailer == "Sainsburys", ] +sainsburys.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Sainsburys", ] +length(sainsburys.bottom15r.scotland$store_name) / length(sainsburys.scotland) + +# check cashing +plot(poi.checkcashing.wgs) +par(new=FALSE) +plot(checkcashing.simd.bottom15r.wgs, col="blue") +length(checkcashing.simd.bottom15r.wgs$name) / length(poi.checkcashing.wgs$name) + +# pawnbrokers +length(pawnbrokers.simd.bottom15r.wgs$name) / length(poi.pawnbrokers.wgs$name) \ No newline at end of file diff --git a/dtas_analysis.Rnw b/dtas_analysis.Rnw new file mode 100644 index 0000000..2528114 --- /dev/null +++ b/dtas_analysis.Rnw @@ -0,0 +1,334 @@ +\documentclass{article} + +\usepackage{geometry} + \geometry{ + a4paper, + total={170mm,257mm}, + left=20mm, + top=20mm, + } +\title{Analysis of DTAS footprint} +\author{Dr. Jeremy H. Kidwell} +\date{17 May 2018} +\begin{document} +\SweaveOpts{concordance=TRUE} + +<>= +# Some opening notes for the stray reader of this code +# This is pretty messy stuff, pulled together in some haste. +# I'm aware that there are tons of places where repeated code +# should be set up as libraries - feel free to put in a +# pull request to this effect! +# +# I'm also aware that my method of creating a table of quantiles +# is massively inefficient. I'm sure there's a brisk way to +# do this, but couldn't find anything workable. +# As always, code review much appreciated and comments welcome! + +# load libraries needed for operations +require(sp) # required for rgdal +require(rgdal) # required for readOGR +require(GISTools) # required for poly.counts +require(maptools) # required for spTransform +require(RColorBrewer) # required for pretty colours +require(xtable) +# Preliminaries------------------------------------------------ +# Define CRS for here and later-------------------------------- +wgs84 = '+proj=longlat +datum=WGS84' +bng = "+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" +setwd("~/Dropbox/writing_articles_chapters/201805_dtas") + +# Create data directory if needed: +if (dir.exists("data") == FALSE) { + dir.create("data") +} + + +# 1. Load in datasets + +# read in polygon for UK admin boundaries---------------------- +admin <- readOGR("data", "scotland_laulevel1_2011") +# transform CRS to wgs84 for comparisons below +admin.wgs <- spTransform(admin, CRS("+init=epsg:4326")) + +# read in relevant polygons, Scottish Index of Multiple deprivation (already WGS84) +simd <- readOGR("data", "sg_simd_2016") + +# Load dtas dataset------------------------------------ +dtas <- read.csv("data/dtas_4.0.csv") +coordinates(dtas) <- c("X", "Y") +proj4string(dtas) <- proj4string(simd) +# transform CRS to wgs84 for comparisons below +dtas.wgs <- spTransform(dtas, CRS("+init=epsg:4326")) + +# Load GWSF dataset ---------------------- +# gwsf <- readOGR("data", "gwsf_groups_gb_sct") +# transform CRS on admin (from BNG) to wgs84 for comparisons below +# gwsf.wgs <- spTransform(gwsf, CRS("+init=epsg:4326")) + + +# Simplify to third decimal point for accurate representation of coordinate precision +# JK Note: TBD! +@ + +\maketitle +\Large\textbf{Presentation for Development Trust Association Scotland} + +\section*{Introduction} +\par +The following represents some provisional analysis compiled by Dr Jeremy Kidwell for DTAS. This report has been written as reproducible research, so anyone can view the code, download the underlying data and reproduce these results. I hope that other scholars and third sector community networks may find this useful for research and strategic thinking. + +\section{Community Anchors: Development Trusts in Deprived Areas} + +<<>>= +# Part I - Generate tables and plots based on SIMD ranks + +# Subset SIMD for bottom 5/10/15% by overall rank +simd.bottom15r <- simd[ which(simd$rank < 1047),] +simd.bottom10r <- simd[ which(simd$rank < 698),] +simd.bottom5r <- simd[ which(simd$rank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15incr <- simd[ which(simd$incrank < 1047),] +simd.bottom10incr <- simd[ which(simd$incrank < 698),] +simd.bottom5incr <- simd[ which(simd$incrank < 349),] + +# Subset SIMD for bottom 5/10/15% by income domain rank +simd.bottom15empr <- simd[ which(simd$emprank < 1047),] +simd.bottom10empr <- simd[ which(simd$emprank < 698),] +simd.bottom5empr <- simd[ which(simd$emprank < 349),] + +# Now generate subsetted SPDF which only includes dtas points which are +# located in remaining (subsetted) SIMD polygons +dtas.simd.bottom15r <- dtas[!is.na(over(dtas, geometry(simd.bottom15r))),] +dtas.simd.bottom10r <- dtas[!is.na(over(dtas, geometry(simd.bottom10r))),] +dtas.simd.bottom5r <- dtas[!is.na(over(dtas, geometry(simd.bottom5r))),] + +# Add data from relevant SIMD polygons for each row in dtas DF +dtas.simd.bottom15r@data <- cbind(dtas.simd.bottom15r@data,over(dtas.simd.bottom15r,simd.bottom15r)) +dtas.simd.bottom10r@data <- cbind(dtas.simd.bottom10r@data,over(dtas.simd.bottom10r,simd.bottom10r)) +dtas.simd.bottom5r@data <- cbind(dtas.simd.bottom5r@data,over(dtas.simd.bottom5r,simd.bottom5r)) + +# Convert to dataframe, strip out unnecessary columns, and sort by rank +dtas.simd.bottom15r.tidy <- data.frame("name"=dtas.simd.bottom15r$name,"rank"=dtas.simd.bottom15r$rank) +dtas.simd.bottom15r.tidy <- dtas.simd.bottom15r.tidy[with(dtas.simd.bottom15r.tidy, order(rank)), ] +dtas.simd.bottom10r.tidy <- data.frame("name"=dtas.simd.bottom10r$name,"rank"=dtas.simd.bottom10r$rank) +dtas.simd.bottom10r.tidy <- dtas.simd.bottom10r.tidy[with(dtas.simd.bottom10r.tidy, order(rank)), ] +dtas.simd.bottom5r.tidy <- data.frame("name"=dtas.simd.bottom5r$name,"rank"=dtas.simd.bottom5r$rank) +dtas.simd.bottom5r.tidy <- dtas.simd.bottom5r.tidy[with(dtas.simd.bottom5r.tidy, order(rank)), ] + +# Export tables to PDF, with titles and plots ahead of each table (admin boundaries, add all points, add subset in red) +# End Part I +@ + +\subsection{DTAS Groups Located within the the lowest 15 percent SIMD ranked areas} + +\par +\begin{figure}[h] +<>= +plot(admin) +plot(dtas, add = TRUE) +plot(dtas.simd.bottom15r, add = TRUE, col="Red") +@ +\caption + {Map of all DTAS groups, with those located within 15 percent most deprived areas in red.} +\end{figure} + +<>= + +xt <- xtable(dtas.simd.bottom15r.tidy, caption = "table of DTAS groups in bottom 15%") + +print(xt, + tabular.environment = "longtable", + sanitize.colnames.function = function(x){x}, + include.rownames = FALSE, + floating = FALSE) +@ + +\section{Part II: Comparing presence of DTAS to other similar features} + +\par \\ +It may be useful to know the number of groups present, but we can illuminate this information by testing out how frequently different types of groups appear in these areas. + +<<>>= +# Part II - Calculate coincidence of other features + +# Load in POI table + +# I'm going to take some shortcuts here, preventing this code from being truly reproducible +# because the poi dataset is really huge - I've already downloaded and filtered to specific codes for features +# so code immediately below is untested. Will need to adjust below to the actual code I'm working with eventually. + +## Skip ==> +# poi <- read.csv("data/poi.csv", header = FALSE, sep = "|") +# coordinates(poi) <- c("Feature Easting", "Feature Northing") +# proj4string(poi) <- proj4string(CRS(bng)) +## transform CRS on churches (from BNG) to wgs84 for comparisons below +# poi.wgs <- spTransform(poi, CRS(wgs84)) + +## subsets +#poi.pubs <- poi[!is.na(poi[3] = "01020034")] +#poi.chequecashing <- poi[!is.na(poi[3] = "02090142")] +#poi.pawnbrokers <- poi[!is.na(poi[3] = "02090151")] +#poi.worship <- poi[!is.na(poi[3] = "06340459")] +# ==> to here for now + +# Load in retail data from geolytics dataset +# from here: https://geolytix.co.uk/?retail_points +poi.grocery <- read.csv("data/retailpoints_version11_dec17.txt", sep = "\t") +# select useful columns +poi.grocery <- subset(poi.grocery, select = c("retailer", "store_name", "long_wgs", "lat_wgs")) +# convert to spdf +coordinates(poi.grocery) <- c("long_wgs", "lat_wgs") +proj4string(poi.grocery) <- CRS("+init=epsg:4326") +# filter out non-Scottish data +poi.grocery <- poi.grocery[!is.na(over(poi.grocery, geometry(admin.wgs))),] + +# Load in British pubs from Ordnance survey dataset +poi.pubs <- read.csv("data/poi_pubs.csv", header = FALSE, sep = "|") +# select useful columns +poi.pubs <- subset(poi.pubs, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pubs) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pubs) <- c("x", "y") +proj4string(poi.pubs) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pubs.wgs <- spTransform(poi.pubs, CRS("+init=epsg:4326")) +# filter out non-Scottish pubs +poi.pubs.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(admin.wgs))),] + +# Load in check cashing from Ordnance survey dataset +poi.checkcashing <- read.csv("data/poi_chequecashing.csv", header = FALSE, sep = "|") +# select useful columns +poi.checkcashing <- subset(poi.checkcashing, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.checkcashing) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.checkcashing) <- c("x", "y") +proj4string(poi.checkcashing) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.checkcashing.wgs <- spTransform(poi.checkcashing, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.checkcashing.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(admin.wgs))),] + +# Load in pawnbrokers from Ordnance survey dataset +poi.pawnbrokers <- read.csv("data/poi_pawnbrokers.csv", header = FALSE, sep = "|") +# select useful columns +poi.pawnbrokers <- subset(poi.pawnbrokers, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pawnbrokers) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pawnbrokers) <- c("x", "y") +proj4string(poi.pawnbrokers) <- CRS("+init=epsg:27700") +# transform CRS on pawnbrokers (from BNG) to wgs84 for comparisons below +poi.pawnbrokers.wgs <- spTransform(poi.pawnbrokers, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) pawnbrokers +poi.pawnbrokers.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(admin.wgs))),] + +# Load in places of worship from Ordnance survey dataset +poi.pow <- read.csv("data/poi_pow.csv", header = FALSE, sep = "|") +# select useful columns +poi.pow <- subset(poi.pow, select = c("V1", "V2", "V3", "V4", "V5")) +# rename columns to tidier names +colnames(poi.pow) <- c("refnum", "name", "code", "x", "y") +coordinates(poi.pow) <- c("x", "y") +proj4string(poi.pow) <- CRS("+init=epsg:27700") +# transform CRS on pubs (from BNG) to wgs84 for comparisons below +poi.pow.wgs <- spTransform(poi.pow, CRS("+init=epsg:4326")) +# filter out non-Scottish (hah!) check cashing shops +poi.pow.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(admin.wgs))),] + +# create WGS versions of SIMD subsets from above for comparisons below +simd.bottom15r.wgs <- spTransform(simd.bottom15r, CRS("+init=epsg:4326")) +simd.bottom10r.wgs <- spTransform(simd.bottom10r, CRS("+init=epsg:4326")) +simd.bottom5r.wgs <- spTransform(simd.bottom5r, CRS("+init=epsg:4326")) + +# Test it out with a quick plot +# plot(poi.pubs.wgs) + +# run further subsetting, as above, for specific bands of simd +grocery.simd.bottom15r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom15r.wgs))),] +grocery.simd.bottom10r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom10r.wgs))),] +grocery.simd.bottom5r.wgs <- poi.grocery[!is.na(over(poi.grocery, geometry(simd.bottom5r.wgs))),] + +pubs.simd.bottom15r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom15r.wgs))),] +pubs.simd.bottom10r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom10r.wgs))),] +pubs.simd.bottom5r.wgs <- poi.pubs.wgs[!is.na(over(poi.pubs.wgs, geometry(simd.bottom5r.wgs))),] + +pawnbrokers.simd.bottom15r.wgs <- poi.pawnbrokers.wgs[!is.na(over(poi.pawnbrokers.wgs, geometry(simd.bottom15r.wgs))),] +checkcashing.simd.bottom15r.wgs <- poi.checkcashing.wgs[!is.na(over(poi.checkcashing.wgs, geometry(simd.bottom15r.wgs))),] +pow.simd.bottom15r.wgs <- poi.pow.wgs[!is.na(over(poi.pow.wgs, geometry(simd.bottom15r.wgs))),] +@ + +\par \\ +Let's begin with DTAS by itself. Measured against the whole of DTAS's network, those groups highlighted above represent + +<<>>= + +# Part II.2 Run some calculations on presence for various types of entities + +# Calculate total number of DTAS "community anchors" within bottom 15% +length(dtas.simd.bottom15r$rank) / length(dtas$name) +@ + +of the whole network. + +\par \\ +We can compare this frequency against some other features. The ordnance survey tells us that there are, for example, a totle of \Sexpr{length(poi.pubs.wgs$name)} pubs in Scotland. The total numnber of Pubs located within these deprived areas noted above is +<<>>= + +# Calculate percentage of total pubs in Scotland located within bottom 15% of IMD by rank +length(pubs.simd.bottom15r.wgs$name) / length(poi.pubs.wgs$name) +@ + +\par \\ +To take another example, there are (according to the Ordnance Survey \Sexpr{length(poi.pow.wgs$name)} places of worship in Scotland. Of all these, our deprived areas have: +<<>>= + +# places of worship +length(pow.simd.bottom15r.wgs$name) / length(poi.pow.wgs$name) +@ + +\par \\ +Another example we can look at is retail grocery stores. A geospatial company called GeoLytix publishes a complete set of British retailers on a regular basis. As of last November, they indicated that there were \Sexpr{length(poi.grocery$store_name)} grocery stores in Scotland. Within our deprived areas, this represents + +<<>>= + +# Calculate percentage of total retail grocers in Scotland +# located within bottom 15% of IMD by rank +length(grocery.simd.bottom15r.wgs$store_name) / length(poi.grocery$store_name) +# drill a bit deeper - morrisons stores +morrisons.scotland <- poi.grocery[poi.grocery$retailer == "Morrisons", ] +morrisons.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Morrisons", ] + +@ + +\par \\ +We can break this down by brand to get a more interesting indication. There are \Sexpr{length(morrisons.scotland)} Morrisons brand stores in Scotland. Of these, the following percentage are located in our deprived areas: + +<<>>= +length(morrisons.bottom15r.scotland$store_name) / length(morrisons.scotland) +# sainsburys +sainsburys.scotland <- poi.grocery[poi.grocery$retailer == "Sainsburys", ] +sainsburys.bottom15r.scotland <- grocery.simd.bottom15r.wgs[grocery.simd.bottom15r.wgs$retailer == "Sainsburys", ] +@ + +\par \\ +We find a contrast by turning to Sainsburys. There are \Sexpr{length(sainsburys.scotland)} of these in Scotland. Represented in our deprived areas are just: +<<>>= +length(sainsburys.bottom15r.scotland$store_name) / length(sainsburys.scotland) +@ + +\par \\ +Sadly, the best bellweather of a deprived area in Scotland isn't any of the above, but rather pawnbrokers (\Sexpr{length(poi.pawnbrokers.wgs$name)}) and check cashing stores (\Sexpr{length(poi.checkcashing.wgs$name)}). You can see these represented on the map below. Of check cashing stores, we find a representation in our areas of \Sexpr{length(checkcashing.simd.bottom15r.wgs$name) / length(poi.checkcashing.wgs$name)}. And pawnbrokers at \Sexpr{length(pawnbrokers.simd.bottom15r.wgs$name) / length(poi.pawnbrokers.wgs$name)} + +<<>>= +# check cashing +plot(admin) +plot(poi.checkcashing.wgs, add = TRUE) +plot(checkcashing.simd.bottom15r.wgs, col="blue", add = TRUE) +@ + + + +\end{document} \ No newline at end of file