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57
mapping_draft.Rmd
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@ -37,6 +37,8 @@ knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FA
## Default repo
setwd("/Users/jeremy/gits/mapping_environmental_action")
# Set repository to be new standard, e.g. cloud server.
# This will avoid a dialogue box if packages are to be installed for below on first run.
local({r <- getOption("repos")
r["CRAN"] <- "https://cloud.r-project.org"
options(repos=r)
@ -73,8 +75,10 @@ if (dir.exists("derivedData") == FALSE) {
}
# Define Coordinate Reference Systems (CRS) for later use
# Note: I've used British National Grid (27000) in this paper, but have found that it is falling out
# of use in many cases, so will be defaulting to WGS84 in future data-sets and papers
# Note: I've used British National Grid (27000) in this paper, but have found that
# 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'
@ -116,7 +120,7 @@ For the sake of comparison, we also measured the geographical footprint of two o
# Technical Background
Analysis was conducted using QGIS 2.8 and R `r getRversion()`, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics May 2014 postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
Analysis was conducted using QGIS 2.8 and R `r getRversion()`, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
# Background and History of Eco-Congregation Scotland
@ -130,6 +134,7 @@ ecs$registration <- as.Date(ecs$registration, "%Y-%m-%d")
# ecs$award2 <- as.Date(ecs$award2)
# ecs$award3 <- as.Date(ecs$award3)
# ecs$award4 <- as.Date(ecs$award4)
# TODO: add "R" to command in paragraph below once this is resolved
ecs_complete_cases <- ecs[complete.cases(ecs$year_begun),]
```
@ -138,7 +143,7 @@ The programme launched officially in 2001 at Dunblane Cathedral in Stirling and
In the case of Eco-Congregation Scotland, congregations are invited to begin by "registering" their interest in the programme by completing a basic one-sided form. The next step requires the completion of an award application, which includes a facilitated curriculum called a "church check-up" and after an application is submitted, the site is visited and assessed by third-party volunteer assessors. Sites are invited to complete additional applications for further awards which are incremental (as is the application process). Transition communities, at least in the period reflected on their map, go through a similar process (though this does not involve the use of a supplied curriculum) by which they are marked first as "interested," become "active" and then gain "official" status.[^1554162]
# Representation by Regional Authorities (Council Areas)
```{r import_admin_data}
```{r import_admin_data, include=FALSE}
# read in polygon for Scottish admin boundaries
# TODO: upload bundle of admin data to new zenodo repository and alter below to use new URLs
# TODO: need to remove readOGR below once st_read is confirmed to be working as sf
@ -292,20 +297,24 @@ Perhaps the first important question to ask of these groups is, where are they?
```{r admin_ecs_choropleth}
# TODO:
# 1. Need to rewrite create_admin_ecs_choropleth to use ggplot
# 2. Need to augment plots to display in 2x2 plots (side-by-side comparison
# 1. Need to augment plots to display in 2x2 plots (side-by-side comparison
# with column 1 normalised by population, i.e. admin_lev1_pop; column 2 normalised by church counts.
# Row 1 plot using polygons from admin_lev1 and row 2 plot using ploygons from admin_lev2
# 3. Need to clip choropleth polygons to buildings shapefile
# 2. Need to clip choropleth polygons to buildings shapefile
# tidy data
# admin_lev1_gathered <- gather(admin_lev1_sf, value="number", ecs_count)
# tidy data to select just ecs_count data
admin_lev1_gathered <- gather(admin_lev1_sf, value="number", ecs_count)
# plot simple choropleth, as here: https://www.r-graph-gallery.com/327-chloropleth-map-from-geojson-with-ggplot2/
# plot simple choropleth map using ECS data points, as here: https://www.r-graph-gallery.com/327-chloropleth-map-from-geojson-with-ggplot2/
# myplot <- ggplot() + geom_sf(data = admin_lev1_sf) + geom_point(data=as.data.frame(ecs), aes(x=X, y=Y))
# ggplot() +
# geom_polygon(data = admin_lev1, aes( x = long, y = lat, group = group)) +
# theme_void() +
# coord_map()
# plot as
myplot <- ggplot() + geom_sf(data = admin_lev1_sf) + geom_point(data=as.data.frame(ecs), aes(x=X, y=Y))
# save plot to PDF (for testing)
# ggsave("figures/admin_choropleth_ecs.pdf")
```
@ -336,14 +345,17 @@ admin.df <- data.frame(admin_lev1)
# Goal here is to generate a grouped bar plot; https://www.r-graph-gallery.com/48-grouped-barplot-with-ggplot2/
# Need to flatten admin_lev1 based on all the count columns and generate using ggplot
admin.df_gathered <- gather(admin.df, key="group_type", value="number", ecs_count, transition_count, dtas_count)
admin_gathered <- gather(data.frame(admin_lev1), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count)
# ggplot(admin.df_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity")
# ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) + geom_bar() + coord_flip()
ggplot(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity") + coord_flip()
# add bubbleplot on map, as here: https://www.r-graph-gallery.com/330-bubble-map-with-ggplot2/ or here: https://www.r-graph-gallery.com/177-map-with-proportional-symbols/
ggplot() +
geom_polygon(data=admin_lev1, aes(x=long, y=lat, group=group), fill="grey", alpha=0.3) +
geom_point(data=as.data.frame(ecs), aes(x=X, y=Y)) +
theme_void() + ylim(50,59) + coord_map()
```
We can compare the representation in these various regions against our comparison groups to see how other community-based organisations cluster in Scottish administrative districts. Here there are some significant contrasts. Scottish Community Development trusts are most intensely concentrated in the Highlands and Argyll & Bute. But, this is consistent with all the other categories, Eco-Congregations, Places of Worship, and dtass are all over-represented in this area, varying only by the degree. Edinburgh is different, here we find that Eco-Congregations and Transition projects are over-represented, while dtass are under-represented. Finally, the highlands are another strong contrast, here we find a very strong over-representation by transition towns and dtass while the representation of Eco-Congregations is relatively close to the population share for that area. The two areas of greatest contrast for Eco-Congregations from the other groups are unsurprising, Edinburgh is the location of the ECS offices, while Stirling is the area in which ECS first began (see Appendix B for full data).
@ -362,20 +374,19 @@ todo: plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
# TODO: uncomment admin.shortened lines below
# admin.shortened <- admin_lev1[,c(7,12:22)]
# Output CSV files for various levels of admin
write.csv(admin_lev1, "derivedData/admin)lev1.csv", row.names=FALSE)
write.csv(admin_lev1, "derivedData/admin_lev1.csv", row.names=FALSE)
write.csv(admin_lev2, "derivedData/admin_lev2.csv", row.names=FALSE)
# write.csv(admin_lev3, "derivedData/admin_lev3.csv", row.names=FALSE)
# write.csv(admin.shortened, "derivedData/admin.csv", row.names=FALSE)
write.csv(as_data_frame(admin_lev1[,c(3,5,7,11,13)]), "derivedData/admin.csv", row.names=FALSE)
write.csv(ecs, "derivedData/ecs.csv", row.names=FALSE)
write.csv(transition, "derivedData/transition.csv", row.names=FALSE)
write.csv(permaculture, "derivedData/permaculture.csv", row.names=FALSE)
write.csv(dtas, "derivedData/dtas.csv", row.names=FALSE)
## write.csv(simd, "derivedData/simd.csv", row.names=FALSE)
#admin.shortened<-data.frame(admin.shortened)
## Output mmd tables using pander
#panderOptions("digits", 2)
#pander(admin.shortened)
panderOptions("digits", 2)
pander(as_data_frame(admin_lev1[,c(3,5,7,11,13)]))
```
@ -394,5 +405,3 @@ write.csv(dtas, "derivedData/dtas.csv", row.names=FALSE)
[^1554162]:From the Transition map key, "Green pins are 'official' groups
Blue pins are active communities who are connected to the Scottish Transition network Yellow pins show interest in this area"
[^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited.
[^159142242]: Fiona Tweedia, *Ecumenical Audit: Questionnaire Findings* (2014).
[^15914204]:See note above regarding the data used from the PointX POI database. Note, for our research,we filtered out religious groups not represented within the Eco-Congregation footprint. We discuss representation by tradition and religion further below.

355
mapping_draft.html
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172
mapping_draft.md
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@ -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**: January 26, 2019
# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: January 27, 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: “2019-01-26”
date: “2019-01-27”
bibliography: /Users/jeremy/Dropbox/bibtex/everything.bib
linkcolor: black
geometry: margin=1in
@ -56,7 +56,7 @@ For the sake of comparison, we also measured the geographical footprint of two o
# Technical Background
Analysis was conducted using QGIS 2.8 and R 3.5.1, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics May 2014 postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
Analysis was conducted using QGIS 2.8 and R 3.5.2, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
# Background and History of Eco-Congregation Scotland
@ -70,29 +70,8 @@ In the case of Eco-Congregation Scotland, congregations are invited to begin by
# Representation by Regional Authorities (Council Areas)
```
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "scotland_ca_2010"
## with 32 features
## It has 4 fields
```
```
## Reading layer `scotland_ca_2010' from data source `/Users/jeremy/gits/mapping_environmental_action/data/scotland_ca_2010.shp' using driver `ESRI Shapefile'
## Simple feature collection with 32 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 5513 ymin: 530249 xmax: 470332 ymax: 1220302
## epsg (SRID): NA
## proj4string: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +datum=OSGB36 +units=m +no_defs
```
```
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "scotland_parlcon_2011"
## with 73 features
## It has 3 fields
```
Perhaps the first important question to ask of these groups is, where are they? I calculated the spread of eco-congregations and transition groups across each of the 32 council areas in Scotland. Every council area in Scotland has at least one eco-congregation or transition group). The most are located in , with 48, whereas the mean among all the 32 council areas is 10.75, with a median of 8, standard deviation of 9.4698162, and interquartile range of 11.5. The following choropleth maps show the relative concentration of eco-congregations (indicated by yellow to red).
@ -112,7 +91,7 @@ Turning to the total of 4048 "places of worship" in Scotland, we find a slightly
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*.
![](figures/create_admin_barplot-1.png)<!-- -->
We can compare the representation in these various regions against our comparison groups to see how other community-based organisations cluster in Scottish administrative districts. Here there are some significant contrasts. Scottish Community Development trusts are most intensely concentrated in the Highlands and Argyll & Bute. But, this is consistent with all the other categories, Eco-Congregations, Places of Worship, and dtass are all over-represented in this area, varying only by the degree. Edinburgh is different, here we find that Eco-Congregations and Transition projects are over-represented, while dtass are under-represented. Finally, the highlands are another strong contrast, here we find a very strong over-representation by transition towns and dtass while the representation of Eco-Congregations is relatively close to the population share for that area. The two areas of greatest contrast for Eco-Congregations from the other groups are unsurprising, Edinburgh is the location of the ECS offices, while Stirling is the area in which ECS first began (see Appendix B for full data).
@ -122,10 +101,149 @@ We can compare the representation in these various regions against our compariso
[Figure 4, normalised by PointX data; also including agricultural parishes etc. as above]
# plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
todo: plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
# Appendix A
----------------------------------------------------------------
name ecs_count pow_count dtas_count
--------------------------- ----------- ----------- ------------
Fife 18 243 11
South Ayrshire 3 68 1
Inverclyde 2 53 1
Aberdeen City 15 96 1
Highland 21 435 34
West Dunbartonshire 6 52 3
Moray 11 103 6
Orkney Islands 4 50 10
Aberdeenshire 19 244 15
East Renfrewshire 8 37 2
Clackmannanshire 3 30 1
East Ayrshire 4 68 6
North Lanarkshire 5 187 1
Dundee City 3 94 1
Argyll and Bute 18 172 24
Angus 12 106 0
Glasgow City 25 329 21
East Dunbartonshire 7 43 1
Shetland Islands 3 89 4
Scottish Borders 11 153 9
Comhairle nan Eilean Siar 0 114 9
Renfrewshire 6 84 5
North Ayrshire 7 96 3
East Lothian 8 71 2
Falkirk 8 83 0
West Lothian 11 70 7
Perth and Kinross 20 162 10
Dumfries and Galloway 7 189 15
Stirling 13 73 10
City of Edinburgh 48 233 7
Midlothian 1 45 4
South Lanarkshire 17 176 8
----------------------------------------------------------------
Table: Table continues below
--------------------
permaculture_count
--------------------
1
0
0
0
1
0
0
0
1
0
0
0
0
0
3
0
2
0
0
2
0
0
0
0
0
0
1
2
0
0
1
0
--------------------
@ -143,5 +261,3 @@ We can compare the representation in these various regions against our compariso
[^1554162]:From the Transition map key, "Green pins are 'official' groups
Blue pins are active communities who are connected to the Scottish Transition network Yellow pins show interest in this area"
[^15571030]:This was calculated by calculating a 10m wide footprint for every postcode in Scotland, areas which are not within 10m of a postcode (as of May 2014) are counted as uninhabited.
[^159142242]: Fiona Tweedia, *Ecumenical Audit: Questionnaire Findings* (2014).
[^15914204]:See note above regarding the data used from the PointX POI database. Note, for our research,we filtered out religious groups not represented within the Eco-Congregation footprint. We discuss representation by tradition and religion further below.

5
mapping_draft_part2.Rmd
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@ -269,3 +269,8 @@ write.csv(urbanrural.shortened, "derivedData/urbanrural.csv", row.names=FALSE)
urbanrural.shortened<-data.frame(urbanrural.shortened)
panderOptions("digits", 2)
pander(urbanrural.shortened)
[^159142242]: Fiona Tweedia, *Ecumenical Audit: Questionnaire Findings* (2014).
[^15914204]:See note above regarding the data used from the PointX POI database. Note, for our research,we filtered out religious groups not represented within the Eco-Congregation footprint. We discuss representation by tradition and religion further below.