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working with new enhanced ecs dataset, changed to dtas

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Jeremy Kidwell 2018-09-11 14:57:53 +01:00
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mapping_draft.Rmd
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@ -43,6 +43,16 @@ if (dir.exists("figures") == FALSE) {
if (dir.exists("derivedData") == FALSE) {
dir.create("derivedData")
}
# 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
# TODO: make canonical CRS definitions and use consistently; remove proj4string(admin_lev1) and other similar instances below
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"
```
# Introduction[^15541312]
@ -54,22 +64,12 @@ Until recently, environmentalism has been treated by governments and environment
```{r load_ecs_data}
# read in Eco-Congregation Scotland data and-------------------
# ...turn it into a SpatialPointsDataFrame---------------------
# TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository
# 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="#")
# actual command is the following------------------------------
# TODO: convert from sp() to sf() which will look something like:
# ecs <- st_read(system.file("pow_osm.gpkg", package = "spData"))
ecs <- read.csv(text=getURL("https://zenodo.org/record/159155/files/ECS-GIS-Locations_2.9_simplified.csv"))
ecs <- read.csv(text=getURL("https://zenodo.org/record/1413333/files/ECS-GIS-Locations_2.9_enhanced.csv"))
coordinates(ecs) <- c("X", "Y")
# Define CRS for here and later--------------------------------
# TODO: make canonical CRS definitions and use consistently; remove proj4string(admin_lev1) below
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"
# set CRS to be British National Grid (27000) ---------------------------
# note: I've used BNG 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--------------------------------
proj4string(ecs) = CRS(bng)
```
@ -85,7 +85,7 @@ For the sake of this analysis, we took each Eco-Congregation Scotland site to re
- Relation to the 8-Fold Scottish Government Urban-Rural Scale
- Proximity to "wilderness" (based on several different designations)
For the sake of comparison, we also measured the geographical footprint of two other forms of community group in Scotland, (1) Transition Towns (taking into account their recent merge with Scotland Communities Climate Action Network) and (2) Scottish Community Development Trusts ("SCDT"). These two groups provide a helpful basis for comparison as they are not centralised and thus have a significant geographical dispersion across Scotland. They also provide a useful comparison as transition is a (mostly) non-religious environmental movement, and community development trusts are not explicitly linked to environmental conservation (though this is often part of their remit), so we have a non-religious point of comparison in Transition and a non-environmental point of comparison with SCDT.
For the sake of comparison, we also measured the geographical footprint of two other forms of community group in Scotland, (1) Transition Towns (taking into account their recent merge with Scotland Communities Climate Action Network) and (2) member groups of the Development Trust Association Scotland ("DTAS"). These two groups provide a helpful basis for comparison as they are not centralised and thus have a significant geographical dispersion across Scotland. They also provide a useful comparison as transition is a (mostly) non-religious environmental movement, and community development trusts are not explicitly linked to environmental conservation (though this is often part of their remit), so we have a non-religious point of comparison in Transition and a non-environmental point of comparison with DTAS
# Technical Background
@ -96,23 +96,18 @@ Analysis was conducted using QGIS 2.8 and R Studio 0.99.893, and data-sets were
Eco-Congregation Scotland began a year before the official launch of Eco-Congregation England and Wales, in 1999, as part of an effort by Kippen Environment Centre (later renamed to Forth Environment Link, or "FEL") a charity devoted to environmental education in central Scotland[^158261210] to broaden the scope of its environmental outreach to churches in central Scotland.[^15826124] Initial funding was provided, through Kippen Environment Centre by way of a "sustainable action grant" (with funds drawn from a government landfill tax) through a government programme called Keep Scotland Beautiful (the Scottish cousin of Keep Britain Tidy). After this initial pilot project concluded, the Church of Scotland provided additional funding for the project in the form of staff time and office space. Additional funding a few years later from the Scottish Government helped subsidise the position of a business manager, and in 2011 the United Reformed Church contributed additional funding which subsidised the position of a full-time environmental chaplain for a 5-year term, bringing the total staff to five.
```{r calculate_ecs_by_year}
# TODO: upload ECS-GIS-Locations_2.9_extended.csv to new zenodo repository, alter below to use CURL
ecs_complete_cases <- ecs[complete.cases(ecs$year_begun),]
# Read in longer csv with award dates
ecs_full <- read.csv("data/ECS-GIS-Locations_2.9_tidy.csv")
ecs <- read.csv("data/ECS-GIS-Locations_2.9_tidy.csv")
# Tidy up date fields and convert to date data type
ecs_full$registration <- as.Date(ecs_full$registration, "%d/%m/%Y")
ecs_full$award1 <- strptime(as.character(ecs_full$award1), "%d/%m/%Y")
ecs_full$award1 <- as.Date(ecs_full$award1)
ecs_full$award2 <- strptime(as.character(ecs_full$award2), "%d/%m/%Y")
ecs_full$award2 <- as.Date(ecs_full$award2)
ecs_full$award3 <- strptime(as.character(ecs_full$award3), "%d/%m/%Y")
ecs_full$award3 <- as.Date(ecs_full$award3)
ecs_full$award4 <- strptime(as.character(ecs_full$award4), "%d/%m/%Y")
ecs_full$award4 <- as.Date(ecs_full$award4)
ecs$registration <- as.Date(ecs$registration, "%d/%m/%Y")
ecs$award1 <- as.Date(ecs$award1)
ecs$award2 <- as.Date(ecs$award2)
ecs$award3 <- as.Date(ecs$award3)
ecs$award4 <- as.Date(ecs$award4)
```
The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2006, ])` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2012, ])` and the number of awarded congregations had quadrupled to `sum(ecs_full$award1 < "2012-01-01", na.rm=TRUE)`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org).
The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2006, ])` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2012, ])` and the number of awarded congregations had quadrupled to `sum(ecs$award1 < "2012-01-01", na.rm=TRUE)`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org).
In the case of Eco-Congregation Scotland, congregations are invited to begin by "registering" their interest in the programme by completing a basic one-sided form. The next step requires the completion of an award application, which includes a facilitated curriculum called a "church check-up" and after an application is submitted, the site is visited and assessed by third-party volunteer assessors. Sites are invited to complete additional applications for further awards which are incremental (as is the application process). Transition communities, at least in the period reflected on their map, go through a similar process (though this does not involve the use of a supplied curriculum) by which they are marked first as "interested," become "active" and then gain "official" status.[^1554162]
@ -163,9 +158,9 @@ coordinates(pow_pointX) <- c("feature_easting", "feature_northing")
proj4string(pow_pointX) <- proj4string(admin_lev1)
# read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame
scdt <- read.csv("data/community-dev-trusts-2.6.csv")
coordinates(scdt) <- c("X", "Y")
proj4string(scdt) <- proj4string(admin_lev1)
dtas <- read.csv("data/community-dev-trusts-2.6.csv")
coordinates(dtas) <- c("X", "Y")
proj4string(dtas) <- proj4string(admin_lev1)
# read in permaculture data and turn it into a SpatialPointsDataFrame
permaculture <- read.csv("data/permaculture_scot-0.8.csv")
@ -186,9 +181,9 @@ admin_lev1$pow_percent<- prop.table(admin_lev1$pow_count)
# calculate count of Transition for fields in admin and provide percentages
admin_lev1$transition_count <- poly.counts(transition,admin_lev1)
admin_lev1$transition_percent<- prop.table(admin_lev1$transition_count)
# calculate count of SCDT for fields in admin and provide percentages
admin_lev1$scdt_count <- poly.counts(scdt,admin_lev1)
admin_lev1$scdt_percent<- prop.table(admin_lev1$scdt_count)
# calculate count of dtas for fields in admin and provide percentages
admin_lev1$dtas_count <- poly.counts(dtas,admin_lev1)
admin_lev1$dtas_percent<- prop.table(admin_lev1$dtas_count)
# calculate count of permaculture for fields in admin and provide percentages
admin_lev1$permaculture_count <- poly.counts(permaculture,admin_lev1)
admin_lev1$permaculture_percent<- prop.table(admin_lev1$permaculture_count)
@ -204,9 +199,9 @@ admin_lev2$pow_percent<- prop.table(admin_lev2$pow_count)
# calculate count of Transition for fields in admin_lev2 and provide percentages
admin_lev2$transition_count <- poly.counts(transition,admin_lev2)
admin_lev2$transition_percent<- prop.table(admin_lev2$transition_count)
# calculate count of SCDT for fields in admin_lev2 and provide percentages
admin_lev2$scdt_count <- poly.counts(scdt,admin_lev2)
admin_lev2$scdt_percent<- prop.table(admin_lev2$scdt_count)
# calculate count of dtas for fields in admin_lev2 and provide percentages
admin_lev2$dtas_count <- poly.counts(dtas,admin_lev2)
admin_lev2$dtas_percent<- prop.table(admin_lev2$dtas_count)
# calculate count of permaculture for fields in admin_lev2 and provide percentages
admin_lev2$permaculture_count <- poly.counts(permaculture,admin_lev2)
admin_lev2$permaculture_percent<- prop.table(admin_lev2$permaculture_count)
@ -222,9 +217,9 @@ admin_lev3$pow_percent<- prop.table(admin_lev3$pow_count)
# calculate count of Transition for fields in admin_lev3 and provide percentages
admin_lev3$transition_count <- poly.counts(transition,admin_lev3)
admin_lev3$transition_percent<- prop.table(admin_lev3$transition_count)
# calculate count of SCDT for fields in admin_lev3 and provide percentages
admin_lev3$scdt_count <- poly.counts(scdt,admin_lev3)
admin_lev3$scdt_percent<- prop.table(admin_lev3$scdt_count)
# calculate count of dtas for fields in admin_lev3 and provide percentages
admin_lev3$dtas_count <- poly.counts(dtas,admin_lev3)
admin_lev3$dtas_percent<- prop.table(admin_lev3$dtas_count)
# calculate count of permaculture for fields in admin_lev3 and provide percentages
admin_lev3$permaculture_count <- poly.counts(permaculture,admin_lev3)
admin_lev3$permaculture_percent<- prop.table(admin_lev3$permaculture_count)
@ -381,7 +376,7 @@ Given the way population and places of worship are unevenly distributed across S
admin_lev1$ecs_pop_factor <- ((admin_lev1$ecs_percent - admin_lev1$pop_percent) / admin_lev1$pop_percent)*2
admin_lev1$ecs_pow_factor <- ((admin_lev1$ecs_percent - admin_lev1$pow_percent) / admin_lev1$pow_percent)*2
admin_lev1$ecs_transition_factor <- ((admin_lev1$ecs_percent - admin_lev1$transition_percent) / admin_lev1$transition_percent)*2
admin_lev1$ecs_scdt_factor <- ((admin_lev1$ecs_percent - admin_lev1$scdt_percent) / admin_lev1$scdt_percent)*2
admin_lev1$ecs_dtas_factor <- ((admin_lev1$ecs_percent - admin_lev1$dtas_percent) / admin_lev1$dtas_percent)*2
```
@ -398,7 +393,7 @@ admin.df<-data.frame(admin_lev1)
qplot(admin_lev1$ecs_count, admin_lev1$transition_count data=admin, geom = "bar")
```
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 SCDTs 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 SCDTs are under-represented. Finally, the highlands are another strong contrast, here we find a very strong over-representation by transition towns and SCDTs 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).
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).
![][Figure3b]
@ -438,9 +433,9 @@ urbanrural$pow_percent<- prop.table(urbanrural$pow_count)
# calculate count of Transition for fields in urbanrural
urbanrural$transition_count <- poly.counts(transition,urbanrural)
urbanrural$transition_percent<- prop.table(urbanrural$transition_count)
# calculate count of SCDT for fields in urbanrural
urbanrural$scdt_count <- poly.counts(scdt,urbanrural)
urbanrural$scdt_percent<- prop.table(urbanrural$scdt_count)
# calculate count of dtas for fields in urbanrural
urbanrural$dtas_count <- poly.counts(dtas,urbanrural)
urbanrural$dtas_percent<- prop.table(urbanrural$dtas_count)
# calculate count of permaculture for fields in urbanrural
urbanrural$permaculture_count <- poly.counts(permaculture,urbanrural)
urbanrural$permaculture_percent<- prop.table(urbanrural$permaculture_count)
@ -519,10 +514,10 @@ transition@data=cbind(transition@data,over(transition,simd))
cbind(permaculture@data, over(permaculture, simd))
# assign combined table with SIMD columns to attribute table slot of permaculture table
permaculture@data=cbind(permaculture@data,over(permaculture,simd))
# examine which scdt fall within each SIMD classifications
cbind(scdt@data, over(scdt, simd))
# assign combined table with SIMD columns to attribute table slot of scdt table
scdt@data=cbind(scdt@data,over(scdt,simd))
# examine which dtas fall within each SIMD classifications
cbind(dtas@data, over(dtas, simd))
# assign combined table with SIMD columns to attribute table slot of dtas table
dtas@data=cbind(dtas@data,over(dtas,simd))
# STAGE 2, extract NULL cells from each data set to prevent errors in stage 3
@ -554,13 +549,13 @@ coordinates(permaculture) <- c("X", "Y")
proj4string(permaculture) <- proj4string(admin_lev1)
# convert back to data frame for null cell extraction
scdt<-data.frame(scdt)
dtas<-data.frame(dtas)
# split out null and normal cells
scdt_clean<-scdt[complete.cases(scdt),]
scdt_null<-scdt[!complete.cases(scdt),]
dtas_clean<-dtas[complete.cases(dtas),]
dtas_null<-dtas[!complete.cases(dtas),]
# convert back to spatialpointdataframe
coordinates(scdt) <- c("X", "Y")
proj4string(scdt) <- proj4string(admin_lev1)
coordinates(dtas) <- c("X", "Y")
proj4string(dtas) <- proj4string(admin_lev1)
# STAGE 3a, calculate sums based on SIMD12R columns and generate new integer sets with quintile count data
simd_rownames = c("Quintile 1","Quintile 2","Quintile 3","Quintile 4","Quintile 5")
@ -570,61 +565,61 @@ simdr12_transition = c((sum(transition_clean$SIMDR12<1301)), (sum(transition_cle
# names(simdr12_transition) <- simd_rownames
simdr12_permaculture = c((sum(permaculture_clean$SIMDR12<1301)), (sum(permaculture_clean$SIMDR12 > 1300 & permaculture_clean$SIMDR12 < 2602)), (sum(permaculture_clean$SIMDR12 > 2601 & permaculture_clean$SIMDR12 < 3903)), (sum(permaculture_clean$SIMDR12 > 3902 & permaculture_clean$SIMDR12 < 5204)), (sum(permaculture_clean$SIMDR12 > 5203 & permaculture_clean$SIMDR12 < 6505)))
# names(simdr12_permaculture) <- simd_rownames
simdr12_scdt = c((sum(scdt_clean$SIMDR12<1301)), (sum(scdt_clean$SIMDR12 > 1300 & scdt_clean$SIMDR12 < 2602)), (sum(scdt_clean$SIMDR12 > 2601 & scdt_clean$SIMDR12 < 3903)), (sum(scdt_clean$SIMDR12 > 3902 & scdt_clean$SIMDR12 < 5204)), (sum(scdt_clean$SIMDR12 > 5203 & scdt_clean$SIMDR12 < 6505)))
# names(simdr12_scdt) <- simd_rownames
simdr12_dtas = c((sum(dtas_clean$SIMDR12<1301)), (sum(dtas_clean$SIMDR12 > 1300 & dtas_clean$SIMDR12 < 2602)), (sum(dtas_clean$SIMDR12 > 2601 & dtas_clean$SIMDR12 < 3903)), (sum(dtas_clean$SIMDR12 > 3902 & dtas_clean$SIMDR12 < 5204)), (sum(dtas_clean$SIMDR12 > 5203 & dtas_clean$SIMDR12 < 6505)))
# names(simdr12_dtas) <- simd_rownames
# STAGE 3b, calculate sums based on INCR12 columns and generate new integer sets with quintile count data
incr12_ecs = c((sum(ecs_clean$INCR12<1301)), (sum(ecs_clean$INCR12 > 1300 & ecs_clean$INCR12 < 2602)), (sum(ecs_clean$INCR12 > 2601 & ecs_clean$INCR12 < 3903)), (sum(ecs_clean$INCR12 > 3902 & ecs_clean$INCR12 < 5204)), (sum(ecs_clean$INCR12 > 5203 & ecs_clean$INCR12 < 6505)))
incr12_transition = c((sum(transition_clean$INCR12<1301)), (sum(transition_clean$INCR12 > 1300 & transition_clean$INCR12 < 2602)), (sum(transition_clean$INCR12 > 2601 & transition_clean$INCR12 < 3903)), (sum(transition_clean$INCR12 > 3902 & transition_clean$INCR12 < 5204)), (sum(transition_clean$INCR12 > 5203 & transition_clean$INCR12 < 6505)))
incr12_permaculture = c((sum(permaculture_clean$INCR12<1301)), (sum(permaculture_clean$INCR12 > 1300 & permaculture_clean$INCR12 < 2602)), (sum(permaculture_clean$INCR12 > 2601 & permaculture_clean$INCR12 < 3903)), (sum(permaculture_clean$INCR12 > 3902 & permaculture_clean$INCR12 < 5204)), (sum(permaculture_clean$INCR12 > 5203 & permaculture_clean$INCR12 < 6505)))
incr12_scdt = c((sum(scdt_clean$INCR12<1301)), (sum(scdt_clean$INCR12 > 1300 & scdt_clean$INCR12 < 2602)), (sum(scdt_clean$INCR12 > 2601 & scdt_clean$INCR12 < 3903)), (sum(scdt_clean$INCR12 > 3902 & scdt_clean$INCR12 < 5204)), (sum(scdt_clean$INCR12 > 5203 & scdt_clean$INCR12 < 6505)))
incr12_dtas = c((sum(dtas_clean$INCR12<1301)), (sum(dtas_clean$INCR12 > 1300 & dtas_clean$INCR12 < 2602)), (sum(dtas_clean$INCR12 > 2601 & dtas_clean$INCR12 < 3903)), (sum(dtas_clean$INCR12 > 3902 & dtas_clean$INCR12 < 5204)), (sum(dtas_clean$INCR12 > 5203 & dtas_clean$INCR12 < 6505)))
# STAGE 3c, calculate sums based on EMPR12 columns and generate new integer sets with quintile count data
empr12_ecs = c((sum(ecs_clean$EMPR12<1301)), (sum(ecs_clean$EMPR12 > 1300 & ecs_clean$EMPR12 < 2602)), (sum(ecs_clean$EMPR12 > 2601 & ecs_clean$EMPR12 < 3903)), (sum(ecs_clean$EMPR12 > 3902 & ecs_clean$EMPR12 < 5204)), (sum(ecs_clean$EMPR12 > 5203 & ecs_clean$EMPR12 < 6505)))
empr12_transition = c((sum(transition_clean$EMPR12<1301)), (sum(transition_clean$EMPR12 > 1300 & transition_clean$EMPR12 < 2602)), (sum(transition_clean$EMPR12 > 2601 & transition_clean$EMPR12 < 3903)), (sum(transition_clean$EMPR12 > 3902 & transition_clean$EMPR12 < 5204)), (sum(transition_clean$EMPR12 > 5203 & transition_clean$EMPR12 < 6505)))
empr12_permaculture = c((sum(permaculture_clean$EMPR12<1301)), (sum(permaculture_clean$EMPR12 > 1300 & permaculture_clean$EMPR12 < 2602)), (sum(permaculture_clean$EMPR12 > 2601 & permaculture_clean$EMPR12 < 3903)), (sum(permaculture_clean$EMPR12 > 3902 & permaculture_clean$EMPR12 < 5204)), (sum(permaculture_clean$EMPR12 > 5203 & permaculture_clean$EMPR12 < 6505)))
empr12_scdt = c((sum(scdt_clean$EMPR12<1301)), (sum(scdt_clean$EMPR12 > 1300 & scdt_clean$EMPR12 < 2602)), (sum(scdt_clean$EMPR12 > 2601 & scdt_clean$EMPR12 < 3903)), (sum(scdt_clean$EMPR12 > 3902 & scdt_clean$EMPR12 < 5204)), (sum(scdt_clean$EMPR12 > 5203 & scdt_clean$EMPR12 < 6505)))
empr12_dtas = c((sum(dtas_clean$EMPR12<1301)), (sum(dtas_clean$EMPR12 > 1300 & dtas_clean$EMPR12 < 2602)), (sum(dtas_clean$EMPR12 > 2601 & dtas_clean$EMPR12 < 3903)), (sum(dtas_clean$EMPR12 > 3902 & dtas_clean$EMPR12 < 5204)), (sum(dtas_clean$EMPR12 > 5203 & dtas_clean$EMPR12 < 6505)))
# STAGE 3d, calculate sums based on HER12 columns and generate new integer sets with quintile count data
her12_ecs = c((sum(ecs_clean$HER12<1301)), (sum(ecs_clean$HER12 > 1300 & ecs_clean$HER12 < 2602)), (sum(ecs_clean$HER12 > 2601 & ecs_clean$HER12 < 3903)), (sum(ecs_clean$HER12 > 3902 & ecs_clean$HER12 < 5204)), (sum(ecs_clean$HER12 > 5203 & ecs_clean$HER12 < 6505)))
her12_transition = c((sum(transition_clean$HER12<1301)), (sum(transition_clean$HER12 > 1300 & transition_clean$HER12 < 2602)), (sum(transition_clean$HER12 > 2601 & transition_clean$HER12 < 3903)), (sum(transition_clean$HER12 > 3902 & transition_clean$HER12 < 5204)), (sum(transition_clean$HER12 > 5203 & transition_clean$HER12 < 6505)))
her12_permaculture = c((sum(permaculture_clean$HER12<1301)), (sum(permaculture_clean$HER12 > 1300 & permaculture_clean$HER12 < 2602)), (sum(permaculture_clean$HER12 > 2601 & permaculture_clean$HER12 < 3903)), (sum(permaculture_clean$HER12 > 3902 & permaculture_clean$HER12 < 5204)), (sum(permaculture_clean$HER12 > 5203 & permaculture_clean$HER12 < 6505)))
her12_scdt = c((sum(scdt_clean$HER12<1301)), (sum(scdt_clean$HER12 > 1300 & scdt_clean$HER12 < 2602)), (sum(scdt_clean$HER12 > 2601 & scdt_clean$HER12 < 3903)), (sum(scdt_clean$HER12 > 3902 & scdt_clean$HER12 < 5204)), (sum(scdt_clean$HER12 > 5203 & scdt_clean$HER12 < 6505)))
her12_dtas = c((sum(dtas_clean$HER12<1301)), (sum(dtas_clean$HER12 > 1300 & dtas_clean$HER12 < 2602)), (sum(dtas_clean$HER12 > 2601 & dtas_clean$HER12 < 3903)), (sum(dtas_clean$HER12 > 3902 & dtas_clean$HER12 < 5204)), (sum(dtas_clean$HER12 > 5203 & dtas_clean$HER12 < 6505)))
# STAGE 3e, calculate sums based on ESTR12 columns and generate new integer sets with quintile count data
estr12_ecs = c((sum(ecs_clean$ESTR12<1301)), (sum(ecs_clean$ESTR12 > 1300 & ecs_clean$ESTR12 < 2602)), (sum(ecs_clean$ESTR12 > 2601 & ecs_clean$ESTR12 < 3903)), (sum(ecs_clean$ESTR12 > 3902 & ecs_clean$ESTR12 < 5204)), (sum(ecs_clean$ESTR12 > 5203 & ecs_clean$ESTR12 < 6505)))
estr12_transition = c((sum(transition_clean$ESTR12<1301)), (sum(transition_clean$ESTR12 > 1300 & transition_clean$ESTR12 < 2602)), (sum(transition_clean$ESTR12 > 2601 & transition_clean$ESTR12 < 3903)), (sum(transition_clean$ESTR12 > 3902 & transition_clean$ESTR12 < 5204)), (sum(transition_clean$ESTR12 > 5203 & transition_clean$ESTR12 < 6505)))
estr12_permaculture = c((sum(permaculture_clean$ESTR12<1301)), (sum(permaculture_clean$ESTR12 > 1300 & permaculture_clean$ESTR12 < 2602)), (sum(permaculture_clean$ESTR12 > 2601 & permaculture_clean$ESTR12 < 3903)), (sum(permaculture_clean$ESTR12 > 3902 & permaculture_clean$ESTR12 < 5204)), (sum(permaculture_clean$ESTR12 > 5203 & permaculture_clean$ESTR12 < 6505)))
estr12_scdt = c((sum(scdt_clean$ESTR12<1301)), (sum(scdt_clean$ESTR12 > 1300 & scdt_clean$ESTR12 < 2602)), (sum(scdt_clean$ESTR12 > 2601 & scdt_clean$ESTR12 < 3903)), (sum(scdt_clean$ESTR12 > 3902 & scdt_clean$ESTR12 < 5204)), (sum(scdt_clean$ESTR12 > 5203 & scdt_clean$ESTR12 < 6505)))
estr12_dtas = c((sum(dtas_clean$ESTR12<1301)), (sum(dtas_clean$ESTR12 > 1300 & dtas_clean$ESTR12 < 2602)), (sum(dtas_clean$ESTR12 > 2601 & dtas_clean$ESTR12 < 3903)), (sum(dtas_clean$ESTR12 > 3902 & dtas_clean$ESTR12 < 5204)), (sum(dtas_clean$ESTR12 > 5203 & dtas_clean$ESTR12 < 6505)))
# STAGE 4a - calculate percentages
simdr12_ecs_percent<- prop.table(simdr12_ecs)
simdr12_transition_percent<- prop.table(simdr12_transition)
simdr12_permaculture_percent<- prop.table(simdr12_permaculture)
simdr12_scdt_percent<- prop.table(simdr12_scdt)
simdr12_dtas_percent<- prop.table(simdr12_dtas)
incr12_ecs_percent<- prop.table(incr12_ecs)
incr12_transition_percent<- prop.table(incr12_transition)
incr12_permaculture_percent<- prop.table(incr12_permaculture)
incr12_scdt_percent<- prop.table(incr12_scdt)
incr12_dtas_percent<- prop.table(incr12_dtas)
empr12_ecs_percent<- prop.table(empr12_ecs)
empr12_transition_percent<- prop.table(empr12_transition)
empr12_permaculture_percent<- prop.table(empr12_permaculture)
empr12_scdt_percent<- prop.table(empr12_scdt)
empr12_dtas_percent<- prop.table(empr12_dtas)
her12_ecs_percent<- prop.table(her12_ecs)
her12_transition_percent<- prop.table(her12_transition)
her12_permaculture_percent<- prop.table(her12_permaculture)
her12_scdt_percent<- prop.table(her12_scdt)
her12_dtas_percent<- prop.table(her12_dtas)
estr12_ecs_percent<- prop.table(estr12_ecs)
estr12_transition_percent<- prop.table(estr12_transition)
estr12_permaculture_percent<- prop.table(estr12_permaculture)
estr12_scdt_percent<- prop.table(estr12_scdt)
estr12_dtas_percent<- prop.table(estr12_dtas)
# STAGE 4b, generate data frame using integer sets
simd = data.frame(simdr12_ecs, simdr12_ecs_percent, incr12_ecs, incr12_ecs_percent, empr12_ecs, empr12_ecs_percent, her12_ecs, her12_ecs_percent, estr12_ecs, estr12_ecs_percent, simdr12_transition, simdr12_transition_percent, incr12_transition, incr12_transition_percent, empr12_transition, empr12_transition_percent, her12_transition, her12_transition_percent, estr12_transition, estr12_transition_percent, simdr12_permaculture, simdr12_permaculture_percent, incr12_permaculture, incr12_permaculture_percent, empr12_permaculture, empr12_permaculture_percent, her12_permaculture, her12_permaculture_percent, estr12_permaculture, estr12_permaculture_percent, simdr12_scdt, simdr12_scdt_percent, incr12_scdt, incr12_scdt_percent, empr12_scdt, empr12_scdt_percent, her12_scdt, her12_scdt_percent, estr12_scdt, estr12_scdt_percent)
simd = data.frame(simdr12_ecs, simdr12_ecs_percent, incr12_ecs, incr12_ecs_percent, empr12_ecs, empr12_ecs_percent, her12_ecs, her12_ecs_percent, estr12_ecs, estr12_ecs_percent, simdr12_transition, simdr12_transition_percent, incr12_transition, incr12_transition_percent, empr12_transition, empr12_transition_percent, her12_transition, her12_transition_percent, estr12_transition, estr12_transition_percent, simdr12_permaculture, simdr12_permaculture_percent, incr12_permaculture, incr12_permaculture_percent, empr12_permaculture, empr12_permaculture_percent, her12_permaculture, her12_permaculture_percent, estr12_permaculture, estr12_permaculture_percent, simdr12_dtas, simdr12_dtas_percent, incr12_dtas, incr12_dtas_percent, empr12_dtas, empr12_dtas_percent, her12_dtas, her12_dtas_percent, estr12_dtas, estr12_dtas_percent)
write.csv(simd, "derivedData/simd.csv", row.names=FALSE)
simd_percents_only = data.frame(simd_rownames, simdr12_ecs_percent, incr12_ecs_percent, empr12_ecs_percent, her12_ecs_percent, estr12_ecs_percent, simdr12_transition_percent, incr12_transition_percent, empr12_transition_percent, her12_transition_percent, estr12_transition_percent, simdr12_permaculture_percent, incr12_permaculture_percent, empr12_permaculture_percent, her12_permaculture_percent, estr12_permaculture_percent, simdr12_scdt_percent, incr12_scdt_percent, empr12_scdt_percent, her12_scdt_percent, estr12_scdt_percent)
simd_percents_only = data.frame(simd_rownames, simdr12_ecs_percent, incr12_ecs_percent, empr12_ecs_percent, her12_ecs_percent, estr12_ecs_percent, simdr12_transition_percent, incr12_transition_percent, empr12_transition_percent, her12_transition_percent, estr12_transition_percent, simdr12_permaculture_percent, incr12_permaculture_percent, empr12_permaculture_percent, her12_permaculture_percent, estr12_permaculture_percent, simdr12_dtas_percent, incr12_dtas_percent, empr12_dtas_percent, her12_dtas_percent, estr12_dtas_percent)
write.csv(simd_percents_only, "derivedData/simd_percents_only.csv", row.names=FALSE)
@ -642,11 +637,11 @@ qplot(data=simd_percents_only_long , geom="bar", fill=(factor(simd_rownames)))
```
Another crucial point of assessment relates to the relation of Eco-Congregation communities to the Scottish Index of Multiple Deprivation. This instrument aggregates a large variety of factors which can lead to deprivation including crime rates, employment levels, access to services (implicating remoteness), and literacy. By assessing ECS, Transition, and SCDT against the deprivation scale, we can assess whether eco-congregations fall within particular demographics and also whether the fully aggregated SIMD measurement provides a useful point of comparison for our purposes. The SIMD essentially divides Scotland into 6407 geographic zones and then ranks them based on their relative deprivation. This data set can be split into any number of groups, but for our purposes we have settled on Quintiles, splitting the SIMD data set at every 1302 entries. We then measured where each transition group, ECS, and SCDT fell within these zones and calculated how they fell into these five quintiles, from more to least deprived.
Another crucial point of assessment relates to the relation of Eco-Congregation communities to the Scottish Index of Multiple Deprivation. This instrument aggregates a large variety of factors which can lead to deprivation including crime rates, employment levels, access to services (implicating remoteness), and literacy. By assessing ECS, Transition, and dtas against the deprivation scale, we can assess whether eco-congregations fall within particular demographics and also whether the fully aggregated SIMD measurement provides a useful point of comparison for our purposes. The SIMD essentially divides Scotland into 6407 geographic zones and then ranks them based on their relative deprivation. This data set can be split into any number of groups, but for our purposes we have settled on Quintiles, splitting the SIMD data set at every 1302 entries. We then measured where each transition group, ECS, and dtas fell within these zones and calculated how they fell into these five quintiles, from more to least deprived.
The first, and most compelling finding is that, in general Eco-Congregation Scotland and Transition Scotland are both roughly the same and match the level of population distribution in the lowest quintile of the general SIMD measurement. 8% of transition groups and eco-congregation groups which have received awards and 9% of the population are located within this quintile. However, taken in relation to the distribution of places of worship in the lowest quintile, we find that eco-congregations are located at half the rate that places of worship are (15%) and SCDTs match this much more closely at 14%. Turning towards the top quintile, this pattern also holds, here both transition groups (21%) and eco-congregations (21% and 29% of awarded congregations) depart from the population distribution in this upper quintile (which is 10%). Again, general places of worship (at 11%) and SCDTs (at 5%) take the opposite direction. We can say decisively that in communities which have been identified as good candidates for intervention to reduce deprivation, ECS and Transition are less likely, and they are over-represented at the areas which fall into the least deprived quintile.
The first, and most compelling finding is that, in general Eco-Congregation Scotland and Transition Scotland are both roughly the same and match the level of population distribution in the lowest quintile of the general SIMD measurement. 8% of transition groups and eco-congregation groups which have received awards and 9% of the population are located within this quintile. However, taken in relation to the distribution of places of worship in the lowest quintile, we find that eco-congregations are located at half the rate that places of worship are (15%) and dtass match this much more closely at 14%. Turning towards the top quintile, this pattern also holds, here both transition groups (21%) and eco-congregations (21% and 29% of awarded congregations) depart from the population distribution in this upper quintile (which is 10%). Again, general places of worship (at 11%) and DTASs (at 5%) take the opposite direction. We can say decisively that in communities which have been identified as good candidates for intervention to reduce deprivation, ECS and Transition are less likely, and they are over-represented at the areas which fall into the least deprived quintile.
We can find divergence between transition communities and eco-congregation when we split out SIMD domains. In the lowest quartile, measuring exclusively for the income domain, ECS is more represented (11%) - roughly the same as SCDT (12%), and transition is less (6%) represented. In general (as shown on the chart in Appendix D), these trends hold when representation of our groups are measured within other non-remoteness domains of the SIMD. Our basic conclusion is that transition towns are least likely to operate within the lowest quartile of SIMD and SCDTs are most likely, with ECS somewhere in the middle. Given the general disparity against the presence of places of worship, it seems fair to suggest that this might be an area for improvement, perhaps even worth developing a special programme which might target areas in SIMD quartile 1 for eco-congregation outreach. This might be considered particularly in light of the starkest underrepresentation of ECS and transition within the SIMD domain of education, skills, and training.
We can find divergence between transition communities and eco-congregation when we split out SIMD domains. In the lowest quartile, measuring exclusively for the income domain, ECS is more represented (11%) - roughly the same as DTAS (12%), and transition is less (6%) represented. In general (as shown on the chart in Appendix D), these trends hold when representation of our groups are measured within other non-remoteness domains of the SIMD. Our basic conclusion is that transition towns are least likely to operate within the lowest quartile of SIMD and DTASs are most likely, with ECS somewhere in the middle. Given the general disparity against the presence of places of worship, it seems fair to suggest that this might be an area for improvement, perhaps even worth developing a special programme which might target areas in SIMD quartile 1 for eco-congregation outreach. This might be considered particularly in light of the starkest underrepresentation of ECS and transition within the SIMD domain of education, skills, and training.
# Appendix A
@ -661,7 +656,7 @@ write.csv(admin.shortened, "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(scdt, "derivedData/scdt.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)