From adf734c1069090945e433c25bace0b3207618f15 Mon Sep 17 00:00:00 2001 From: Jeremy Kidwell Date: Sun, 27 Jan 2019 19:58:42 +0000 Subject: [PATCH] fixed chart --- mapping_draft.Rmd | 57 ++++--- mapping_draft.html | 355 +++++++++++++++++++++++++++++++++++++--- mapping_draft.md | 172 +++++++++++++++---- mapping_draft_part2.Rmd | 7 +- 4 files changed, 511 insertions(+), 80 deletions(-) diff --git a/mapping_draft.Rmd b/mapping_draft.Rmd index f7373c6..8f48f81 100644 --- a/mapping_draft.Rmd +++ b/mapping_draft.Rmd @@ -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(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity") + coord_flip() -# ggplot(mtcars, aes(x=as.factor(cyl), fill=as.factor(cyl) )) + geom_bar() + 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. diff --git a/mapping_draft.html b/mapping_draft.html index 8aa2d5f..c833a7b 100644 --- a/mapping_draft.html +++ b/mapping_draft.html @@ -371,7 +371,7 @@ $(document).ready(function () {

Mapping Environmental Action in Scotland

“Jeremy H. Kidwell”

-

“2019-01-26”

+

“2019-01-27”

@@ -382,9 +382,8 @@ $(document).ready(function () {
  • 3 Technical Background
  • 4 Background and History of Eco-Congregation Scotland
  • 5 Representation by Regional Authorities (Council Areas)
    • -
  • 6 plot as chlorogram: https://www.r-graph-gallery.com/331-basic-cartogram/
    • -
  • 7 Appendix A
    • -
  • 8 Citations
    • +
  • 6 Appendix A
    • +
  • 7 Citations
    • @@ -408,7 +407,7 @@ $(document).ready(function () {

    3 Technical Background

    -

    Analysis was conducted using QGIS 2.8 and R 3.5.1, and data-sets were generated in CSV format.4 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.5 These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);6 and another community-based NGO, Scottish Community Development Trusts.7 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.8

    +

    Analysis was conducted using QGIS 2.8 and R 3.5.2, and data-sets were generated in CSV format.4 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.5 These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);6 and another community-based NGO, Scottish Community Development Trusts.7 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.8

    4 Background and History of Eco-Congregation Scotland

    @@ -418,41 +417,344 @@ $(document).ready(function () {

    5 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).

    (TODO: need to implement) Though there are too few eco-congregations and transition groups for a numerically significant representation in any of the intermediate geographies, mapping the concentration of sites by agricultural parishes allows for a more granular visual and I include this for comparison sake. Note, for the sake of a more accurate visual communication, we have also marked out areas of Scotland that are uninhabited with hash marks on the map of agricultural parishes. (TODO: this will be done in the final draft, once I get my image masking fixed!).12

    [][Figure2]

    -

    Given the way population and places of worship are unevenly distributed across Scotland it is important to represent data in terms of relative distribution. For this study, we attempted to “normalise” our data in two different ways, (1) as shown by Figure 2 above, by taking population figures from the 2011 census (see data sheet in Appendix A) and (2) by adjusting relative to the number of places of worship in each council region.13 The latter of these two can yield particularly unexpected results. Thus, of the 4048 “places of worship” in Scotland, the highest concentration is actually the region, with 435, second is 329 (). Rank of Council Areas by population and number of places of worship is also included in Appendix A.

    +

    Given the way population and places of worship are unevenly distributed across Scotland it is important to represent data in terms of relative distribution. For this study, we attempted to “normalise” our data in two different ways, (1) as shown by Figure 2 above, by taking population figures from the 2011 census (see data sheet in Appendix A) and (2) by adjusting relative to the number of places of worship in each council region.[^15914204] The latter of these two can yield particularly unexpected results. Thus, of the 4048 “places of worship” in Scotland, the highest concentration is actually the region, with 435, second is 329 (). Rank of Council Areas by population and number of places of worship is also included in Appendix A.

    We can use this data to normalise our figures regarding Eco-Congregation Scotland communities and this draws the presence in Edinburgh of ECS communities into even sharper relief, as Edinburgh, though ranked second in terms of population and fifth in terms of places of worship, ranks first for the presence of all ECS congregations and awarded ECS congregations. However, taking population as the basis for normalisation first, we find that Edinburgh is far from the most prominent outlier. In trying to communicate this difference for a lay-audience, we have chosen to list this difference as a multiplier (i.e. there are 2.x times as many congregations as their share of population and an average figure of congregations might allow for) as this conveys the difference in a straight-forward way. Outliers where the disparity between their relative share of the total ECS footprint and their relative share of population is different by a positive ratio of more than double include the Orkney Islands (3.7 times more eco-congregations than their expected average share based on population), Argyll and Bute (admin_lev1[CODE=S12000023]$ecs_pop_factor 4.2x), Stirling (2.76x), and Perthshire and Kinross (2.18x). Interestingly, there are no outliers whose relative share of the total footprint of ECS is double or more in the negative direction (see Appendix A chart for full numbers).

    Turning to the total of 4048 “places of worship” in Scotland, we find a slightly different picture of the relative concentration of Eco-Congregations in Scotland. In this case, the outliers are

    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.

    +

    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]

    [Figure 3c, map normalised by population; choropeth map with 6 gradients. with white represented by =/- 0.5; side-by-side with agricultural parishes map; uninhabited areas greyed out]

    [Figure 4, normalised by PointX data; also including agricultural parishes etc. as above]

    -
    -
    -

    6 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/

    -

    7 Appendix A

    +

    6 Appendix A

    + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Table continues below
    nameecs_countpow_countdtas_count
    Fife1824311
    South Ayrshire3681
    Inverclyde2531
    Aberdeen City15961
    Highland2143534
    West Dunbartonshire6523
    Moray111036
    Orkney Islands45010
    Aberdeenshire1924415
    East Renfrewshire8372
    Clackmannanshire3301
    East Ayrshire4686
    North Lanarkshire51871
    Dundee City3941
    Argyll and Bute1817224
    Angus121060
    Glasgow City2532921
    East Dunbartonshire7431
    Shetland Islands3894
    Scottish Borders111539
    Comhairle nan Eilean Siar01149
    Renfrewshire6845
    North Ayrshire7963
    East Lothian8712
    Falkirk8830
    West Lothian11707
    Perth and Kinross2016210
    Dumfries and Galloway718915
    Stirling137310
    City of Edinburgh482337
    Midlothian1454
    South Lanarkshire171768
    + +++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    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
    -

    8 Citations

    +

    7 Citations

    @@ -469,7 +771,6 @@ $(document).ready(function () {

  • Interview with Margaret Warnock, 29 Aug 2014.↩

  • 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”↩

  • 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.↩

    • -

  • 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.↩

    • diff --git a/mapping_draft.md b/mapping_draft.md index db3e9a8..52b532d 100644 --- a/mapping_draft.md +++ b/mapping_draft.md @@ -1,13 +1,13 @@ --- title: "Mapping Environmental Action in Scotland" abstract: -# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: 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. diff --git a/mapping_draft_part2.Rmd b/mapping_draft_part2.Rmd index d70385b..0e77078 100644 --- a/mapping_draft_part2.Rmd +++ b/mapping_draft_part2.Rmd @@ -268,4 +268,9 @@ write.csv(urbanrural, "derivedData/urbanrural.csv", row.names=FALSE) write.csv(urbanrural.shortened, "derivedData/urbanrural.csv", row.names=FALSE) urbanrural.shortened<-data.frame(urbanrural.shortened) panderOptions("digits", 2) -pander(urbanrural.shortened) \ No newline at end of file +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.