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mapping_draft.Rmd
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@ -853,7 +853,7 @@ We can find divergence between transition communities and eco-congregation when
# Download data for SSSI:
if (file.exists("data/National_Forest_Inventory_Woodland_Scotland_2017.shp") == FALSE) {
if (file.exists("data/SSSI_SCOTLAND.shp") == FALSE) {
# TODO: get reliable URL for data download
# http://gateway.snh.gov.uk/natural-spaces/dataset.jsp?dsid=SSSI
# download.file("", destfile = "data/SSSI_SCOTLAND_ESRI.zip")
@ -892,18 +892,23 @@ forest_inventory_sp <- readOGR("./data", "National_Forest_Inventory_Woodland_Sco
# International Union for Conservation of Nature (IUCN), Category V Protected Landscapes
# for England: https://environment.data.gov.uk/DefraDataDownload/?mapService=NE/AreasOfOutstandingNaturalBeautyEngland&Mode=spatial
download.file("https://opendata.arcgis.com/datasets/d7f6b987c7224a72a185ce012258d500_23.zip", destfile = "data/ScenicAreas.zip")
unzip("data/ScenicAreas.zip", exdir = "data")
scenicareas <- st_read("data/ScenicAreas.shp")
scenicareas_sp <- readOGR("./data", "ScenicAreas")
# download.file("https://opendata.arcgis.com/datasets/d7f6b987c7224a72a185ce012258d500_23.zip", destfile = "data/ScenicAreas.zip")
# unzip("data/ScenicAreas.zip", exdir = "data")
# scenicareas <- st_read("data/ScenicAreas.shp")
# scenicareas_sp <- readOGR("./data", "ScenicAreas")
# Set symmetrical CRS for analysis below
st_crs(sssi) <- 27700
st_crs(ecs_sf) <- 27700
st_crs(pow_pointX_sf) <- 27700
st_crs(dtas_sf) <- 27700
st_crs(transition_sf) <- 27700
st_crs(permaculture_sf) <- 27700
# Define buffer and measure number of ECS groups within 0.5 miles of all SSSI
# CRS uses meters for units, so buffer for 0.5 miles will use 805 meters)
# CRS uses meters for units, so a buffer for 0.5 miles would use 805 meters)
```
# Proximity to "Wilderness"
@ -912,7 +917,7 @@ Chasing down a curiosity, I decided to try and calculate whether proximity to "w
Proximity to these areas was the next concern, because many of these designations deliberately exclude human habitat, so it was necessary to measure the number of sites within proximity. There is a question which lies here regarding aesthetics, namely, what sort of proximity might generate an affective connection? From my own experience, I decided upon the distance represented by a short walk, i.e. a half-kilometre. However, with the more generic measurements, such as SSSI and forestation, this wouldn't do, as there are so many of these sites that a buffer of 500 meters encapsulates almost all of inhabited Scotland. So for these sites I also calculated a count within 50 metres.
So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, 0 sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `st_within(pow_pointX_sf, sssi)` places of worship out of `r length(pow_pointX)`, `st_within(transition_sf, sssi)` transition towns, (or 2%) and `st_within(dtas_sf, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `st_within(ecs_sf, st_buffer(sssi, dist = 50))` Eco-Congregations (or just under 1%), which compares favourably with the `st_within(pow_pointX_sf, st_buffer(sssi, dist = 50))` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only one Eco-Congregation lies within either native or generally forested land.
So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, `r st_within(ecs_sf, wildland)` sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `r st_within(pow_pointX_sf, sssi)` places of worship out of `r length(pow_pointX)`, `r st_within(transition_sf, sssi)` transition towns, (or 2%) and `r st_within(dtas_sf, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `r st_within(ecs_sf, st_buffer(sssi, dist = 50))` Eco-Congregations (or just under 1%), which compares favourably with the `r st_within(pow_pointX_sf, st_buffer(sssi, dist = 50))` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only `r st_within(ecs_sf, forest_inventory)` Eco-Congregation lies within either native or generally forested land.
```{r wilderness_table}
# Calculate number of groups within polygons

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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**: February 01, 2019
# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 05, 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: "2019-02-01"
date: "2019-02-05"
bibliography: biblio.bib
linkcolor: black
geometry: margin=1in
@ -15,13 +15,17 @@ geometry: margin=1in
fontsize: 11pt
output:
html_document:
theme: readable
keep_md: true
code_folding: hide
self_contained: false
toc: true
toc_depth: 2
number_sections: true
fig_caption: true
fig_retina: 1
pdf_document:
toc: false
keep_tex: true
number_sections: true
fig_caption: true
@ -57,7 +61,7 @@ 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 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
@ -74,6 +78,8 @@ In the case of Eco-Congregation Scotland, congregations are invited to begin by
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!*).[^15571030]
@ -94,9 +100,16 @@ Whereas our initial measurements indicated a prominent lead for Edinburgh, by no
![](figures/create_admin_barplot-1.png)<!-- -->
## Concentration of groups {.tabset}
### Choropleth
![Figure 4](figures/create_choropleth_others-1.png)![Figure 4](figures/create_choropleth_others-2.png)![Figure 4](figures/create_choropleth_others-3.png)
### Cartogram
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 dtas 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).
# Christian Denominations #
@ -139,7 +152,7 @@ While Roman Catholic churches make up just over 10% of the church buildings in S
```
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "SG_UrbanRural_2016"
## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "SG_UrbanRural_2016"
## with 8 features
## It has 6 fields
```
@ -169,13 +182,24 @@ Of all the groups surveyed in this study, Eco-Congregation Scotland is the most
```
## OGR data source with driver: ESRI Shapefile
## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "sc_dz_11"
## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "sc_dz_11"
## with 6976 features
## It has 9 fields
```
## SIMD representation across domains by group {.tabset}
### Jitterplot
### Barplot
![](figures/create_simd_barplot-1.png)<!-- -->
### Boxplot
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.
@ -184,17 +208,228 @@ The first, and most compelling finding is that, in general Eco-Congregation Scot
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.
```
## Reading layer `SSSI_SCOTLAND' from data source `/Users/jeremy/gits/mapping_environmental_action/data/SSSI_SCOTLAND.shp' using driver `ESRI Shapefile'
## Simple feature collection with 15872 features and 7 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -296506.9 ymin: 530237.9 xmax: 467721.5 ymax: 1220310
## 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: "SSSI_SCOTLAND"
## with 15872 features
## It has 7 fields
## Integer64 fields read as strings: PA_CODE
```
```
## Reading layer `WILDLAND_SCOTLAND' from data source `/Users/jeremy/gits/mapping_environmental_action/data/WILDLAND_SCOTLAND.shp' using driver `ESRI Shapefile'
## Simple feature collection with 42 features and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 76877.24 ymin: 578454.1 xmax: 435367.1 ymax: 1190510
## 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: "WILDLAND_SCOTLAND"
## with 42 features
## It has 3 fields
```
```
## Reading layer `National_Forest_Inventory_Woodland_Scotland_2017' from data source `/Users/jeremy/gits/mapping_environmental_action/data/National_Forest_Inventory_Woodland_Scotland_2017.shp' using driver `ESRI Shapefile'
## Simple feature collection with 199698 features and 7 fields
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: 65210.1 ymin: 532547.9 xmax: 461253.7 ymax: 1209179
## 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: "National_Forest_Inventory_Woodland_Scotland_2017"
## with 199698 features
## It has 7 fields
## Integer64 fields read as strings: OBJECTID
```
# Proximity to "Wilderness"
Chasing down a curiosity, I decided to try and calculate whether proximity to "wilderness" or "scenic nature" or just trees might have some impact on generating more mobilised communities. I realised that there would be several problems with this kind of calculation up front, first being that "nature" is a deeply problematic construct, reviled by geographers and philosophers alike. With this in mind, I identified several different ways of reckoning wilderness, starting with the highly anachronistic "Scenic Land" designation from the 1970s. Then I pursued the more carefully calculated "core wild areas" generated by SNH just a few years ago. However, even the core wile areas concept has been criticised heavily, so I also expanded out my search to include all sites of special scientific interest and then went even wider to include the Scottish Forestry Service's "Native Woodland" and finally, the most generic possible measurement, any land identified as forested at the last Forest Inventory.
Proximity to these areas was the next concern, because many of these designations deliberately exclude human habitat, so it was necessary to measure the number of sites within proximity. There is a question which lies here regarding aesthetics, namely, what sort of proximity might generate an affective connection? From my own experience, I decided upon the distance represented by a short walk, i.e. a half-kilometre. However, with the more generic measurements, such as SSSI and forestation, this wouldn't do, as there are so many of these sites that a buffer of 500 meters encapsulates almost all of inhabited Scotland. So for these sites I also calculated a count within 50 metres.
So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, 0 sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only 5 places of worship out of over 4k, 2 transition towns, (or 2%) and 7 community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie 3 Eco-Congregations (or just under 1%), which compares favourably with the 61 places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only one Eco-Congregation lies within either native or generally forested land
So what did I discover? The results were inconclusive. First, it is important to note that on the whole, Eco-Congregations tend to be more urban than place of worship taken generally at a rate of nearly 3:1 (5.4% of Eco-Congregations lie in areas currently designated as "Very Remote Rural Areas" whereas nearly 15% of places of worship lie in these areas), so what I was testing for was whether this gap was smaller when specifying these various forms of "wild" remoteness. For our narrowest measurements, there were so few sites captured as to render measurement unreliable. There are, for obvious reasons, 0 sites located within any of SNG's core wild areas. Similarly, there are very few of our activist communities located within SSSI's (only `st_within(pow_pointX_sf, sssi)` places of worship out of 4048, `st_within(transition_sf, sssi)` transition towns, (or 2%) and `st_within(dtas_sf, sssi)` community development trusts (3%)). However, expanding this out makes things a bit more interesting, within 50 metres of SSSI's in Scotland lie `st_within(ecs_sf, st_buffer(sssi, dist = 50))` Eco-Congregations (or just under 1%), which compares favourably with the `st_within(pow_pointX_sf, st_buffer(sssi, dist = 50))` places of worship (or just 1.5%) far exceeding our ratio (1:1.5 vs. 1:3). This is the same with our more anachronistic measure of "scenic areas," there are 7 eco-congregations within these areas, and 175 places of worship, making for a ratio of nearly 1:2 (2.1% vs. 4.3%). Taking our final measure, of forested areas, this is hard to calculate, as only one Eco-Congregation lies within either native or generally forested land.
--------------------------------- -------------- -------- ---------
**titles** Within SSSIs ...50m ...500m
**ecs_wilderness_row** 0 3 59
**pow_wilderness_row** 7 62 610
**dtas_wilderness_row** 7 11 49
**transition_wilderness_row** 0 1 17
**permaculture_wilderness_row** 0 0 3
--------------------------------- -------------- -------- ---------
# 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
--------------------
# Appendix B
@ -203,6 +438,71 @@ So what did I discover? The results were inconclusive. First, it is important to
# Appendix C - Data by Urban / Rural Classification
--------------------------------------------------------------------------------
UR8FOLD ecs_count ecs_percent pow_count pow_percent transition_count
--------- ----------- ------------- ----------- ------------- ------------------
1 111 0.32 907 0.22 35
2 98 0.28 1015 0.25 16
3 30 0.087 265 0.065 7
4 9 0.026 100 0.025 2
5 6 0.017 87 0.021 2
6 54 0.16 696 0.17 14
7 16 0.047 372 0.092 7
8 20 0.058 606 0.15 11
--------------------------------------------------------------------------------
Table: Table continues below
---------------------------------------------------------------------
transition_percent dtas_count dtas_percent permaculture_count
-------------------- ------------ -------------- --------------------
0.37 35 0.15 2
0.17 25 0.11 1
0.074 20 0.087 1
0.021 7 0.03 0
0.021 1 0.0043 1
0.15 46 0.2 2
0.074 27 0.12 3
0.12 69 0.3 4
---------------------------------------------------------------------
Table: Table continues below
----------------------
permaculture_percent
----------------------
0.14
0.071
0.071
0
0.071
0.14
0.21
0.29
----------------------
# Citations
@ -220,4 +520,4 @@ So what did I discover? The results were inconclusive. First, it is important to
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.
[^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.adition and religion further below.