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tidied up urbanrural data

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Jeremy Kidwell 2018-10-16 11:25:21 +01:00
parent 71406ef201
commit ddab457d8d
1 changed files with 5 additions and 2 deletions
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mapping_draft.Rmd
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@ -430,6 +430,8 @@ download.file("http://sedsh127.sedsh.gov.uk/Atom_data/ScotGov/ZippedShapefiles/S
unzip("data/SG_UrbanRural_2016.zip", exdir = "data")
urbanrural <- readOGR("./data", "SG_UrbanRural_2016")
# TODO: worth considering uploading data to zenodo for long-term reproducibility as ScotGov shuffles this stuff around periodically breaking URLs
# This code will generate a table of frequencies for each spatialpointsdataframe in urbanrural
# calculate count of ECS for fields in urbanrural
urbanrural$ecs_count <- poly.counts(ecs,urbanrural)
@ -449,9 +451,10 @@ urbanrural$permaculture_percent<- prop.table(urbanrural$permaculture_count)
# Create dataframe for analysis
urbanrural_df<-data.frame(urbanrural)
# Create bar chart
qplot(OBJECTID, data=urbanrural_df, geom = "bar")
ggplot(urbanrural_df) aes(x=OBJECTID, geom_bar(position="fill"))
# TODO: use ggplot here to generate stacked bar chart showing representation across 8 urban/rural bands from urbanrural_df
# TODO: generate map based on urbanrural
```
Rather than bifurcate congregations into an urban/rural dichotomy, for this study we used the Scottish Government's eight-point remoteness scale to categorise eco-congregations along a spectrum of highly populated to remote areas. This 8-fold scale (calculated biennially) offers a more nuanced measurement that combines measurements of remoteness and population along the following lines: