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tidied up urbanrural data

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Jeremy Kidwell 2018-10-16 11:44:36 +01:00
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mapping_draft.Rmd
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@ -468,20 +468,11 @@ The key question which this analysis seeks to answer is whether ECS, or the othe
Of all the groups surveyed in this study, Eco-Congregation Scotland is the most heavily concentrated in large urban areas (33.53%), exceeding by almost 50% the rate for all places of worship (22.96% in large urban areas). Transition is a much more modest 20% and development trusts a bit lower at 15%. It is interesting to note that the rate of ECS concentration in these large urban areas matches the level of overall population distribution (34.5%). On the other end of the scale, Eco-Congregation Scotland is the least concentrated in remote rural areas (with 3.93% on level 7 and 5.44% on level 8 on the urban-rural scale), though again, they correlate roughly to the general population distribution (3.2% and 2.9% respectively). Places of worship outpace both the population of Scotland and the footprint of Eco-Congregation Scotland, with 14.98% in very remote rural areas, but this is exceeded by transition at 16.47% and both by Scottish community development trusts at 32.14%. So while Eco-Congregation Scotland correlates roughly with Scottish population distribution across the urban-rural scale, it has a considerably more urban profile than either of the other two groups surveyed.
```{r}
```{r create_urbanrural_ecs_chart_choropleth}
# Create bar chart
# 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
# TODO: generate map based on urbanrural polygons, shade based on 8-fold categories, draw transparent dots onto polygons
var01 <- admin_lev1$ecs_count
bins <- unique(quantile(var01, seq(0,1,length.out=30)))
admin_lev1$binId01 <- findInterval(var01, bins)
colSet01 <- rev(heat.colors(length(bins)))
plot(admin, col=colSet01[admin_lev1$binId01], border="grey", lwd=0.25)
par(mar=c(5,3,2,2)+0.1)
```
```{r create_urbanrural_ecs_choropleth}
pdf(file="figures/urbanrural_choropleth_ecs.pdf", width=4, height=4)
bins <- unique(quantile(urbanrural$ecs_count, seq(0,1,length.out=30)))
urbanrural$binId01 <- findInterval(urbanrural$ecs_count, bins)
@ -496,8 +487,7 @@ title(main="Figure 5", sub="Eco-Congregation Scotland\nconcentrations in Urban R
dev.off()
```
[Figure6 map of ECS concentration by UR Areas Urban - Rural via Brown to Green]
![][Figure6 map of ECS concentration by UR Areas Urban - Rural via Brown to Green]
# Wealth, Employment, and Literacy