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Merge branch 'master' of git@github.com:kidwellj/mapping_environmental_action.git

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Jeremy Kidwell 2019-01-31 12:23:56 +00:00
parent a69b8565ec a3552a5cc2
commit ee1def48c8
1 changed files with 24 additions and 6 deletions
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mapping_draft.Rmd
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@ -99,7 +99,7 @@ wgs84_epsg <- CRS("+init=epsg:4326")
# Configure fonts for plots below # Configure fonts for plots below
## Loading Google fonts (http://www.google.com/fonts) ## Loading Google fonts (http://www.google.com/fonts)
font_add_google("Merriweather", "merriweather") # font_add_google("Merriweather", "merriweather")
# The following will load in system fonts (uncomment and run as needed on first execution) # The following will load in system fonts (uncomment and run as needed on first execution)
# font_import(pattern="[A/a]rial", prompt=FALSE) # font_import(pattern="[A/a]rial", prompt=FALSE)
``` ```
@ -431,6 +431,8 @@ admin.df <- data.frame(admin_lev1)
admin_gathered <- gather(data.frame(admin_lev1), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count) admin_gathered <- gather(data.frame(admin_lev1), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count)
# TODO: make custom legend labels, remove X axis label
# Ambitious TODO: can we link Admin area titles to locations on adjacent map with lines
ggplot(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity") + coord_flip() + labs(title = "Figure 4", subtitle="Comparison of Groups by Admin Region", fill = "Groups") ggplot(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity") + coord_flip() + labs(title = "Figure 4", subtitle="Comparison of Groups by Admin Region", fill = "Groups")
``` ```
@ -583,15 +585,10 @@ 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. 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 create_ur_barplot} ```{r create_ur_barplot}
# Create dataframe for analysis
# urbanrural_df<-data.frame(urbanrural)
# Need to flatten urbanrural based on all the count columns and generate using ggplot # Need to flatten urbanrural based on all the count columns and generate using ggplot
urbanrural_gathered <- gather(data.frame(urbanrural), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count) urbanrural_gathered <- gather(data.frame(urbanrural), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count)
# geom_bar( stat="identity", position="fill")
# TODO: switch to stacked percentage plot, see here: https://www.r-graph-gallery.com/48-grouped-barplot-with-ggplot2/ # TODO: switch to stacked percentage plot, see here: https://www.r-graph-gallery.com/48-grouped-barplot-with-ggplot2/
ggplot(urbanrural_gathered, aes(fill=group_type, y=number, x=UR8FOLD)) + geom_bar(position="dodge", stat="identity") + coord_flip() + labs(title = "Figure 8", subtitle="Comparison of Groups by UrbanRural category", fill = "Groups") ggplot(urbanrural_gathered, aes(fill=group_type, y=number, x=UR8FOLD)) + geom_bar(position="dodge", stat="identity") + coord_flip() + labs(title = "Figure 8", subtitle="Comparison of Groups by UrbanRural category", fill = "Groups")
``` ```
@ -604,9 +601,11 @@ names(admin_lev1)[names(admin_lev1) == "newcode"] <- "id"
urbanrural@data$id <- as.integer(rownames(urbanrural@data)) urbanrural@data$id <- as.integer(rownames(urbanrural@data))
urbanrural@data$id <- urbanrural@data$id - 1 urbanrural@data$id <- urbanrural@data$id - 1
urbanrural_fortified <- tidy(urbanrural) urbanrural_fortified <- tidy(urbanrural)
# Convert ecs to df for representation as points on the map
ecs_df <- as.data.frame(ecs) ecs_df <- as.data.frame(ecs)
urbanrural_fortified <- join(urbanrural_fortified,urbanrural@data, by="id") urbanrural_fortified <- join(urbanrural_fortified,urbanrural@data, by="id")
# Make dots smaller - add outlines, & lines thinner
ggplot() + ggplot() +
geom_polygon(aes(x = long, y = lat, group = group, geom_polygon(aes(x = long, y = lat, group = group,
fill = cut_interval(urbanrural_fortified$ecs_count, 5)), fill = cut_interval(urbanrural_fortified$ecs_count, 5)),
@ -649,6 +648,25 @@ simd_shapes <- readOGR("./data", "sc_dz_11")
simd_indicators <- read.csv("./data/simd2016_withinds.csv") simd_indicators <- read.csv("./data/simd2016_withinds.csv")
simd_wgs <- merge(x=simd_shapes, y=simd_indicators, by.x = "DataZone", by.y = "Data_Zone") simd_wgs <- merge(x=simd_shapes, y=simd_indicators, by.x = "DataZone", by.y = "Data_Zone")
simd <- spTransform(simd_wgs, bng) simd <- spTransform(simd_wgs, bng)
simd_df <- data.frame(simd)
simd_min <- simd[,-(26:55)]
simd_min <- simd[,-(3:13)]
simd@data[c(1:2,14:25)]
simd@data[-c(3:13,26:55)]
simd[, -(3:13)]
# SIMD_2016_Quintile
# SIMD_2016_Decile
#
# Income_Domain_2016_Rank
# Employment_Domain_2016_Rank
# Health_Domain_2016_Rank
# Education_Domain_2016_Rank
# Geographic_Access_Domain_2016_Rank
# Crime_Domain_2016_Rank
# Housing_Domain_2016_Rank
# STAGE 1, augment each dataset with relevant (geolocated) columns from SIMD # STAGE 1, augment each dataset with relevant (geolocated) columns from SIMD
# examine which ecs fall within each SIMD classification # examine which ecs fall within each SIMD classification