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merged plots 1/2, updated styling on tmap plots

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Jeremy Kidwell 2019-02-16 22:34:13 +00:00
parent 880f0250d0
commit 4fcb4b076c
1 changed files with 31 additions and 30 deletions
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mapping_draft.Rmd
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@ -321,46 +321,39 @@ Though there are too few eco-congregations and transition groups for a numerical
```{r plot_admin_ecs_choropleth, fig.width=4, fig.cap="Figure 1"}
# Note: for more information on EU administrative levels, see here: https://ec.europa.eu/eurostat/web/nuts/national-structures-eu
# TODO: Need to clip choropleth polygons to buildings shapefile
# TODO: clip choropleth polygons to buildings shapefile (possble superceded by pverlay on lev2)
# Draw initial choropleth map of ECS concentration (using tmap and sf below by default)
tm_shape(admin_lev1) +
tm_fill(col = "ecs_count", palette = "Oranges") +
tm_shape(admin_lev2) +
tm_fill(col = "ecs_count", palette = "Oranges", title = "Concentration of ECS groups") +
tm_borders(alpha=.5) +
tm_shape(admin_lev1) +
tm_borders(lwd=2) +
# TODO: Change title field "name" to match admin1
# also consider scaling text size using area # quick plot example:
# qtm(World, fill = "income_grp", text = "iso_a3", text.size = "AREA") # use "World$" to see the two attributes: income_grp and iso_a3, text.size= area: text is sized increasingly with coutry area size.
tm_text("name", size=.8, shadow=TRUE,
bg.color="white", bg.alpha=.25) +
# tm_shape(ecs_sf) +
# tm_dots("red", size = .05, alpha = .4) +
# tm_scale_bar(position = c("left", "bottom")) +
tm_style("gray", title = "Figure 1a") +
tm_credits("Data: UK Data Service (OGL)
& Jeremy H. Kidwell,
Graphic is CC-by-SA 4.0",
position = c("right", "bottom")) +
tm_layout(title = "Concentration of ECS groups",
# tm_scale_bar(position = c("right", "bottom")) +
tm_style("gray") +
tm_credits("Data: UK Data Service (OGL)\n& Jeremy H. Kidwell,\nGraphic is CC-by-SA 4.0",
size = 0.7,
position = c("left", "bottom"),
just = c("left", "bottom"),
align = "left") +
tm_layout(asp=NA
frame = FALSE,
title = "Figure 1a",
title.size = .7,
legend.title.size = .7,
inner.margins = c(0.1, 0.1, 0.05, 0.05)
)
```
## Eco-Congregation Scotland groups shown by concentration in administrative regions (LAU)
```{r plot_admin_ecs_admin2_choropleth, fig.width=4, fig.show="hold", fig.cap="Figure 2"}
tm_shape(admin_lev2) +
tm_fill(col = "ecs_count", palette = "Oranges") +
tm_style("gray", title = "Figure 2") +
tm_credits("Data: UK Data Service (OGL)
& Jeremy H. Kidwell,
Graphic is CC-by-SA 4.0",
position = c("right", "bottom")) +
tm_layout(title = "Concentration of ECS groups",
frame = FALSE,
title.size = .7,
inner.margins = c(0.1, 0.1, 0.05, 0.05)
)
```
```{r plot_admin_ecs_normed_choropleth, fig.width=4, fig.show="hold", fig.cap="Figure 3"}
@ -573,6 +566,7 @@ urbanrural$dtas_percent<- prop.table(urbanrural$dtas_count)
# calculate count of permaculture for fields in urbanrural
urbanrural$permaculture_count <- poly.counts(permaculture,urbanrural)
urbanrural$permaculture_percent<- prop.table(urbanrural$permaculture_count)
```
Rather than bifurcate congregations into an urban/rural dichotomy, for this study we used the Scottish Government's six-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:
@ -619,7 +613,7 @@ ggplot(urbanrural_gathered,
# Generate static plot for printing
tm_shape(urbanrural_sf_simplified) +
tm_polygons(col = "UR8FOLD", palette = "BrBG", lwd=0.001) +
tm_polygons(col = "UR8FOLD", palette = "BrBG", lwd=0.001, n=8) +
tm_shape(ecs_sf) +
tm_dots("red", size = .05, alpha = .4) +
tm_scale_bar(position = c("left", "bottom")) +
@ -910,6 +904,13 @@ st_crs(forestinv) <- 27700
st_crs(forestinv_buf50) <- 27700
st_crs(forestinv_buf500) <- 27700
# TODO: consider subsetting instead here, e.g.:
# plot(lnd, col = "lightgrey") # plot the london_sport object
# sel <- lnd$Partic_Per > 25
# plot(lnd[ sel, ], col = "turquoise", add = TRUE) # add selected zones to map
# from https://gotellilab.github.io/Bio381/StudentPresentations/SpatialDataTutorial.html
ecs_sf_sssi <- st_within(ecs_sf, sssi)
ecs_sf_sssi50m <- st_within(ecs_sf, sssi_buf50)
ecs_sf_sssi500m <- st_within(ecs_sf, sssi_buf500)