knitr::opts_chunk$set( eval = FALSE, collapse = TRUE, comment = "#>" )
This vignettes demonstrates possibilities when zoning systems from different cities meet. It raises the question: how should different systems be combined geographically?
You need the latest version of the package:
remotes::install_github("zonebuilders/zonebuilder")
For this demo, we need the following libraries:
library(zonebuilder) # for the zoning system library(sf) # for processing spatial data library(dplyr) # for processing general data library(tmap) # for visualizing spatial data
We will apply the zoning system to the main Dutch cities, and analyse commuting patterns between the zones. The data can be read as follows:
NLD_cities = readRDS(url("https://github.com/zonebuilders/zonebuilder/releases/download/0.0.1/NLD_cities.Rds")) NLD_wijk_od = readRDS(url("https://github.com/zonebuilders/zonebuilder/releases/download/0.0.1/NLD_wijk_od.Rds")) NLD_wijk_centroids = readRDS(url("https://github.com/zonebuilders/zonebuilder/releases/download/0.0.1/NLD_wijk_centroids.Rds"))
Let's take a look at the NLD_cities
data:
NLD_cities %>% arrange(desc(population))
...and plot it on an interactive map:
tmap_mode("view") # enable interactive mode in tmap qtm(NLD_cities, symbols.size = "population")
The following code chunk generated zones for the Dutch cities:
zbs = do.call(rbind, lapply(1:nrow(NLD_cities), function(i) { ci = NLD_cities[i, ] # Amsterdam 5, Eindhoven-Rotterdam 4, Roermond-Zeeland 2, others 3 nrings = ifelse(ci$population < 60000, 2, ifelse(ci$population < 220000, 3, ifelse(ci$population < 800000, 4, 5))) zb = zb_zone(x = ci, n_circles = nrings) %>% mutate(name = ci$name, labelplus = paste(ci$name, label, sep = "_")) zb }))
tm_basemap("OpenStreetMap") + tm_shape(zbs) + tm_polygons(col = "circle_id", id = "labelplus", style = "cat", palette = "YlOrBr", alpha = 0.7) + tm_scale_bar()
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