How Big are Chicago Neighborhoods?

Premise

In certain methods of spatial cross-validation, you need to set an inclusion radius and a buffer distance for the folds into which you divide your data. I am working with census tracts, and think it would make sense to use distances based on neighborhoods to set the inclusion radius and buffer distance.

Image source: A and N mortgage

Set-Up

Load the data wrangling and mapping libraries, then import Chicago neighborhoods:

Code

library(tidyverse)
library(sf)
library(tmap)

# Source: Chicago Data Portal - Boundaries - Neighborhoods
# URL: https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Neighborhoods/bbvz-uum9
neighborhoods <- st_read(here::here("data", "boundaries_neighborhoods_chicago.kml"))

Reading layer `Layer0' from data source 
  `/home/riggins/blog/data/boundaries_neighborhoods_chicago.kml' 
  using driver `LIBKML'
Simple feature collection with 98 features and 15 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
Geodetic CRS:  WGS 84

Calculations

1. Use S2 backend of sf library to calculate areas of each neighborhood
2. Calculate a rough radius of each neighborhood by treating it as if it were a circle
3. Calcuate the average rough radius

Code

neighborhoods |> 
    mutate(
        area = st_area(neighborhoods),
        rough_radius = sqrt(area/pi)
    ) |> 
    summarize(avg_rough_radius = mean(rough_radius))

Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -87.94011 ymin: 41.64454 xmax: -87.52414 ymax: 42.02304
Geodetic CRS:  WGS 84
  avg_rough_radius                       geometry
1     1273.473 [m] MULTIPOLYGON (((-87.94001 4...

The average rough radius is about 1273 meters.

Visualize

Visualize what it looks like to add a 1300 meter buffer zone around each neighborhood:

Code

buffers <- st_buffer(neighborhoods, 1300)

Code

tmap_mode("view")
tm_shape(buffers) + tm_polygons(col = "pri_neigh", alpha = 0.5, border.alpha = 0) + tm_shape(neighborhoods) + tm_polygons(alpha = 0)