chicago_crimes: Chicago Crime (2023)

In treeSS: Tree-Spatial Scan Statistic for Cluster Detection

Chicago Crime (2023)

Description

Combined long-format dataset of crime incidents in the 77 community areas of Chicago, 2023. Use together with chicago_tree.

Usage

chicago_crimes

Format

A data.frame in long format with columns:

region_id

Integer identifier of the community area.

area_number

Official community area number (1–77).

name

Community area name.

population

Total incidents in the area. This is a compositional denominator (sums to the total incident count of the city) and is useful for analyses that ask "which crime types over-occur in which areas relative to the citywide profile". Not a population at risk.

pop_residential

Residential population of the community area. Use this as the denominator for incidence-rate analyses (incidents per resident). Source: U.S. Census Bureau, ACS 2020 5-year estimates, aggregated to community areas by CMAP Community Data Snapshots. The 77 values sum to approximately 2.71M, matching the published Chicago population.

x

Longitude of the centroid.

y

Latitude of the centroid.

node_id

Character leaf identifier from chicago_tree.

cases

Integer count of crime incidents.

Source

Crime incidents: City of Chicago Data Portal, Crimes – 2001 to Present.

Residential population: U.S. Census Bureau ACS 2020 5-year estimates, aggregated to community areas by the Chicago Metropolitan Agency for Planning (CMAP), Community Data Snapshots (2023 release). https://cmap.illinois.gov/data/community-data-snapshots/

See Also

chicago_tree, chicago_map

Examples

data(chicago_crimes)
head(chicago_crimes)
cat("Total incidents:", sum(chicago_crimes$cases), "\n")
cat("Total residents:",
    sum(unique(chicago_crimes[, c("area_number", "pop_residential")])$pop_residential),
    "\n")

treeSS documentation built on May 16, 2026, 1:08 a.m.