hexseg: simplifying two popular methods used in crime place studies
Published 8 February 2026
hexseg is a simple Python package I created to simplify generating statistics to prioritise locations for crime reduction efforts. It assists in streamlining two popular count-based methods by creating hexagonal grids or street-segment analyses across geographic study areas.
It is designed with crime analysis in mind, but may also be suitable for other urban planning and resource allocation tasks.
Features
- Hexagonal grid analyses: generate Uber hexagon layers over a defined study area and calculate count and weight-based statistics to identify and prioritise locations.
- Hexagonal grid statistics: calculate spatially lagged statistics (mean or sum of neighbours), generate global and local ranks and z-scores.
- Road segment analysis: calculate counts and ranks for road segments, or for contiguous clusters of segments, to identify and prioritise locations.
- Interactive visualisation: add priority hexagons and/or segments to interactive maps using
folium.
Installation
Install via pip:
Alternatively, install directly via GitHub:
pip install git+https://github.com/routineactivity/hexseg.git
Quick Start
Here is an example to get started quickly.
Hexagon Grid Example
In this example, you can produce a range of count and weight statistics to prioritise places within hexagons.
import geopandas as gpd
import hexseg as HS
# filepaths
base = "https://raw.githubusercontent.com/routineactivity/hexseg/main/data"
districts = f"{base}/cleveland_districts.gpkg"
roads = f"{base}/cleveland_roads.gpkg"
crimes = f"{base}/cleveland_sample_data.gpkg"
gdf_districts = gpd.read_file(districts)
gdf_roads = gpd.read_file(roads)
gdf_crimes = gpd.read_file(crimes)
# generate hexagons for study boundaries
hexes = HS.get_hexagons(
gdf_districts,
name_col="lad21nm",
resolution=9
)
# summarise crime counts and/or weights from crime data to hexagons
hex_both = HS.summarise_by_hex(
hexes_gdf=hexes,
crimes_gdf=gdf_crimes,
count_col="any",
weight_col="pseudo_harm"
)
# optional: add spatial lagged means/sums to counts/weights
# for nearest neighbours
# default neighbours is 6 - typically a 'donut' around hexagons,
# unless on edge of boundary
hex_lagged = HS.add_spatial_lag(
hexes_gdf=hex_both,
count_col="crime_count",
weight_col="crime_weight",
k=6
)
# add z-scores and ranks (global and local) to measure/s
# being used for prioritisation
hex_stats = HS.add_spatial_stats(
hex_lagged,
col="weight_plus_mean_sqrt",
group_col="geo_boundary"
)
Output: crime harm weighted hexagon for Cleveland Police, UK
Plotting Hexagon Results
Choose which measure you want to map outputs for.
The example below selects the top 20 hexagons ranked by crime-harm weight plus mean, grouped by district. By this measure, the top 20 hexagons are selected for each district.
m = HS.create_folium_map(
hex_gdf=hex_stats,
hex_query="weight_plus_mean_sqrt_rank_by_geo_boundary <= 20",
seg_gdf=None,
seg_query=None,
district_gdf=gdf_districts,
district_query=None
)
Plotting top 20 hexagons in the police force area
Street Segment Example
In this example, you can calculate crime counts for individual street segments or contiguous clusters of segments for easier prioritisation.
Counting by individual segments alone often identifies more locations than can realistically be addressed with available resources. By clustering segments, you can create manageable areas for targeted interventions, customised based on segment size — reflecting patrol distance or officer coverage — and crime thresholds.
Segment clusters can be used independently or combined with hexagonal grids to highlight priority streets within key hexagons.
import geopandas as gpd
import hexseg as HS
# filepaths
base = "https://raw.githubusercontent.com/routineactivity/hexseg/main/data"
districts = f"{base}/bkn_precincts.gpkg"
roads = f"{base}/bkn_roads_lion.gpkg"
crimes = f"{base}/bkn_sample_data.gpkg"
gdf_districts = gpd.read_file(districts)
gdf_roads = gpd.read_file(roads)
gdf_crimes = gpd.read_file(crimes)
# summarise number of crimes by segment
roads_with_counts = HS.count_crimes_by_nearest_road(
crimes_gdf=gdf_crimes,
roads_gdf=gdf_roads,
max_dist=75 # set maximum distance from segment to point
)
# build adjacency graph
G = HS.build_adj_graph(
roads_with_counts,
fid_col=None,
crime_count_col="crime_count"
)
# create user-defined clusters of segments
clusters = HS.segment_clusters(
G,
min_size=5,
max_size=8,
min_crimes=100
)
# add cluster id and sum of crime in clusters to segment layer
gdf_clusters = HS.clusters_to_gdf(
clusters,
G,
roads_with_counts,
fid_col="fid",
crime_count_col="crime_count"
)
Plotting Segment Results
Visualise the street-segment clusters:
m = HS.create_folium_map(
hex_gdf=None,
hex_query=None,
seg_gdf=gdf_clusters,
seg_query="cluster_crime_sum > 200",
district_gdf=gdf_districts,
district_query=None
)
m
Or visualise the highest individual segments:
# If you don't want to cluster segments and just see the highest,
# use the roads_with_counts object you created
m = HS.create_folium_map(
hex_gdf=None,
hex_query=None,
seg_gdf=roads_with_counts,
seg_query="crime_count > 20",
district_gdf=gdf_districts,
district_query=None
)
m
Top image shows clustered high-crime segments; bottom image shows individual high-crime segments.
Use Cases
- Public Safety: identify and statistically rank places to prioritise police resource allocation.
- Urban Planning: understand distributions of other datasets for planning purposes where point data is available, such as education, social care, employment or public health.
I originally posted this on Medium