cityseer

What it is and what it does

Cityseer is a Python package for pedestrian-scale urban network analysis, combining network topology, geospatial data, and statistical methods to measure urban form and accessibility. It computes network centrality metrics (including harmonic closeness and segment-weighted variants) on primal or dual graphs, calculates land-use accessibility and mixed-use indices with dynamic aggregation, and performs pedestrian-flow betweenness analysis. The package integrates with the NumPy ecosystem (NetworkX, GeoPandas, Shapely, Rasterio) and uses Rust-backed algorithms for computationally intensive loops, enabling analysis of large or decomposed street networks.

Typical workflows involve extracting street networks from OpenStreetMap via osmnx, assigning land-use or other contextual data to street segments, and computing multi-scalar metrics from selected analysis points. The package supports high-resolution, moving-window analysis with strict network-distance thresholds and facilitates downstream machine learning and data science tasks by generating multi-variable datasets. It is designed for urbanists, researchers, and planners conducting computational urban analytics.

Use it for:

• Compute network centrality measures on street networks to identify structurally important locations.
• Assess land-use accessibility and mixed-use diversity around points of interest using network-distance thresholds.
• Analyze pedestrian-flow demand and origin-destination betweenness to understand movement patterns.
• Generate multi-variable urban datasets combining centrality and accessibility for machine learning.
• Perform high-resolution moving-window urban morphometric analysis with network-based distance constraints.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Computes network centrality, land-use accessibility, mixed-use indices, and pedestrian-flow analysis on street networks extracted from OpenStreetMap or custom graphs.

Yes, if you are conducting pedestrian-scale urban network analysis or research. The package is actively maintained, supports current Python versions, has no known vulnerabilities, and integrates well with standard geospatial tools. The AGPL-3.0 license is permissive for open-source and research use but requires review for proprietary integration. Medium install friction is acceptable for specialized geospatial work. Not suitable if you need a lightweight, minimal-dependency tool or are restricted to permissive licenses.

Install

cityseer on PyPI

pip install cityseer

uv add cityseer

poetry add cityseer

Installing cityseer

Before you install

Medium install friction due to 12 runtime dependencies including geospatial libraries (geopandas, rasterio, pyogrio, osmnx) and compiled extensions (Rust-backed). Active maintenance with a release 14 days old and recent commits; Python 3.10–3.14 support is current.

License in practice

AGPL-3.0 license requires derivative works and modifications to be shared under the same license. Suitable for research and open-source projects; review licensing obligations if integrating into proprietary software.

Quickstart

pip install cityseer

import cityseer
from cityseer import metrics
# Load a network graph and compute centrality metrics
# See https://cityseer.benchmarkurbanism.com/ for detailed examples

Requires Python 3.10–3.14; geospatial dependencies (geopandas, rasterio) may need system libraries; Rust toolchain required only for development builds.

Verify before relying

• Performance characteristics and scalability limits for large or decomposed graphs.
• Whether the package supports custom graph formats beyond osmnx and NetworkX structures.

Package facts

Evidence: cityseer-5.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; cityseer-5.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; cityseer-5.8.0-cp310-cp310-win_amd64.whl; cityseer-5.8.0-cp311-cp311-macosx_10_12_x86_64.whl; cityseer-5.8.0-cp311-cp311-macosx_11_0_arm64.whl; cityseer-5.8.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; cityseer-5.8.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; cityseer-5.8.0-cp311-cp311-win_amd64.whl; cityseer-5.8.0-cp312-cp312-macosx_10_12_x86_64.whl; cityseer-5.8.0-cp312-cp312-macosx_11_0_arm64.whl; cityseer-5.8.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; cityseer-5.8.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; cityseer-5.8.0-cp312-cp312-win_amd64.whl; cityseer-5.8.0-cp313-cp313-macosx_10_12_x86_64.whl; cityseer-5.8.0-cp313-cp313-macosx_11_0_arm64.whl; cityseer-5.8.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; cityseer-5.8.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; cityseer-5.8.0-cp313-cp313-win_amd64.whl; cityseer-5.8.0-cp314-cp314-macosx_10_12_x86_64.whl; cityseer-5.8.0-cp314-cp314-macosx_11_0_arm64.whl

Keywords: network-topology, numpy, architecture, openstreetmap, urban-planning, python3, networkx, networks, spatial-analysis, geographical-information-system, spatial-data, morphometrics, network-analysis, momepy, spatial-data-analysis, centrality, shapely, landuse, osmnx, network-centralities

Tags

More Information Analysis packages