---
id: cityseer
version: "5.8.0"
license: AGPL-3.0
license_treatment: agpl
maintenance: active
---

# cityseer — Computational tools for network-based pedestrian-scale urban analysis

License: agpl · Maintenance: active · Downloads: 126.8K/mo

## 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 above — 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

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

- License: AGPL-3.0 (agpl)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 126.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

network centrality analysis, urban pedestrian-scale metrics, land-use accessibility, street network analysis, mixed-use urban metrics, openstreetmap network analysis, spatial network computation, urban-analysis, network-centrality, geospatial

[View on SkillFed](https://skillfed.io/packages/cityseer) · [View on PyPI](https://pypi.org/project/cityseer/)