Getting Started

cityseer provides tools for street-network and land-use analysis at the pedestrian scale. It measures how central each street is in the walking network (centrality), how easily pedestrians can reach amenities like shops or parks (accessibility), and how numerical attributes such as property prices are distributed across neighbourhoods (statistical aggregation). All measures are computed along actual walking routes rather than straight-line distances.

cityseer integrates with NetworkX, GeoPandas, OSMnx, and the broader Python geospatial ecosystem including shapely, numpy, and momepy. The underlying algorithms are implemented in Rust for performance and scale to large networks. Code tests are run against Python versions 3.10 - 3.13.

Installation

pip install --upgrade cityseer

cityseer requires Python 3.10 or later. The underlying algorithms are implemented in Rust and distributed as pre-compiled wheels, so no Rust toolchain is needed. A projected coordinate reference system (CRS) is required for all analyses; coordinates must be in metres, not degrees. Use epsg.io to find the appropriate EPSG code for your study area (e.g. EPSG:32630 for London, EPSG:32632 for central Europe, EPSG:2154 for France).

Quick Start

The Quickstart notebook provides a full walkthrough. The following minimal example downloads a street network from OpenStreetMap, computes centrality, and plots the result:

from shapely.geometry import box
from cityseer.network import CityNetwork

# Define a bounding box in WGS84 (lon, lat)
polygon = box(-0.13, 51.51, -0.12, 51.52)

# Build the network (projected to UTM zone 30N)
cn = CityNetwork.from_osm(polygon, to_crs_code=32630)

# Compute shortest-path centrality at 400m and 800m walking distance
cn.centrality_shortest(distances=[400, 800])

# Export as a GeoDataFrame with original street geometries
result_gdf = cn.to_geopandas()

# Visualise betweenness at 800m (in a Jupyter notebook; for scripts, call plt.show())
result_gdf.plot(column="cc_betweenness_800", cmap="inferno", linewidth=0.5)

Distance thresholds can also be specified as walking times using the minutes parameter:

cn.centrality_shortest(minutes=[5, 10, 20])  # assumes default walking speed of 1.33 m/s

Saving and loading

Networks can be saved to disk and restored later, preserving all computed metrics:

cn.save("my_network")
# Creates: my_network.nodes.parquet, my_network.state.pkl

cn_restored = CityNetwork.load("my_network")

Learning Path

New to Python or computational notebooks? Work through the Python 101 course first:

1. Notebooks: What computational notebooks are and how to use marimo.
2. Python Basics: Variables, data types, collections, control flow, and functions.
3. Spatial Data: Points, lines, and polygons with the shapely package.
4. GeoPandas: Handling geospatial datasets with geopandas.
5. Urban Analytics: Downloading OSM data with osmnx and analysing urban morphology with momepy.
6. Data Science: Dimensionality reduction, clustering, and prediction with seaborn and scikit-learn.

Each lesson page renders the executed notebook and offers the raw .py file for download; open a downloaded lesson interactively with uv run marimo edit <file>. Alternatively, create a notebook in whichever environment you prefer and copy the cells across as you work through a lesson: the code is plain Python.

Where Next

• The Quickstart notebook walks through a complete first analysis.
• The Guide explains how cityseer frames analysis: fundamentals, networks, centrality, and land-use.
• The examples section holds worked recipes for every module, built on bundled real-world data.
• The API reference documents every function and parameter.

Support and Attribution

Report bugs to the issues tracker; for questions and workflow help, start a discussion.

Please cite the tools you use: for cityseer, cite the associated paper in research outputs, and as a minimum of good conscience, openly link to this documentation site from work that builds on the package. cityseer is licensed AGPLv3; get in touch for technical support or sponsored development.