cityseer

A Python package for pedestrian-scale network-based urban analysis: network analysis, landuse accessibilities & mixed uses, statistical aggregations.

Examples: https://cityseer.benchmarkurbanism.com/examples

API Documentation: https://cityseer.benchmarkurbanism.com/

Issues: https://github.com/benchmark-urbanism/cityseer-api/issues

Questions: https://github.com/benchmark-urbanism/cityseer-api/discussions

Installation

pip install cityseer

Development

Contributions are welcome — please open an issue or discussion before larger changes.

[!IMPORTANT] Active development happens on the dev branch; master tracks the latest released version. Please base branches and target pull requests against dev, not master:
gh pr create --base dev
Pushing an alpha tag (e.g. 4.25.0b1) from dev publishes a pre-release to PyPI for testing ahead of a stable release off master.

Setup

The loop-intensive algorithms are written in rust and exposed to Python via maturin, so a rust toolchain is required alongside uv:

brew install uv rust rust-analyzer rustfmt   # or your platform's equivalent
uv sync                                       # creates the venv and builds the rust extension

After editing rust sources, rebuild the extension before re-running Python:

uv run maturin develop                        # or re-run `uv sync`

Verify before pushing

Run the same formatting, linting, type-checking, and test suite that CI runs:

uv run poe verify_project                     # ruff format && ruff check && ty check && pytest ./tests

To preview the documentation site locally:

uv run poe docs_dev

Cite

Cite as: The cityseer Python package for pedestrian-scale network-based urban analysis

Background

The cityseer-api Python package addresses a range of issues specific to computational workflows for urban analytics from an urbanist's point of view and contributes a combination of techniques to support developments in this field:

High-resolution workflows including localised moving-window analysis with strict network-based distance thresholds; spatially precise assignment of land-use or other data points to adjacent street-fronts for improved contextual sensitivity; dynamic aggregation workflows which aggregate and compute distances on-the-fly from any selected point on the network to any accessible land-use or data point within a selected distance threshold; facilitation of workflows eschewing intervening steps of aggregation and associated issues such as ecological correlations; and the optional use of network decomposition to increase the resolution of the analysis.
Localised computation of network centralities using shortest paths on primal or dual graphs, and simplest-path heuristics on dual graphs, including tailored methods such as harmonic closeness centrality, and segmented versions of centrality (which convert centrality methods from a discretised to an explicitly continuous form). For more information, see "Network centrality measures and their correlation to mixed-uses at the pedestrian-scale".
Land-use accessibilities and mixed-use calculations incorporate dynamic and directional aggregation workflows with the optional use of spatial-impedance-weighted forms. Shortest-path workflows operate on primal or dual graphs, while simplest-path workflows require dual graphs. For more information, see "The application of mixed-use measures at the pedestrian-scale".
Network centralities dovetailed with land-use accessibilities, mixed-uses, and general statistical aggregations from the same points of analysis to generate multi-scalar and multi-variable datasets facilitating downstream data science and machine learning workflows. For examples, see "Untangling urban data signatures: unsupervised machine learning methods for the detection of urban archetypes at the pedestrian scale" and "Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods".
The inclusion of graph cleaning methods reduce topological distortions for higher quality network analysis and aggregation workflows while accommodating workflows bridging the wider NumPy ecosystem of scientific and geospatial packages.
Underlying loop-intensive algorithms are implemented in rust, allowing these methods to be applied to large and, optionally, decomposed graphs, which have substantial computational demands.

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