cityseer
A Python package for pedestrian-scale network-based urban analysis: network centrality, land-use accessibility and mixed uses, statistical aggregation, pedestrian-flow (demand and origin-destination) betweenness, and street-level visibility.
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.
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 segment-weighted variants. 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
NumPyecosystem 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.
1 maintainer
Metadata
1 maintainer
Release files for cityseer 5.8.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| cityseer-5.8.0.tar.gz | 217.6 kB | With provenance | Details |
Built distributions (wheels)
Total release size: 38.7 MB
Release files / cityseer-5.8.0.tar.gz
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| Size | 1.8 MB |
| Tags | CPython 3.11 Linux glibc 2.17+ ARM64 |
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Transparency logRelease files / cityseer-5.8.0-cp311-cp311-macosx_11_0_arm64.whl
| Download URL | cityseer-5.8.0-cp311-cp311-macosx_11_0_arm64.whl |
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| Size | 1.6 MB |
| Tags | CPython 3.11 macOS 11.0+ ARM64 |
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Transparency logRelease files / cityseer-5.8.0-cp311-cp311-macosx_10_12_x86_64.whl
| Download URL | cityseer-5.8.0-cp311-cp311-macosx_10_12_x86_64.whl |
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| Size | 1.7 MB |
| Tags | CPython 3.11 macOS 10.12+ x86-64 |
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Transparency logRelease files / cityseer-5.8.0-cp310-cp310-win_amd64.whl
| Download URL | cityseer-5.8.0-cp310-cp310-win_amd64.whl |
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| Size | 1.5 MB |
| Tags | CPython 3.10 Windows x86-64 |
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Transparency logRelease files / cityseer-5.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | cityseer-5.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
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| Size | 1.8 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ x86-64 |
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Transparency logRelease files / cityseer-5.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
| Download URL | cityseer-5.8.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl |
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| Size | 1.8 MB |
| Tags | CPython 3.10 Linux glibc 2.17+ ARM64 |
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Transparency logSecurity for cityseer 5.8.0
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