cityseer 4.25.0b24

Computational tools for network-based pedestrian-scale urban analysis

cityseer

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

Examples: https://benchmark-urbanism.github.io/cityseer-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.