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Notebook"],"sub_categories":[],"readme":"# cityseer\n\nA `Python` package for pedestrian-scale network-based urban analysis: network analysis, landuse accessibilities \u0026 mixed uses, statistical aggregations.\n\n[![PyPI version](https://badge.fury.io/py/cityseer.svg)](https://badge.fury.io/py/cityseer)\n\n[![publish package](https://github.com/benchmark-urbanism/cityseer-api/actions/workflows/publish_package.yml/badge.svg)](https://github.com/benchmark-urbanism/cityseer-api/actions/workflows/publish_package.yml)\n\n[![deploy docs](https://github.com/benchmark-urbanism/cityseer-api/actions/workflows/firebase-hosting-merge.yml/badge.svg)](https://github.com/benchmark-urbanism/cityseer-api/actions/workflows/firebase-hosting-merge.yml)\n\nExamples: \u003chttps://benchmark-urbanism.github.io/cityseer-examples/\u003e\n\nAPI Documentation: \u003chttps://cityseer.benchmarkurbanism.com/\u003e\n\nIssues: \u003chttps://github.com/benchmark-urbanism/cityseer-api/issues\u003e\n\nQuestions: \u003chttps://github.com/benchmark-urbanism/cityseer-api/discussions\u003e\n\n## Installation\n\n```bash\npip install cityseer\n```\n\n## Development\n\n`brew install uv rust rust-analyzer rustfmt`\n`uv sync`\n\n## Cite\n\nCite as: [The cityseer Python package for pedestrian-scale network-based urban analysis](https://journals.sagepub.com/doi/full/10.1177/23998083221133827)\n\n## Background\n\nThe `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:\n\n- 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.\n- 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\"_](https://arxiv.org/abs/2106.14040).\n- 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\"_](https://arxiv.org/abs/2106.14048).\n- 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\"_](https://arxiv.org/abs/2106.15363) and [_\"Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods\"_](https://arxiv.org/abs/2106.15364).\n- 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.\n- 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenchmark-urbanism%2Fcityseer-api","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbenchmark-urbanism%2Fcityseer-api","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbenchmark-urbanism%2Fcityseer-api/lists"}