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Open Source Science

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

Computational tools for urban analysis

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

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (11.4%) to scientific vocabulary

Keywords

architecture centrality geographical-information-system landuse morphometrics network-analysis network-centralities network-topology networks networkx numba numpy openstreetmap osmnx python3 shapely spatial-analysis spatial-data spatial-data-analysis urban-planning
Last synced: 5 months ago · JSON representation

Repository

Computational tools for urban analysis

Basic Info
Statistics
  • Stars: 108
  • Watchers: 6
  • Forks: 9
  • Open Issues: 2
  • Releases: 98
Topics
architecture centrality geographical-information-system landuse morphometrics network-analysis network-centralities network-topology networks networkx numba numpy openstreetmap osmnx python3 shapely spatial-analysis spatial-data spatial-data-analysis urban-planning
Created over 7 years ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

cityseer

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

PyPI version

publish package

deploy docs

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

bash pip install cityseer

Development

brew install uv rust rust-analyzer rustfmt uv sync

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 either shortest or simplest path heuristics on either primal or 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. These can likewise be applied with either shortest or simplest path heuristics and on either primal or 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.

Owner

  • Name: Benchmark Urbanism
  • Login: benchmark-urbanism
  • Kind: organization
  • Email: info@benchmarkurbanism.com
  • Location: London

GitHub Events

Total
  • Create event: 96
  • Issues event: 1
  • Release event: 9
  • Watch event: 13
  • Delete event: 14
  • Issue comment event: 22
  • Push event: 166
  • Pull request event: 39
  • Fork event: 4
Last Year
  • Create event: 96
  • Issues event: 1
  • Release event: 9
  • Watch event: 13
  • Delete event: 14
  • Issue comment event: 22
  • Push event: 166
  • Pull request event: 39
  • Fork event: 4

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 1,144
  • Total Committers: 2
  • Avg Commits per committer: 572.0
  • Development Distribution Score (DDS): 0.001
Past Year
  • Commits: 218
  • Committers: 1
  • Avg Commits per committer: 218.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Gareth Simons g****s@m****m 1,143
Martin Fleischmann m****n@m****t 1
Committer Domains (Top 20 + Academic)
martinfleischmann.net: 1 me.com: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 18
  • Total pull requests: 130
  • Average time to close issues: 12 months
  • Average time to close pull requests: 9 days
  • Total issue authors: 6
  • Total pull request authors: 3
  • Average comments per issue: 1.17
  • Average comments per pull request: 0.87
  • Merged pull requests: 104
  • Bot issues: 0
  • Bot pull requests: 22
Past Year
  • Issues: 1
  • Pull requests: 33
  • Average time to close issues: N/A
  • Average time to close pull requests: 11 days
  • Issue authors: 1
  • Pull request authors: 2
  • Average comments per issue: 0.0
  • Average comments per pull request: 0.91
  • Merged pull requests: 24
  • Bot issues: 0
  • Bot pull requests: 8
View more stats
Top Authors
Issue Authors
  • songololo (12)
  • martinfleis (2)
  • 329081772 (1)
  • mohdalattar (1)
  • gregmaya (1)
  • sepehrzh (1)
Pull Request Authors
  • songololo (122)
  • dependabot[bot] (35)
  • martinfleis (1)
Top Labels
Issue Labels
enhancement (7) question (1)
Pull Request Labels
dependencies (35) javascript (16)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 18,365 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 227
  • Total maintainers: 1
pypi.org: cityseer

Computational tools for network-based pedestrian-scale urban analysis

  • Versions: 227
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 18,365 Last month
Rankings
Downloads: 2.2%
Dependent packages count: 10.1%
Average: 11.3%
Dependent repos count: 21.6%
Maintainers (1)
shongololo
Last synced: 6 months ago

Dependencies

.github/workflows/firebase-hosting-merge.yml actions
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.github/workflows/firebase-hosting-pull-request.yml actions
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  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pdm-project/setup-pdm main composite
.github/workflows/publish_package.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pdm-project/setup-pdm main composite
  • pypa/gh-action-pypi-publish release/v1 composite
.github/workflows/publish_package_dev.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v3 composite
  • pdm-project/setup-pdm main composite
  • pypa/gh-action-pypi-publish release/v1 composite
docs/package-lock.json npm
  • 890 dependencies
docs/package.json npm
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pyproject.toml pypi
  • geopandas >=0.12.2
  • matplotlib >=3.5.1
  • networkx >=2.8.8
  • numba >=0.56.2
  • numba-progress >=0.0.3
  • numpy >=1.23.3
  • pyproj >=3.3.0
  • requests >=2.27.1
  • scikit-learn >=1.0.2
  • shapely >=2.0.1
  • tqdm >=4.63.1
  • utm >=0.7.0