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

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  • Gareth Simons

Abstract

cityseer-api is a Python package consisting of computational tools for fine-grained street-network and land-use analysis, helpful in assessing the morphological precursors to vibrant neighbourhoods. It is underpinned by network-based methods developed specifically for urban analysis at the pedestrian scale. cityseer-api computes a variety of node and segment-based network centrality methods, land-use accessibility and mixed-use measures, and statistical aggregations. Accessibilities and aggregations are computed dynamically over the street-network while taking walking distance thresholds and the direction of approach into account, and can optionally incorporate spatial impedances and network decomposition to increase spatial precision. The use of Python facilitates compatibility with popular computational tools for network manipulation (NetworkX), geospatial topology (shapely), geospatial data state management (GeoPandas), and the NumPy stack of scientific packages. The provision of robust network cleaning tools aids the use of OpenStreetMap data for network analysis. Underlying loop-intensive algorithms are implemented in Numba JIT compiled code so that the methods scale efficiently to larger cities and regions. Online documentation is available from cityseer.benchmarkurbanism.com, and the Github repository is available at github.com/benchmark-urbanism/cityseer. Example notebooks are available at cityseer.benchmarkurbanism.com/examples/

Suggested Citation

  • Gareth Simons, 2023. "The cityseer Python package for pedestrian-scale network-based urban analysis," Environment and Planning B, , vol. 50(5), pages 1328-1344, June.
    • Handle: RePEc:sae:envirb:v:50:y:2023:i:5:p:1328-1344
    DOI: 10.1177/23998083221133827
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    References listed on IDEAS

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    1. Boeing, Geoff, 2017. "OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks," SocArXiv q86sd, Center for Open Science.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Juan C Duque & Henry Laniado & Adriano Polo, 2018. "S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-25, November.
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    1. Paiva Neto, José B. & Santos, Narciso F. & Orrico Filho, Romulo D., 2025. "Paths to prosperity: How transport networks and income accessibility shape retail location," Journal of Transport Geography, Elsevier, vol. 128(C).

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