Simons, Gareth; (2021) The cityseer Python package for pedestrian-scale network-based urban analysis. Environment and Planning B: Urban Analytics and City Science 10.1177/23998083221133827.

Preview

Text 23998083221133827.pdf - Published Version Download (2MB) | Preview

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/

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as CSVXMLJSON

Downloads by country - last 12 months

Export as XMLCSVJSON

Archive Staff Only

View Item