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

TL;DR

The cityseer Python package offers advanced tools for detailed pedestrian-scale street network and land-use analysis, enabling urban researchers to assess neighborhood vibrancy with high spatial precision and computational efficiency.

Contribution

It introduces a comprehensive Python toolkit integrating network analysis, land-use measures, and spatial computations specifically designed for pedestrian-scale urban analysis.

Findings

Efficient algorithms scale to large cities and regions.

Dynamic accessibility measures incorporate walking distance and approach direction.

Robust tools facilitate OpenStreetMap data analysis.

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 https://cityseer.benchmarkurbanism.com, and the Github repository is available at https://github.com/benchmark-urbanism/cityseer. Example notebooks are available at https://cityseer.benchmarkurbanism.com/examples/.