Network centrality measures and their correlation to mixed-uses at the pedestrian-scale

Abstract

Street network analysis holds appeal as a tool for the assessment of pedestrian connectivity and its relation to the intensity and mix of land-uses; however, application within urban-design triggers a range of questions on implementary specifics due to a variety of theories, methods, and considerations and it is not immediately clear which of these might be the most applicable at the pedestrian scale in relation to land-uses. It is, furthermore, difficult to directly evaluate differing approaches on a like-for-like basis without recourse to the underlying algorithms and computational workflows. To this end, the cityseer-api Python package is here used to develop, compute, and compare a range of centrality methods which are then applied to the Ordnance Survey Open Roads dataset for Greater London. The centralities are correlated to high-resolution land-use and mixed-used measures computed from the Ordnance Survey Points of Interest dataset for the same points of analysis using a spatially precise methodology based on network distances to premise locations. The comparisons show that mixed-uses correlate more strongly against closeness than betweenness centralities; segmented measures tend to offer slightly stronger correlations than node-based equivalents; weighted variants offer correlations similar to unweighted versions, but with a greater degree of spatial specificity; simplest-path methods confer an advantage in the context of local high-street mixed-uses but not necessarily for district-wide mixed-uses or land-use accessibilities; and the application of centrality measures to the dual network does not offer tangible benefits over the primal network.