Papers › The application of mixed-use measures at the pedestrian-scale

The application of mixed-use measures at the pedestrian-scale

26 Jun 2021arXiv:2106.14048links table onlyarchive 2025-07-28

Gareth D. Simons

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Mixed-use urbanism affords access to diverse assortments of land-uses within a pedestrian-accessible context. It confers advantages such as reductions to driving, air pollution, and Body Mass Index with associated increases in active transportation and improvements to health. However, whereas mixed-use urbanism is clearly beneficial, methods for measuring and assessing the presence of mixed-uses at a granular level of analysis remain murkier. This work demonstrates techniques for gauging mixed-uses in more spatially precise terms concurring more readily with an urbanist's conception of pedestrian-accessible mixed-uses. It does so through the use of the cityseer-api Python package, which facilitates the use of spatially granular land-use classification data assigned to adjacent street edges and then aggregated dynamically, with distances measured from each point of analysis to each accessible land-use while taking the direction of approach into account. It is argued that Hill Numbers is a suitable measure of diversity because it can mirror the intent of traditional indices while behaving more intuitively. Further, distance-weighted formulations of Hill diversity can be applied with spatial impedances, thus conferring a particularly spatially nuanced gauge of local access to mixed-uses. These methods and indices are demonstrated for Greater London with observations correlated to Principal Component Analysis derived from a range of land-use accessibilities measured from the same locations and for the same point-of-interest dataset. The Hill diversity measures, particularly the distance-weighted formulations, offer the most robust correlations for both expansive mixed-use districts and more local 'high-street' mixes of uses while yielding the most intuitive and spatially precise behaviour in the accompanying plots.

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