Exploring street visual audits to make sense of unequal urban landscapes

Abstract

With the growing availability of panoramic street-level imagery, on platforms such as Google Street View, researchers can investigate urban landscapes in innovative ways. Vir tual neighbourhood audits enable the use of this information to describe urban landscapes and their implications for people. However, most publications exploring these possibilities rely on highly specialized programming skills not yet generalized among social research ers. In this study, we make use of panoramic street-level imagery to assess fve munici palities and the territorial and spatial inequalities that shape them. It adds to the literature by proposing a mixed method approach that accounts for urban landscape multi-thematic dimensionality, combining a non-computational data extraction procedure with a multivariate analysis that researchers with low programming expertise can replicate. Observational data not only captured the diferences between territories as previously known but also pro vided new insights into territorial inequalities, ofering considerations for potential urban management priorities. Illustrating alternative ways to use open visual data enhances the possibility of insight and understanding of urban landscapes, identifying promising areas for multidisciplinary partnerships.