1. Multi-dimensional urban segregation in João Pessoa, a coastal Brazilian northeastern city.
- Author
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Donegan, Lucy and Tavares, Felipe
- Subjects
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PUBLIC spaces , *URBAN planning , *ENVIRONMENTAL policy , *URBAN policy , *K-means clustering - Abstract
Urban segregation can happen in many dimensions. Its study reveals city inequalities to inform urban planning policies; access to green and leisure spaces can contribute to well-being and social mixing. Different studies underline different characteristics. However, studies in Latin America, especially north-east Brazil, explore few segregation dimensions. This paper investigates multi-dimensional socio-spatial patterns in a broader urban area of João Pessoa city, relating residential census information - as income, race/colour, literacy and types of dwellings -, space syntax street network centralities, and public leisure spaces access. Variables were correlated and characterized k-means groups for sectors and neighbourhoods. Results indicate spatial wealth patterns with more whites and literates living in more apartments in areas stemming from the city expansion to the coast beyond municipal boundaries. Impoverished areas concentrate further from the sea, with barely any access to large leisure areas, transitioned by the city centre and Bancários neighbourhoods. Income was related more to access to large leisure areas than city-scale street network centralities. K-means clustering indicated heterogeneous profiles as poorer clusters, despite lower street network centrality and little sanitation, were either the densest or sparsest. Meanwhile, areas with higher street network access exhibited intermediate-to-high rather than highest-income inhabitants. Findings indicate a need to look over municipal boundaries and distribute better city amenities. • Findings indicate a tendency of white, literate, and wealthy living in apartments • Low income, particularly very low, have barely any reach to large leisure areas • Very high-income groups have access to large leisure areas, most close to the sea • K-means shows two poorest areas profiles: sparsest or densest, located differently • Intermediate income clusters exhibit higher street integration than higher income [ABSTRACT FROM AUTHOR]
- Published
- 2024
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