Dealing with socio-spatial conflicts in the context of the Program 100 Parks for S...
Urban insertion: from the slums to Minha Casa Minha Vida program
Grant number: | 20/03449-2 |
Support Opportunities: | Scholarships in Brazil - Master |
Start date until: | June 01, 2020 |
End date until: | May 31, 2022 |
Field of knowledge: | Applied Social Sciences - Urban and Regional Planning - Urban and Regional Services |
Principal Investigator: | Suzana Pasternak |
Grantee: | Leticia Casagrande Dupont |
Host Institution: | Faculdade de Arquitetura e Urbanismo (FAU). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Abstract The Brazilian slumps have historically been represented as places of absence, that is, lack of public services and infrastructure, and categorized as homogeneously poor and segregated. However, in addition to understanding the process of favelization and segregation present in the urban space of the metropolises in Brazil, in order to deepen the discussion of the levels of segregation existing within the poorest areas, it is necessary to recognize the differences in the microscale of the slumps. Given the heterogeneity of physical spaces and the profile of the population residing in the slumps, it is assumed that the socio-spatial segregation present in the city is also reproduced internally in the slumps. In this context, we seek to investigate the patterns of socio-spatial segregation in the slumps by creating an indicator of housing precariousness in the households, which covers the complexity of factors related to housing. It seeks to explore the relationship between the precariousness of households and the sociodemographic characteristics of the residents, highlighting the indispensable intersection of socio-spatial segregation with racial, gender and class issues. For the elaboration of the research it is intended to use a spatial and quantitative approach, analyzing the Olaria, Capadocia, Pedra Branca and Vila Nova Esperança slumps of the city of São Paulo as case studies from an exploratory bias. Data analysis is based on the use of statistical and geoprocessing tools in a GIS environment (Geographic Information System). (AU) | |
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