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A platform for research: civil engineering, architecture and urbanism
Using statistical modelling to identify precarious urban Settlements and their access to Water and Sanitation services in Brazil
SDG 6 aims universal coverage of Water and Sanitation services for all, under the principle of leaving no one behind. Thus, measuring progress on service levels cannot be based on averages, at the risk of not capturing the real situation in vulnerable areas such as Urban Precarious Settlements (UPS), to which the lack of appropriate indicators and representative data hinder access to the services. This paper aims to use statistical modelling to propose a new procedure to identify UPS in Brazil, to then measure their access to WS services and compare with general figures. The method was applied to the Federal District of Brazil, testing three statistical techniques: Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression. The latter presented the best predictive performance resulting in a sensitivity of 88,5% and an Area Under the Curve of 0,96. Overall results demonstrated inequalities of access among the poorest, particularly to sanitation.
Using statistical modelling to identify precarious urban Settlements and their access to Water and Sanitation services in Brazil
SDG 6 aims universal coverage of Water and Sanitation services for all, under the principle of leaving no one behind. Thus, measuring progress on service levels cannot be based on averages, at the risk of not capturing the real situation in vulnerable areas such as Urban Precarious Settlements (UPS), to which the lack of appropriate indicators and representative data hinder access to the services. This paper aims to use statistical modelling to propose a new procedure to identify UPS in Brazil, to then measure their access to WS services and compare with general figures. The method was applied to the Federal District of Brazil, testing three statistical techniques: Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression. The latter presented the best predictive performance resulting in a sensitivity of 88,5% and an Area Under the Curve of 0,96. Overall results demonstrated inequalities of access among the poorest, particularly to sanitation.
Using statistical modelling to identify precarious urban Settlements and their access to Water and Sanitation services in Brazil
Dos Santos, Laís Freitas Moreira (author) / de Maria Albuquerque Alves, Conceição (author)
Urban Water Journal ; 20 ; 1766-1783
2023-11-26
18 pages
Article (Journal)
Electronic Resource
Unknown
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