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Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil.

Hacker KP, Seto KC, Costa F, Corburn J, Reis MG, Ko AI, Diuk-Wasser MA - Int J Health Geogr (2013)

Bottom Line: The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features.Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting.These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College St, New Haven, CT 06511, USA. maria.diuk@yale.edu.

ABSTRACT

Background: The expansion of urban slums is a key challenge for public and social policy in the 21st century. The heterogeneous and dynamic nature of slum communities limits the use of rigid slum definitions. A systematic and flexible approach to characterize, delineate and model urban slum structure at an operational resolution is essential to plan, deploy, and monitor interventions at the local and national level.

Methods: We modeled the multi-dimensional structure of urban slums in the city of Salvador, a city of 3 million inhabitants in Brazil, by integrating census-derived socioeconomic variables and remotely-sensed land cover variables. We assessed the correlation between the two sets of variables using canonical correlation analysis, identified land cover proxies for the socioeconomic variables, and produced an integrated map of deprivation in Salvador at 30 m × 30 m resolution.

Results: The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features. The first canonical axis captured a gradient from crowded, low-income communities with corrugated roof housing to higher-income communities. The second canonical axis discriminated among socioeconomic variables characterizing the most marginalized census tracts, those without access to sanitation or piped water. The third canonical axis accounted for the least amount of variation, but discriminated between high-income areas with white-painted or tiled roofs from lower-income areas.

Conclusions: Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting. These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading.

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Flow diagram of general approach. Flow diagram describing the general approach for creating a final map of deprivation in Salvador. The approach is composed of three main steps: A) Selection of variables, B) Canonical correlation analysis and C) Mapping using weights from the canonical correlation analysis.
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Figure 1: Flow diagram of general approach. Flow diagram describing the general approach for creating a final map of deprivation in Salvador. The approach is composed of three main steps: A) Selection of variables, B) Canonical correlation analysis and C) Mapping using weights from the canonical correlation analysis.

Mentions: We used canonical correlation analysis to model the association between sets of variables characterizing the land cover and socioeconomic urban features in Salvador, Brazil, and applied this association to map slums at a higher spatial resolution than the smallest census unit (Figure 1). Land cover and socioeconomic variables were derived from a 2002 land cover classification and the 2000 Brazilian census, respectively (Figure 1A). Canonical correlation analysis identified a set of orthogonal axes that maximized the correlation between the land cover and socioeconomic variable sets (Figure 1B). The first two canonical dimensions were used to map deprivation across Salvador, Brazil, at a 30 m × 30 m resolution, using the canonical loadings as weights to reflect the importance of each variable in the overall urban structure (Figure 1C).


Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil.

Hacker KP, Seto KC, Costa F, Corburn J, Reis MG, Ko AI, Diuk-Wasser MA - Int J Health Geogr (2013)

Flow diagram of general approach. Flow diagram describing the general approach for creating a final map of deprivation in Salvador. The approach is composed of three main steps: A) Selection of variables, B) Canonical correlation analysis and C) Mapping using weights from the canonical correlation analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924348&req=5

Figure 1: Flow diagram of general approach. Flow diagram describing the general approach for creating a final map of deprivation in Salvador. The approach is composed of three main steps: A) Selection of variables, B) Canonical correlation analysis and C) Mapping using weights from the canonical correlation analysis.
Mentions: We used canonical correlation analysis to model the association between sets of variables characterizing the land cover and socioeconomic urban features in Salvador, Brazil, and applied this association to map slums at a higher spatial resolution than the smallest census unit (Figure 1). Land cover and socioeconomic variables were derived from a 2002 land cover classification and the 2000 Brazilian census, respectively (Figure 1A). Canonical correlation analysis identified a set of orthogonal axes that maximized the correlation between the land cover and socioeconomic variable sets (Figure 1B). The first two canonical dimensions were used to map deprivation across Salvador, Brazil, at a 30 m × 30 m resolution, using the canonical loadings as weights to reflect the importance of each variable in the overall urban structure (Figure 1C).

Bottom Line: The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features.Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting.These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology of Microbial Disease, Yale School of Public Health, 60 College St, New Haven, CT 06511, USA. maria.diuk@yale.edu.

ABSTRACT

Background: The expansion of urban slums is a key challenge for public and social policy in the 21st century. The heterogeneous and dynamic nature of slum communities limits the use of rigid slum definitions. A systematic and flexible approach to characterize, delineate and model urban slum structure at an operational resolution is essential to plan, deploy, and monitor interventions at the local and national level.

Methods: We modeled the multi-dimensional structure of urban slums in the city of Salvador, a city of 3 million inhabitants in Brazil, by integrating census-derived socioeconomic variables and remotely-sensed land cover variables. We assessed the correlation between the two sets of variables using canonical correlation analysis, identified land cover proxies for the socioeconomic variables, and produced an integrated map of deprivation in Salvador at 30 m × 30 m resolution.

Results: The canonical analysis identified three significant ordination axes that described the structure of Salvador census tracts according to land cover and socioeconomic features. The first canonical axis captured a gradient from crowded, low-income communities with corrugated roof housing to higher-income communities. The second canonical axis discriminated among socioeconomic variables characterizing the most marginalized census tracts, those without access to sanitation or piped water. The third canonical axis accounted for the least amount of variation, but discriminated between high-income areas with white-painted or tiled roofs from lower-income areas.

Conclusions: Our approach captures the socioeconomic and land cover heterogeneity within and between slum settlements and identifies the most marginalized communities in a large, complex urban setting. These findings indicate that changes in the canonical scores for slum areas can be used to track their evolution and to monitor the impact of development programs such as slum upgrading.

Show MeSH
Related in: MedlinePlus