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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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Related in: MedlinePlus

Land cover classification. Landsat TM 2002 Land cover classification based on water, vegetation, sand, exposed soil, corrugated roofs, tile roofs, white-painted roofs, pavement, and cloud land covers.
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Figure 2: Land cover classification. Landsat TM 2002 Land cover classification based on water, vegetation, sand, exposed soil, corrugated roofs, tile roofs, white-painted roofs, pavement, and cloud land covers.

Mentions: To capture the structural quality of housing listed in the UN-Habitat definition of a slum, we generated a land cover classification of a Landsat TM image acquired on February 24th, 2002, based primarily on roof types (Figures 1A and Figure 2). We hypothesized that corrugated roofs could serve as a proxy for poor structural quality of housing because corrugated steel roof tops are a relatively inexpensive material commonly used for roofing among Brazilian slum households. In contrast, red tile roofs and white painted roofs are associated with higher income residential areas and commercial or apartment buildings, respectively. A ‘texture’ layer was also derived using a 3 × 3 pixel window high-pass filter on the red band of the Landsat image. Higher standard deviation of this texture layer was associated with increased edge between buildings and roads, a common feature of higher income areas. In contrast, slum areas were more spatially homogenous. We also mapped pavement, vegetation, water, sand, and exposed soil (Figure 2). We assessed the accuracy of the classification using a fuzzy accuracy assessment, which accounts for intra-pixel heterogeneity (see Methods). The weighted fuzzy accuracy of the classification was 79% with an un-weighted kappa of 0.68, 71% fuzzy weighted user’s accuracy and 86% weighted producer’s accuracy. The classification had a higher level of accuracy for non-urban than urban classes (Additional file 1). The pavement classification had the lowest accuracy of all classes. Importantly, corrugated roofs were rarely classified as either tile or complex roofs (0.012%); the majority of errors in the corrugated roof classification were due to areas that were too heterogeneous to assign to any particular class.


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)

Land cover classification. Landsat TM 2002 Land cover classification based on water, vegetation, sand, exposed soil, corrugated roofs, tile roofs, white-painted roofs, pavement, and cloud land covers.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Land cover classification. Landsat TM 2002 Land cover classification based on water, vegetation, sand, exposed soil, corrugated roofs, tile roofs, white-painted roofs, pavement, and cloud land covers.
Mentions: To capture the structural quality of housing listed in the UN-Habitat definition of a slum, we generated a land cover classification of a Landsat TM image acquired on February 24th, 2002, based primarily on roof types (Figures 1A and Figure 2). We hypothesized that corrugated roofs could serve as a proxy for poor structural quality of housing because corrugated steel roof tops are a relatively inexpensive material commonly used for roofing among Brazilian slum households. In contrast, red tile roofs and white painted roofs are associated with higher income residential areas and commercial or apartment buildings, respectively. A ‘texture’ layer was also derived using a 3 × 3 pixel window high-pass filter on the red band of the Landsat image. Higher standard deviation of this texture layer was associated with increased edge between buildings and roads, a common feature of higher income areas. In contrast, slum areas were more spatially homogenous. We also mapped pavement, vegetation, water, sand, and exposed soil (Figure 2). We assessed the accuracy of the classification using a fuzzy accuracy assessment, which accounts for intra-pixel heterogeneity (see Methods). The weighted fuzzy accuracy of the classification was 79% with an un-weighted kappa of 0.68, 71% fuzzy weighted user’s accuracy and 86% weighted producer’s accuracy. The classification had a higher level of accuracy for non-urban than urban classes (Additional file 1). The pavement classification had the lowest accuracy of all classes. Importantly, corrugated roofs were rarely classified as either tile or complex roofs (0.012%); the majority of errors in the corrugated roof classification were due to areas that were too heterogeneous to assign to any particular class.

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