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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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Mapping in geographic and canonical space. A) Map of deprivation in Salvador at 30 m × 30 m resolution using combined canonical dimensions 1 and 2. Green areas are at a low risk of slum characteristics, while red indicates a high risk for slum characteristics. The final map was clipped to the impervious surfaces. The geographic location of selected communities representing a range of land cover and socioeconomic characteristics is also represented. B) Location of the same communities in canonical space. Canonical scores for dimensions 1 and 2 are shown for all census tracts; the colors correspond to the mean combined dimension 1 and 2 score for each census tract (as represented in A).
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Figure 5: Mapping in geographic and canonical space. A) Map of deprivation in Salvador at 30 m × 30 m resolution using combined canonical dimensions 1 and 2. Green areas are at a low risk of slum characteristics, while red indicates a high risk for slum characteristics. The final map was clipped to the impervious surfaces. The geographic location of selected communities representing a range of land cover and socioeconomic characteristics is also represented. B) Location of the same communities in canonical space. Canonical scores for dimensions 1 and 2 are shown for all census tracts; the colors correspond to the mean combined dimension 1 and 2 score for each census tract (as represented in A).

Mentions: We produced the final deprivation map by summing the values of both dimensions for each pixel (Figure 5A), thus incorporating attributes identified in both the first and second dimensions. Additionally, we generated a visual representation of Salvador urban structure in canonical space by plotting the mean pixel value for each census tract derived from the final map (Figure 5B). Canonical scores for all census tracts represented a continuum, with the least developed and lower income areas loaded most positively (red) and more affluent areas were loaded negatively (green) in both dimensions. To assess the position of slum communities in canonical space, we selected three communities that represented different levels of urban slum development and age, as well as a higher-income community for comparison (Table 6, Figure 5). The slum communities loaded highly in dimension one, while Barra, the high-income community, loaded low. Among the three slum communities, the newest area with the poorest infrastructure, Barrio da Paz (settled in 1982) loaded highest in dimension 2, followed by Pau da Lima (settled in 1950) and Nordeste Amaralina, the oldest community, first inhabited in 1629 [20].


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)

Mapping in geographic and canonical space. A) Map of deprivation in Salvador at 30 m × 30 m resolution using combined canonical dimensions 1 and 2. Green areas are at a low risk of slum characteristics, while red indicates a high risk for slum characteristics. The final map was clipped to the impervious surfaces. The geographic location of selected communities representing a range of land cover and socioeconomic characteristics is also represented. B) Location of the same communities in canonical space. Canonical scores for dimensions 1 and 2 are shown for all census tracts; the colors correspond to the mean combined dimension 1 and 2 score for each census tract (as represented in A).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Mapping in geographic and canonical space. A) Map of deprivation in Salvador at 30 m × 30 m resolution using combined canonical dimensions 1 and 2. Green areas are at a low risk of slum characteristics, while red indicates a high risk for slum characteristics. The final map was clipped to the impervious surfaces. The geographic location of selected communities representing a range of land cover and socioeconomic characteristics is also represented. B) Location of the same communities in canonical space. Canonical scores for dimensions 1 and 2 are shown for all census tracts; the colors correspond to the mean combined dimension 1 and 2 score for each census tract (as represented in A).
Mentions: We produced the final deprivation map by summing the values of both dimensions for each pixel (Figure 5A), thus incorporating attributes identified in both the first and second dimensions. Additionally, we generated a visual representation of Salvador urban structure in canonical space by plotting the mean pixel value for each census tract derived from the final map (Figure 5B). Canonical scores for all census tracts represented a continuum, with the least developed and lower income areas loaded most positively (red) and more affluent areas were loaded negatively (green) in both dimensions. To assess the position of slum communities in canonical space, we selected three communities that represented different levels of urban slum development and age, as well as a higher-income community for comparison (Table 6, Figure 5). The slum communities loaded highly in dimension one, while Barra, the high-income community, loaded low. Among the three slum communities, the newest area with the poorest infrastructure, Barrio da Paz (settled in 1982) loaded highest in dimension 2, followed by Pau da Lima (settled in 1950) and Nordeste Amaralina, the oldest community, first inhabited in 1629 [20].

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