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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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Socioeconomic and land cover variables in the first two canonical dimensions. Canonical loadings for each original variable in the first two canonical dimensions. Variables definitions are listed in Table 2.
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Figure 3: Socioeconomic and land cover variables in the first two canonical dimensions. Canonical loadings for each original variable in the first two canonical dimensions. Variables definitions are listed in Table 2.

Mentions: The first dimension of the canonical correlation captured the strongest association between the socioeconomic and land cover variables. The ordination of census tracts along the first canonical dimension captured a gradient from slum areas (positive loadings) to higher income areas (negative loadings) (Figure 3). This gradient can be inferred from the canonical loadings associated with this dimension: positive for crowding and corrugated roofs, negative for pavement, white-painted roofs and income (Figure 3, Table 5). Based on the canonical coefficients of the land cover variables, corrugated roofs and pavement variables most strongly influenced the first dimension (Table 4). Of the socioeconomic variables, income and bathroom variables had the most influence in the first dimension (Table 4). Based on the canonical loadings, the second canonical dimension was mostly driven by socioeconomic variables such as bathroom, garbage and crowding (positive) and income and water (negative) (Figure 3, Table 5). The majority of households in Salvador have access to sanitation and piped water, which may account for why these variables explain less of the overall variance. Based on this lack of infrastructure, communities that are highly loaded in this dimension are likely newly-squatted areas. The land cover variables did not weigh strongly in this dimension, however dimension 2 still identifies important deprivation characteristics (Table 5). The third dimension explained only a 0.11 of the variance and differentiated high-income areas with high proportion of white-painted (typical commercial areas in Salvador) or tiled roofs from lower-income areas.


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)

Socioeconomic and land cover variables in the first two canonical dimensions. Canonical loadings for each original variable in the first two canonical dimensions. Variables definitions are listed in Table 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Socioeconomic and land cover variables in the first two canonical dimensions. Canonical loadings for each original variable in the first two canonical dimensions. Variables definitions are listed in Table 2.
Mentions: The first dimension of the canonical correlation captured the strongest association between the socioeconomic and land cover variables. The ordination of census tracts along the first canonical dimension captured a gradient from slum areas (positive loadings) to higher income areas (negative loadings) (Figure 3). This gradient can be inferred from the canonical loadings associated with this dimension: positive for crowding and corrugated roofs, negative for pavement, white-painted roofs and income (Figure 3, Table 5). Based on the canonical coefficients of the land cover variables, corrugated roofs and pavement variables most strongly influenced the first dimension (Table 4). Of the socioeconomic variables, income and bathroom variables had the most influence in the first dimension (Table 4). Based on the canonical loadings, the second canonical dimension was mostly driven by socioeconomic variables such as bathroom, garbage and crowding (positive) and income and water (negative) (Figure 3, Table 5). The majority of households in Salvador have access to sanitation and piped water, which may account for why these variables explain less of the overall variance. Based on this lack of infrastructure, communities that are highly loaded in this dimension are likely newly-squatted areas. The land cover variables did not weigh strongly in this dimension, however dimension 2 still identifies important deprivation characteristics (Table 5). The third dimension explained only a 0.11 of the variance and differentiated high-income areas with high proportion of white-painted (typical commercial areas in Salvador) or tiled roofs from lower-income areas.

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