Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil.
Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil.
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DOI：
10.1186/1476-072x-12-45
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发表时间：
2013-10-20
影响因子：
4.9
通讯作者：
Diuk-Wasser MA
中科院分区：
文献类型：
--
作者：
Hacker KP;Seto KC;Costa F;Corburn J;Reis MG;Ko AI;Diuk-Wasser MA
城市贫民窟的扩大是21世纪公共和社会政策面临的一项重大挑战。贫民窟社区的异质性和动态性限制了严格的贫民窟定义的使用。采用系统和灵活的方法，以业务解决方案来描述、描绘和模拟城市贫民窟的结构，对于在地方和国家一级规划、部署和监测干预措施至关重要。我们通过整合人口普查产生的社会经济变量和遥感土地覆盖变量，对巴西萨尔瓦多市300万居民的城市贫民窟的多维结构进行了建模。我们使用典型相关分析评估了两组变量之间的相关性，确定了社会经济变量的土地覆盖代理，并以30米× 30米的分辨率制作了萨尔瓦多的贫困综合地图。规范分析确定了三个重要的协调轴，根据土地覆盖和社会经济特征描述了萨尔瓦多人口普查区的结构。第一个规范轴捕获了从拥挤的低收入社区到高收入社区的梯度。第二个标准轴区分了最边缘化的人口普查区的社会经济变量，这些地区没有卫生设施或自来水。第三条规范轴的变化最小，但区分了高收入地区和低收入地区的白色屋顶或瓦片屋顶。我们的方法抓住了贫民窟内部和之间的社会经济和土地覆盖异质性，并确定了大型复杂城市环境中最边缘化的社区。这些发现表明，贫民窟地区标准得分的变化可用于跟踪其演变，并监测贫民窟改造等发展计划的影响。
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. 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. 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. 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.