Health, Poverty and Place: Modeling Inequalities in Accra Using RS and GIS

描述（由申请人提供）：该项目通过对加纳阿克拉健康方面的空间差异进行详细分析，旨在为解释贫穷国家城市中的城市健康不平等提供一个模型。利用加纳的阿克拉，我们将产生一些方法，我们希望这些方法将成为对发展中国家其他主要城市进行比较分析的模板。该项目将以政府和负责改善总体健康状况和减少健康不平等现象的发展机构可以立即获得的格式，编制城市地区健康和死亡率差异的地理概况;绘制贫困分布图（收入和财富），并评估其与选定的健康和死亡率结果的空间关联;利用遥感和地理信息系统技术，衡量不利的健康和死亡结果与邻近生态的关系，包括与从多谱段高空间分辨率卫星图像可见的物理特征和建筑结构观测中得出的空间计量的关系;并评估社区一级的变量，包括社会组织和机构，对产生健康和死亡率方面的不平等的额外贡献，扣除个人一级的特点。我们的研究包括以下主要步骤：（1）应用新的方法来定义城市街区的空间和时间使用人口普查和调查数据和信息从遥感图像;（2）验证数据派生的街区定义，属性和边界与现场评估的目的性样本的阿克拉街区;（3）基于导出的邻域定义以及现有的和更新的图像和地理层，在邻域水平上集合现有的基于普查的、基于调查的和基于生命统计学的发病率和死亡率度量;（4）重新访谈（2008年）2003年阿克拉妇女健康调查的受访者，以产生对社区一级健康结果和这些结果变化的新衡量标准;（5）采用空间分析、多变量和多层次分析来预测阿克拉社区之间和社区内部健康结果的不平等;（6）开发基于WHS和DHS数据并结合图像的模型，以预测社区的健康结果;（7）将建模算法应用于两个关键资源-人口与健康调查和遥感图像-以预测变异性（8）将阿克拉当地的结果用于卫生政策和规划目的，并使用数据相对丰富的阿克拉的结果来开发模型，随后可用于估计撒哈拉以南非洲数据较少的城市的健康空间变异性。

DESCRIPTION (provided by applicant): This project, through a detailed analysis of spatial disparities in health in Accra, Ghana, aims to provide a model for the interpretation of urban health inequalities in cities in poor countries. Using Accra in Ghana, we will generate methods that we hope will serve as templates for comparative analyses of other major cities in the developing world. The project will produce a geographical synopsis of the differentials in health and mortality within the urban area in a format immediately accessible to government and the development agencies tasked with the improvement of health overall and with the reduction of health inequalities; map the distribution of poverty (both income and wealth) in the city and assess its spatial association with selected health and mortality outcomes; using remote sensing and GIS technology, measure the association of adverse health and mortality outcomes with neighborhood ecology, including the association with spatial measures derived from observations of physical features and built structures visible from multi-spectral high spatial resolution satellite imagery; and assess the additional contribution of community-level variables including social organization and institutions to the production of inequalities in health and mortality, net of individual-level characteristics. Our research involves the following major steps: (i) apply new methods of defining urban neighborhoods across space and time using census and survey data and information from remotely sensed imagery; (2) validate data-derived neighborhood definitions, attributes, and boundaries with field assessments in a purposive sample of Accra neighborhoods; (3) assemble existing census-based, survey-based, and vital statistics-based measures of morbidity and mortality at the neighborhood level, based on the derived neighborhood definitions, as well as existing and updated imagery and geographic layers; (4) re-interview (in 2008) the respondents to the 2003 Accra Women's Health Survey to generate new measures of neighborhood-level health outcomes and changes in those outcomes; (5) employ spatial analytic, multivariate and multi-level analyses to predict the inequalities in health outcomes between and within neighborhoods of Accra; (6) develop models that build on the WHS and DHS data in combination with the imagery to predict health outcomes by neighborhood; (7) apply the modeling algorithms to two key resources-the Demographic and Health Survey and remotely sensed imagery-to predict the variability (inequality) in neighborhood level health outcomes in Accra; and (8) employ the results locally in Accra for health policy and planning purposes, and use the results from relatively data-rich Accra to develop models that can subsequently be used to estimate the spatial variability in health in data-poorer cities in sub-Saharan Africa.