Need for an Integrated Deprived Area "Slum" Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)
Need for an Integrated Deprived Area "Slum" Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)
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DOI：
10.3390/socsci9050080
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发表时间：
2020-05-01
影响因子：
1.7
通讯作者：
Kabaria, Caroline
中科院分区：
文献类型：
--
作者：
Thomson, Dana R.;Kuffer, Monika;Kabaria, Caroline
在未来30年内，地球上90%的新增人口将生活在非洲和亚洲的城市，其中很大一部分人口将居住在贫民窟、非正式住区或住房不足的贫困社区。目前的四种邻里贫困地图绘制方法在很大程度上是孤立的，每一种都不能产生准确、及时和可比较的地图，反映当地的情况。第一种方法是在人口普查和调查数据中对“贫民窟家庭”进行分类，反映的是家庭一级而不是社区一级的贫困状况。第二种方法，基于字段的映射，可以为给定的邻域生成最准确和与上下文相关的地图，但它需要大量的资源，阻止了放大。第三种和第四种方法，即对航空或航天图像的人工（视觉）判读和机器分类，都过分强调非正规住区，未能反映贫困地区的关键社会特征，如缺乏土地保有权、暴露于污染和缺乏公共服务。我们总结了共同的理解领域，并提出了一套要求和框架，以制作贫困城市地区的常规，准确的地图，可供地方到国际利益相关者用于宣传，规划和决策在低收入和中等收入国家（LMICs）。我们建议扩展机器学习模型，以纳入社会区域层面的协变量和定期贡献的最新和上下文相关的基于领域的分类贫困的城市地区。
Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying "slum households" in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low- and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas.