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Integrated Deprived Area Mapping System (IDEAMAPS) Network

综合贫困地区测绘系统 (IDEAMAPS) 网络

基本信息

批准号：
EP/T029900/1
负责人：
Caroline Kabaria
金额：
$ 18.42万
依托单位：
African Population and Health Research Center
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FT029900%2F1
关键词：
Integrated Deprived Area Mapping System

项目摘要

Introduction:The UN estimates that 2.5 billion people will be added to the planet over the next 30 years. A large portion of these populations will reside in deprived neighbourhoods including slums, informal settlements, and areas of inadequate housing and face a range of challenges from insecure tenure, to unplanned housing, pollution, environmental risk, and social exclusion. Spatial data on such neighbourhoods are commonly not available. On the occasions that they do exist, they quickly become out-dated. Without up-to-date information on the geography (location and extent) of deprived neighbourhoods and the specific social and physical environmental conditions faced by their inhabitants, the impact of these on health and social outcomes are not traceable and the development of effective interventions is not achievable. However, there is currently no systematic, scalable approach to map deprived neighbourhoods across cities, particularly in low- and middle-income countries (LMICs). Methodology:The project will design an Integrated Deprived Area Mapping System (IDEAMAPS) that will push the boundaries and overcome the weaknesses of each of these current approaches to mapping slums, informal settlements, and areas of inadequate housing. This will be accomplished by joining up a multisectoral and transdisciplinary network of researchers, technologists and societal stakeholders who will collaborate to co-design the IDEAMAPS approach to map and address neighbourhood deprivation and perform pilot studies in Nairobi, Accra, and Lagos, where we have existing strong partnerships and access to existing data (see details below). This approach will consist of an innovative combination of three components: (a) engagement of stakeholders in different scales; (b) modelling and data infrastructure techniques that aim to integrate and leverage the accuracy of field-based maps; (c) an integrative framework, which defines assessment criteria for characterising neighbourhood deprivation in connection with sustainable development. Work Groups:WP1. Stakeholder Engagement- This work package (WP) leads engagement of stakeholders across sectors (e.g. EO, demography, community, policy) in both established and innovative ways to further develop understanding of urban neighbourhood deprivation, and to create enabling environments in which a deprived area mapping system can be implemented.WP2. Integrative Frameworks- This will be dedicated to the synthesis of key requirements and a shared agenda for the IDEAMAPS approach. It will begin with collating existing literature reviews from current and past projects of the research team: specifically, the Improving Health in Slums Collaborative, Surveys for Urban Equity, Million Neighborhoods Initiative, Modelling African Urban Population Patterns, Accra women's health study, and Standardizing City-Level Data-Gathering in Lagos and Accra. WP3. Modelling and Data Infrastructure Techniques. This WP3 collate existing data and methods to test potential IDEAMAPS approaches. Based on the domains of deprivation framework developed in WP 2 with input from stakeholders of WP1, we will develop new area-level environmental (e.g. flood risk) and social datasets (e.g. open sewers), and evaluate modelling approaches that can capture domains of deprivation individually, as well as an overall deprivation "slumness" index. WP2 will also investigate different visualisation targeted at end-users in different scales. The evaluation criteria developed in WP 2 will be used to assess and compare model outputs.Expected Output:The work packages will create a foundation of stakeholder engagement approaches, frameworks, and techniques to scale-up over the next three years to flesh out an integrated deprivation area mapping system (IDEAMAPS) with a capacity to generate more accurate and usable maps and integrate them into community upgrading and advancing progress toward sustainable development goal.

导读：据联合国估计，未来30年地球人口将增加25亿。这些人口中的很大一部分将居住在贫困社区，包括贫民窟、非正式定居点和住房不足地区，并面临着一系列挑战，从不安全的租住权到计划外的住房、污染、环境风险和社会排斥。这些社区的空间数据通常是无法获得的。即使它们确实存在，它们也会很快过时。如果没有关于贫困社区的地理（位置和范围）及其居民所面临的具体社会和自然环境条件的最新信息，就无法追踪这些对健康和社会结果的影响，也无法制定有效的干预措施。然而，目前还没有系统的、可扩展的方法来绘制各个城市的贫困社区地图，特别是在低收入和中等收入国家。方法：该项目将设计一个综合贫困地区测绘系统（IDEAMAPS），该系统将突破现有方法的界限，克服目前每种方法在绘制贫民窟、非正规住区和住房不足地区地图方面的弱点。这将通过加入一个由研究人员、技术人员和社会利益攸关方组成的多部门和跨学科网络来实现，他们将合作共同设计IDEAMAPS方法，以绘制和解决社区贫困问题，并在内罗毕、阿克拉和拉各斯开展试点研究，我们在这些地方建立了牢固的伙伴关系，并可获得现有数据（详见下文）。这种方法将包括三个组成部分的创新组合：(a)不同规模的利益相关者的参与；(b)旨在综合和利用实地地图准确性的建模和数据基础结构技术；(c)综合架构，界定界定与可持续发展有关的邻里贫穷特征的评估标准。工作组:WP1。利益相关者参与-本工作包（WP）引导各部门利益相关者（如行政、人口、社区、政策）以既定和创新的方式参与，以进一步加深对城市邻里贫困的理解，并创造有利的环境，使贫困地区测绘系统得以实施。综合框架-这将专门用于综合关键要求和IDEAMAPS方法的共同议程。首先将整理研究小组当前和过去项目的现有文献综述：具体而言，改善贫民窟健康合作项目、城市公平调查、百万社区倡议、非洲城市人口模式建模、阿克拉妇女健康研究以及拉各斯和阿克拉城市级数据收集标准化。WP3。建模和数据基础设施技术。WP3整理了现有的数据和方法，以测试潜在的IDEAMAPS方法。基于《工作计划2》中开发的贫困领域框架，我们将开发新的区域级环境（如洪水风险）和社会数据集（如露天下水道），并评估能够单独捕获贫困领域的建模方法，以及总体贫困“贫民窟”指数。WP2还将研究针对不同规模的最终用户的不同可视化。WP 2中制定的评价标准将用于评估和比较模型产出。预期产出：这些工作包将为利益攸关方参与的方法、框架和技术奠定基础，以便在未来三年内扩大规模，充实贫困地区综合制图系统（IDEAMAPS），使其能够生成更准确、更可用的地图，并将其纳入社区升级和推进可持续发展目标的进程。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

"Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review

用于绘制中低收入国家贫民窟、非正规住区和其他贫困地区地图以改善城市规划和政策的“贫困领域框架”：范围界定审查

DOI：
10.20944/preprints202102.0242.v2
发表时间：
2021
期刊：
影响因子：
0
作者：
Abascal Á
通讯作者：
Abascal Á

Earth Observations and Statistics: Unlocking Sociodemographic Knowledge through the Power of Satellite Images

DOI：
10.3390/su132212640
发表时间：
2021-11-01
期刊：
SUSTAINABILITY
影响因子：
3.9
作者：
Merodio Gomez, Paloma;Juarez Carrillo, Olivia Jimena;Lustosa Brito, Patricia
通讯作者：
Lustosa Brito, Patricia

Identifying degrees of deprivation from space using deep learning and morphological spatial analysis of deprived urban areas