Deprived Area (Slum) Mapping

Dear Colleagues,

We are inviting submissions on the topic of “Deprived Area (Slum) Mapping,” which aims to gather innovative datasets, methods, and approaches to generating citywide data (e.g., maps, statistics) of deprivation. We especially invite submissions that describe and evaluate approaches that integrate two or more of the following “slum” mapping traditions: (1) field-based mapping by residents; (2) aggregation of “slum household” data; (3) human visual interpretation of Earth observation (EO) imagery (e.g., satellites); and (4) semi-automatic classification of EO imagery or geospatial models with machine algorithms. These traditions have operated, largely, in silos for the last two decades, and have yet to regularly produce accurate maps of deprived areas that are fit-for-use by multiple stakeholders. Residents of deprived areas require data from their neighborhood(s) to plan and advocate for upgrading. Local governments and their partners use citywide deprived area maps to prioritize areas of the city and types of investments for urban development. National and global actors use deprived area maps from multiple cities to set policies (e.g., New Urban Agenda) and monitor development indicators (e.g., SDG 11). Despite a plethora of EOs, community-generated data, official data, and new sources of Big Data being available for a decade or more, and despite major advancements in computing power and data algorithms, stakeholders, particularly in low- and middle-income countries (LMICs), are still in want of current, accurate, inclusive deprived area data.

The production of such data is a wicked socio-technical challenge. Choices about how deprived areas are depicted as spatial data, and the processes used to generate these data, can lead to wildly different outcomes. In the best-case scenario, the production of deprived area maps is inclusive of diverse stakeholders and results in participatory “slum” upgrading with the integration of marginalized communities into the urban fabric enabling all urban residents to prosper. Though, too often, the process of generating deprived area maps further excludes already marginalized communities and even contributes to harmful stereotypes, harassment, fines, and evictions. As the world grapples with the economic and social consequences of the global COVID-19 pandemic, multiple food crises, increased frequency and severity of climate-related events, and the fallout of conflicts, including in Afghanistan and Ukraine, the need for data about deprived urban areas and their residents is as important as ever. We encourage researchers and practitioners to submit original research and review articles, as well as case studies and critical perspectives on topics, including, but not limited to:

• Integration of data and methods for deprived area mapping;
• Geo-ethics of mapping “slums” with considerations of spatial data format, scale, and access;
• Ethical codes for machine learning and artificial intelligence modelling of urban deprivation;
• Community participation in data collection and/or generation;
• Fair exchange of data (or comparable, e.g., training) among diverse stakeholders (e.g., citizen scientists, local governments, NGOs, academia, international agencies);
• Co-design of data/modelling ecosystems about urban deprivation;
• Localization of SDGs and other urban planning and development indicator initiatives;
• Inclusive urban planning and upgrading processes;
• Definitions and measurements of urban deprivation;
• New data sources and models (e.g., digital twins) and their integration with community-based data;
• Geospatial data models for characterizing deprivation, including environmental, hazard, socio-economic, health, and demographic conditions;
• Applications and impacts of deprived area data being used (in)effectively for urban planning, upgrading, and human rights.

Dr. Dana R. Thomson
Dr. Anthony Boanada-Fuchs
Dr. Monika Kuffer
Guest Editors

Manuscript Submission Information

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