Deprived Area (Slum) Mapping

A special issue of Urban Science (ISSN 2413-8851).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 12693

Special Issue Editors


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Guest Editor
Faculty of Geo-Information Science and Earth Observation, University of Twente, 5, 7522 NB Enschede, The Netherlands
Interests: data equity; urban data; deprived area mapping; slum mapping; earth observation; open data; data integration; gridded population data; gridded population sampling; household survey methods
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Guest Editor
St. Gallen Institute of Management in Latin America, University of St. Gallen, Sao Paulo 01310-920, Brazil
Interests: urban policies; affordable housing; slum upgrading; data needs for policy actions; impact of policy actions

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Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Interests: urban remote sensing; urban modelling; spatial statistics; urban planning; slum mapping; deprived area mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

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Keywords

  • slum
  • informal settlement
  • data ecosystem
  • data needs
  • spatial data
  • Earth observation
  • big data
  • data integration
  • Global South
  • LMIC
  • SDG 11
  • localization of SDGs

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Published Papers (3 papers)

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Research

20 pages, 1495 KiB  
Article
Cartographic Resources for Equitable University–Community Interaction in Slum Areas
by Marbrisa N. R. das Virgens, Patricia L. Brito, Ricardo Lustosa, Julio Pedrassoli, Philipp Ulbrich, João Porto de Albuquerque, Marcos Rodrigo Ferreira, Fernando G. Severo, Alessandra da S. Figueiredo, Marcel Fantin, Hussein Khalil and Federico Costa
Urban Sci. 2024, 8(1), 20; https://doi.org/10.3390/urbansci8010020 - 14 Mar 2024
Viewed by 2380
Abstract
Cartographic resources play a crucial role in facilitating communication across various sectors, including research projects focused on low-income communities. Despite this, some researchers still adhere to colonialist and exploitative approaches. This study aims to promote equitable university–community interaction though cartographic resources, aid academic [...] Read more.
Cartographic resources play a crucial role in facilitating communication across various sectors, including research projects focused on low-income communities. Despite this, some researchers still adhere to colonialist and exploitative approaches. This study aims to promote equitable university–community interaction though cartographic resources, aid academic and vulnerable community users in choosing a better platform for their work, and provide insights to developers for improving the platforms to better serve the user profiles of community members. To achieve this, we examined the use of cartographic resources in five projects within low-income communities (commonly referred to as favelas or so-called “slums”) in three Brazilian cities, all guided by equitable principles. The study unfolds in four stages: (i) data collection from documents and interviews; (ii) systematization into seven analytical categories—cartographic resources, data, personnel, processes, equipment, general objectives, and specific objectives; (iii) analysis of eight cartographic resources; and (iv) a critical examination of the outcomes. The synthesis of the collected information identified 65 characteristics/demands, with 17 common to all projects, including vector feature creation, thematic map design, printed map usage, and satellite imagery. We also identified 53 geographic information system (GIS) functionalities required for the projects, predominantly related to vector data generation and editing. The outcomes demonstrate the benefits of project methodologies, contributing to a decolonial university–community praxis. Additionally, they underscore the potential of digital cartographic resources, functioning not solely as data collection tools but also as powerful instruments that empower slum residents to advocate for improvements and foster local development. Full article
(This article belongs to the Special Issue Deprived Area (Slum) Mapping)
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23 pages, 18423 KiB  
Article
Mapping Deprived Urban Areas Using Open Geospatial Data and Machine Learning in Africa
by Maxwell Owusu, Ryan Engstrom, Dana Thomson, Monika Kuffer and Michael L. Mann
Urban Sci. 2023, 7(4), 116; https://doi.org/10.3390/urbansci7040116 - 8 Nov 2023
Cited by 4 | Viewed by 4602
Abstract
Reliable data on slums or deprived living conditions remain scarce in many low- and middle-income countries (LMICs). Global high-resolution maps of deprived areas are fundamental for both research- and evidence-based policies. Existing mapping methods are generally one-off studies that use proprietary commercial data [...] Read more.
Reliable data on slums or deprived living conditions remain scarce in many low- and middle-income countries (LMICs). Global high-resolution maps of deprived areas are fundamental for both research- and evidence-based policies. Existing mapping methods are generally one-off studies that use proprietary commercial data or other physical or socio-economic data that are limited geographically. Open geospatial data are increasingly available for large areas; however, their unstructured nature has hindered their use in extracting useful insights to inform decision making. In this study, we demonstrate an approach to map deprived areas within and across cities using open-source geospatial data. The study tests this methodology in three African cities—Accra (Ghana), Lagos (Nigeria), and Nairobi (Kenya) using a three arc second spatial resolution. Using three machine learning classifiers, (i) models were trained and tested on individual cities to assess the scalability for large area application, (ii) city-to-city comparisons were made to assess how the models performed in new locations, and (iii) a generalized model to assess our ability to map across cities with training samples from each city was designed. Our best models achieved over 80% accuracy in all cities. The study demonstrates an inexpensive, scalable, and transferable approach to map deprived areas that outperforms existing large area methods. Full article
(This article belongs to the Special Issue Deprived Area (Slum) Mapping)
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21 pages, 13502 KiB  
Article
Testing the Informal Development Stages Framework Globally: Exploring Self-Build Densification and Growth in Informal Settlements
by Jota Samper and Weichun Liao
Urban Sci. 2023, 7(2), 50; https://doi.org/10.3390/urbansci7020050 - 9 May 2023
Cited by 3 | Viewed by 3917
Abstract
This article challenges the narrow definition of informal settlements as solely lacking a formal framework, which overlooks the dynamic city-making and urban design processes within these areas. Communities’ self-building processes and areas’ constant growth are indeed informal settlements’ most salient morphological features. The [...] Read more.
This article challenges the narrow definition of informal settlements as solely lacking a formal framework, which overlooks the dynamic city-making and urban design processes within these areas. Communities’ self-building processes and areas’ constant growth are indeed informal settlements’ most salient morphological features. The study builds upon the informal development stages (IDS) framework and explores how it applies globally. The research follows a sample of fifty informal settlements with a high change coefficient from the Atlas of Informality (AoI) across five world regions to explore how change and urban densification across IDS can be mapped in such areas using human visual interpretation of Earth observation (EO). The research finds evidence of IDS framework fitment across regions, with critical morphological differences. Additionally, the study finds that settlements can pass through all IDS phases faster than anticipated. The study identifies IDS as a guiding principle for urban design, presenting opportunities for policy and action. The study suggests that integrating IDS with predictive morphological tools can create valuable data to refine identification models further. Finally, the article concludes that an IDS approach can anticipate development and integrate into an urban design evolutionary process that adapts to the deprived areas’ current and future needs. Full article
(This article belongs to the Special Issue Deprived Area (Slum) Mapping)
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