“Domains of deprivation framework” for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review

Abstract

The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, policy efforts and the monitoring of global goals and agendas such as the United Nation's Sustainable Development Goals (SDGs) and UN-Habitat New Urban Agenda are hindered by the unavailability of statistical and spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, and presently, there is a strong focus within the existing literature on household-level (including individual) deprivation and less on area-level deprivation and this is problematic because deprivation at the area and household-level are known to interrelate and result in multiple challenges for individuals and communities. Within this scoping review, we build on existing literature that focuses on household- or area-level deprivation to arrive at a combined understanding of how urban deprivation is defined in relation to LMIC cities. The scoping review of existing literature was used in conjunction with local stakeholder workshops to produce a framework titled “Domains of Deprivation Framework”. The Domains of Deprivation Framework conceptualizes urban deprivation at three different scales, including at the household scale, within the area scale and at the area connect scale. It includes nine domains, (1) Socio-Economic Status and (2) Housing Domains (Household scale); (3) Social Hazards & Assets, (4) Physical Hazards & Assets, (5) Unplanned Urbanization and (6) Contamination (Within Area scale); and (7) Infrastructure, (8) Facilities & Services and (9) City Governance (Area Connect scale). The Domains of Deprivation Framework is designed to support diverse urban, health, poverty, and development initiatives globally to characterize and address deprivation in LMIC cities from a holistic perspective, combining traditional data sources (e.g., surveys or census data) with new data sources (e.g., Earth Observation data).