Leveraging Digital Technologies to Solve the Last-Mile-Problems for Localizing Sdg 11 Indicators on Housing and Informal Settlements

Urbanization rates in Low- and Middle-Income Country (LMIC) cities show unprecedented growth. In the context of the current housing crisis, with around 1.1 billion people living in informal settlements or slums, and an estimated increase of 2 billion, urgent action is required. This urgency is stressed by SDG 11, and the prioritization of housing during the UN-Habitat Assembly (2023). Action on housing and, in particular, the SDG 11.1 target of “adequate, safe and affordable housing and basic services and upgrade slums” require efficient monitoring instruments that include the mapping of spatial inequalities.
National to city-level statistics are often lacking behind the rapid development dynamics in terms of spatial and temporal granularities. Official data are often unable to provide quality data that are openly available on spatial inequalities (e.g., environmental and housing conditions). For example, the average LMIC census omits half of residents in slums and informal settlements, though this varies widely, with some censuses omitting nearly all slum-dwellers. The session will build on IDEAMAPS, IDEAtlas and UNITAC projects, in which informal settlements are being mapped in more than a dozen metropolitan areas around the world. Emerging and alternative data sources (e.g., Geospatial, Earth Observation and Citizen Generated data), combined with official statistics, offer invaluable opportunities for measuring the problem and guiding actions. By its very nature, mapping settlements implies measuring SDGs at the local (city) level. With increased access to computing power and the wide availability of open, fine-scale spatial data from satellite imagery (e.g., building footprints), crowd-sourcing (e.g., OpenStreetMap), and from governments and communities, the time is ripe for adding measures of spatial inequalities to official statistics. However, besides the development of technical approaches, questions of ethics (e.g., the ‘do no harm’ principle) and building trust with stakeholders are critical.
In this session, panellists will share their perspectives about the importance and innovations of measures of spatial inequalities with a focus on housing and environmental deprivation (with links to climate adaptation), and how maps can shape the policy discourse on improving housing. We will share recent experiences in several cities (e.g., Buenos Aires, Jakarta, Kano, Lagos, Medellin, Mexico City, Mumbai, Nairobi, eThekwini Municipality and Guatemala City, among others). The combination of experts on housing policies, national statistical experts, city governments, data scientists, and NGOs will reflect the opportunities as well as the challenges of combining various data sources (e.g., linking to data quality and trust building) into Open and Inclusive Data Ecosystems that capture housing combined with environmental and social conditions at high spatial and temporal granularities and have the potential or are integrated within official data.