The Scope of Earth-Observation to Improve the Consistency of the SDG Slum Indicator

Abstract

The continuous increase in deprived living conditions in many cities of the Global South contradicts efforts to make cities inclusive, safe, resilient, and sustainable places. Using examples of Asian, African, and Latin American cities, this study shows the scope and limits of earth observation (EO)-based mapping of deprived living conditions in support of providing consistent global information for the SDG indicator 11.1.1 “proportion of urban population living in slums, informal settlements or inadequate housing”. At the technical level, we compare several EO-based methods and imagery for mapping deprived living conditions, discussing their ability to map such areas including differences in terms of accuracy and performance at the city scale. At the operational level, we compare available municipal maps showing identified deprived areas with the spatial extent of morphological mapped areas of deprived living conditions (using EO) at the city scale, discussing the reasons for inconsistencies between municipal and EO-based maps. We provide an outlook on how EO-based mapping of deprived living conditions could contribute to a global spatial information base to support targeting of deprived living conditions in support of the SDG Goal 11.1.1 indicator, when uncertainties and ethical considerations on data provision are well addressed.

Seeing Sustainability from Space: Using Earth Observation Data to Populate the UN Sustainable Development Goal Indicators

“…There have been efforts by the research community to use EO-derived data to populate this indicator. Notably, [42] provide an overview of suitable EO-based methods to monitor the dynamics of deprived living condition areas via proxies (roof type, building structure, proximities) and to estimate the population using EO-based maps combined with traditional survey data.…”

“…As a next step, the literature was searched for those peer-reviewed articles and/or reports which provide evidence that the EO could be used to populate the components of the indicator. In this case, the research article [42] was selected among several, as a principal reference for the MMS calculation due to its comprehensive approach. As a last step, we critically reviewed the approach against MMF 2.0, and a listing of metadata was extracted and is presented in Table 5.…”

“…Of special relevance here is the spatial and temporal resolution used, the methods of processing EO data and validation and scalability. To allocate an MMS for this indicator, a score from 1 to 5 was assigned for each premise (see Table 6) based upon the judgement of the authors and the information found in [42].…”

“…Furthermore, the visual image interpretation of slums by local experts could bring disputes, because of the subjectivity in delineation of deprived vs. non-deprived status, thus affecting the mapping results. • Premise F (Cost-effectiveness): A score of 4 was given based on the assumption, from the discussion of satellite data prices in [42], that mapping deprived areas at regional and country scale would require the combination of VHR satellite data (e.g., SPOT 5-lower cost) with high resolution free data (e.g., from Sentinel 2) to provide greater cost-efficiency compared with traditional non-EO alternatives. Although the improved cost efficiency depends on the scale of mapping and the satellite company prices.…”

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

“…While the body of EO literature about deprived area mapping is rapidly increasing (e.g., [50,51,[53][54][55][56][57][58][59][60][61][62][63][64][65]), several challenges in this area still exist. For example, most studies are not addressing global information needs (e.g., producing data in support of SDG 11 [66]), with most deprived area mapping approaches mainly focusing on small areas below the city scale and for very specific sites. Further, very few approaches have been used to examine the temporal dynamics of slums to understand how conditions between and among them change over time (e.g., due to changes with policy [7]).…”