Development after Displacement: Evaluating the Utility of OpenStreetMap Data for Monitoring Sustainable Development Goal Progress in Refugee Settlements

Abstract

In 2015, 193 countries declared their commitment to “leave no one behind” in pursuit of 17 Sustainable Development Goals (SDGs). However, the world’s refugees have been routinely excluded from national censuses and representative surveys, and, as a result, have broadly been overlooked in SDG evaluations. In this study, we examine the potential of OpenStreetMap (OSM) data for monitoring SDG progress in refugee settlements. We collected all available OSM data in 28 refugee and 26 nearby non-refugee settlements in the major refugee-hosting country of Uganda. We created a novel SDG-OSM data model, measured the spatial and temporal coverages of SDG-relevant OSM data across refugee settlements, and compared these results to non-refugee settlements. We found 11 different SDGs represented across 92% (21,950) of OSM data in refugee settlements, compared to 78% (1919 nodes) in non-refugee settlements. However, most data were created three years after refugee arrival, and 81% of OSM data in refugee settlements were never edited, both of which limit the potential for long-term monitoring of SDG progress. In light of our findings, we offer suggestions for improving OSM-driven SDG monitoring in refugee settlements that have relevance for development and humanitarian practitioners and research communities alike.

Investigating the digital divide in OpenStreetMap: spatio-temporal analysis of inequalities in global urban building completeness

“…It has been shown that open data communities -such as OpenStreetMap (OSM) -are not only promising, but already contribute to filling existing data gaps 8,10,11 . OSM is now used widely for applications such as web maps and navigation services at and data about buildings from OSM has been used in domains such as urban planning 12 , SDG monitoring 13 , disaster management 11,14 , public health [15][16][17] , as well as during the COVID-19 pandemic 18 . However, particular attention needs to be paid to data quality, when OSM data is utilized in global studies or to derive global data products, e.g.…”

Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda

“…The average stay in a UNHCR-managed refugee settlement was 10 years as of 2015 [28], though many settlements become intergenerational homes through a "protracted refugee scenario", in which refugee populations greater than twenty-five thousand people are displaced for more than five years [29]. The location, setting and timing of refugee settlements are broadly documented with openly accessible geospatial data on building footprints, settlement boundaries, as well as ancillary information on settlement population, refugee arrival and duration of habitation, much of which has been collected in support of rapid or prolonged humanitarian support [30].…”

Mapping of Dwellings in IDP/Refugee Settlements from Very High-Resolution Satellite Imagery Using a Mask Region-Based Convolutional Neural Network

“…According to an in-depth analysis by the authors of [33], the global datasets on built-up areas are less representative for IDP/refugee settlements, which are characterized by a low probability of detection, the underestimation of the built-up extent, and the complete overlooking of a few refugee settlements. A comprehensive assessment on the relevance of Open Street Maps (OSM) for monitoring the sustainable development goals in refugee settlements, by the authors of [34], also concludes that although OSM has relevant thematic layers for the intended goal, the information is mapped long after refugee settlements form, and it is not frequently updated. From the qualitative inspection made on the test site during the writing phase of this report, the underrepresentation of dwelling structures in IDP/refugee camps is also common in well-known generic open datasets, such as OpenStreetMap (OSM), Bing Maps, and Google Maps (Appendix A Figures A1 and A2 for details in specific study sites), and specific thematic layers hosted by entities working on IDP/refugee settlements [35].…”