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

Year: 2021
Type: article
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.
DOI: 10.3390/ijgi10030153
Language: English
Open Access status: gold
Estimated APC paid: $1,512
Award: WE PROPOSE A THREE YEAR PROGRAM OF APPLIED RESEARCH TO DEVELOP OPEN GLOBAL-SCALE GEOSPATIAL DATA TO LOCATE CHARACTERIZE AND ASSESS SUSTAINABLE DEVELOPMENT GOAL (SDG) INDICATORS AT INFORMAL SETTLEMENTS BELONGING TO REFUGEES INTERNALLY DISPLACED POPULATIONS AND FORCIBLY DISPLACED AND RESETTLED PEOPLE: THE MISSING MILLIONS. OUR PRIMARY GOALS ARE TO 1) HARNESS THE POWER OF CROWDSOURCED DATA COLLECTION TO ASSESS LOCATIONAL AND THEMATIC ACCURACIES OF THE GLOBAL HUMAN SETTLEMENT LAYER (GHSL) 2) DEPLOY MACHINE LEARNING TO DETECT AND CHARACTERIZE SPATIAL TYPOLOGIES AND OTHER CHARACTERISTICS (SPECTRAL CONTEXTUAL TEMPORAL) OF INFORMAL SETTLEMENTS PRESENT IN OR ABSENT FROM GHSL 3) UTILIZE REMOTELY SENSED SDG INDICATOR PROXIES TO MONITOR SDGS AT INFORMAL SETTLEMENTS AND COMPARE SDG PROFILES OF MISSING MILLIONS SETTLEMENTS AT THE GLOBAL SCALE 4) CONDUCT MULTI-SITE FIELD ASSESSMENTS IN TWO NATIONAL CONTEXTS (NEPAL AND UGANDA) TO VALIDATE OUTPUTS: SDG ASSESSMENT AND THE SETTLEMENT SCHEMA. THE PROPOSED EFFORT WILL CULMINATE IN THE CREATION AND DISSEMINATION OF A NOVEL OPEN GLOBAL-SCALE VECTOR DATASET REPRESENTING MISSING MILLIONS INFORMAL SETTLEMENTS ATTRIBUTED WITH INFORMATION ON SCHEMA SPATIAL TYPOLOGY AND ENVIRO-CLIMATIC CONDITIONS TO SUPPORT SUB-NATIONAL SDGS ASSESSMENT FOR THE WORLD S MOST VULNERABLE.
OpenAlex ID: W3133545925
FWCI: 13.79
Cites: 65
Cited by: 25
Related to: 10