Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings

Lloyd, Christopher T.; Sturrock, Hugh J. W.; Leasure, Douglas R.; Jochem, Warren C.; Lázár, Attila N.; Tatem, Andrew J.

doi:10.3390/rs12233847

Cited by 25 publications

(23 citation statements)

References 29 publications

(35 reference statements)

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“…Since OSM data in Uganda generally do not include boundaries of rural settlements in refugee-hosting regions, we used the Africa-wide GRID3 (Geo-Referenced Infrastructure and Demographic Data for Development) dataset [54][55][56]. GRID3 is a human-validated, open-source geospatial dataset in which settlement boundaries are generated by clustering individual buildings detected in very high-resolution Maxar satellite imagery (Figure 5) [57].…”

Section: Grid3 Non-refugee Settlement Boundary Datamentioning

confidence: 99%

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

Hoek

Friedrich

Ballasiotes

et al. 2021

IJGI

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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.

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Section: Grid3 Non-refugee Settlement Boundary Datamentioning

confidence: 99%

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

Hoek

Friedrich

Ballasiotes

et al. 2021

IJGI

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“…New datasets derived from very high resolution satellite imagery, in particular building footprints, are a promising new covariate to reduce the "halo" effect of populations misallocated nearby, but not directly over, the highest density cells. More work will be needed to improve building footprint datasets by distinguishing residential and nonresidential buildings to avoid population being misallocated to business districts, factories, universities, airports, and other non-residential cells [98,99]. These two steps -use of building footprint covariates and finer-scale training data -stand to improve cell-level accuracy of gridded population datasets derived from complex models, including all WorldPop-Global datasets as well as LandScan [22,23], WPE [24], and GRID3 [26,86].…”

Section: Discussionmentioning

confidence: 99%

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Thomson¹,

Leasure²,

Bird³

et al. 2021

Preprint

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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

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“…Across all four models, hundreds of non-residential cells in an industrial zone near downtown Windhoek were allocated dozens of people each (see figure 5). Among the building covariates included in the WPG-Constrained model, not a single one distinguished between residential and non-residential buildings, though the algorithms and technologies to extract such information are being developed [58]. Inclusion of a building residential/non-residential dataset would likely improve estimates, especially in urban not-deprived areas, by further constraining settled residential areas.…”

Section: Discussionmentioning

confidence: 99%

Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

Thomson¹,

Stevens²,

Chen³

et al. 2021

Preprint

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People living in slums and other deprived areas in low- and middle-income country (LMIC) cities are under-represented in censuses, and subsequently in "top-down" gridded population estimates. Modelled gridded population data are a unique source of disaggregated population information to calculate local development indicators such as the Sustainable Development Goals (SDGs). This study evaluates if, and how, WorldPop-Global (WPG) -Unconstrained and -Constrained “top-down” datasets might be improved in a simulated realistic LMIC urban population by incorporating slum profile population counts into model training. We found that the WPG-Unconstrained model with or without slum training data grossly underestimated population in urban deprived areas while grossly overestimating population in rural areas. SDG 11.1.1, the percent of population living in slums, for example, was estimated to be 20% or less compared to a "true" value of 29.5%. The WPG-Constrained model, which included building auxiliary datasets, far more accurately estimated the population in all grid cells (including rural areas), and the inclusion of slum training data further improved estimates such that SDG 11.1.1 was estimated at 27.1% and 27.0%, respectively. Inclusion of building metrics and slum training data in “top-down” gridded population models can substantially improve grid cell-level accuracy in both urban and rural areas.

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Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings

Cited by 25 publications

References 29 publications

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

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

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

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