Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. (March 2020) - Nieves, Jeremiah J.; Sorichetta, Alessandro; Linard, Catherine; Bondarenko, Maksym; Steele, Jessica E.; Stevens, Forrest R.; Gaughan, Andrea E.; Carioli, Alessandra; Clarke, Donna J.; Esch, Thomas; Tatem, Andrew J.
Abstract: Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics. Highlights: Model framework correctly predicts 85–95% of built-settlement transitions for the most years. Model framework shows consistent performance across variety of settings. Model framework can fill in temporal coverage gaps of remotely sensed urban feature datasets. Relative changes in population count and density are sufficient predictors of built-settlement expansion.