Preprint Article Version 1 This version is not peer-reviewed

Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents

Version 1 : Received: 1 December 2019 / Approved: 2 December 2019 / Online: 2 December 2019 (05:15:03 CET)

How to cite: Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents. Preprints 2019, 2019120007 (doi: 10.20944/preprints201912.0007.v1). Nieves, J.J.; Bondarenko, M.; Sorichetta, A.; Steele, J.E.; Kerr, D.; Carioli, A.; Stevens, F.R.; Gaughan, A.E.; Tatem, A.J. Annual Projections of Future Built-Settlement Expansion Using Relative Changes in Projected Small Area Population and Short Time-Series of Built-Extents. Preprints 2019, 2019120007 (doi: 10.20944/preprints201912.0007.v1).

Abstract

Advances in the availability of multitemporal and global built-/human-settlements datasets as derived from Remote Sensing (RS) can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. We present a flexible modelling framework for producing annual built-settlement extents in the near future past last observed extents as provided by RS-based data. Using a random forest and autoregressive temporal models with short time-series of built-settlement extents and subnational level data, we predict annual 100m resolution binary settlement extents five years beyond the last observations. We applied this framework within varying contexts and predicted annual extents from 2010 to 2015. We found that our model framework preformed consistently across all sample countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and validation extents. When comparing building footprints of small settlements to forecast extents, we saw that the modelling framework had a 12 percent increase in ground-truth accuracy. This framework shows promise for predicting near-future settlement extents, and provides a foundation for forecasts further into the future.

Subject Areas

urban; growth model; forecast; built; settlement; machine learning; time series

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