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Predicting near-future built-settlement expansion using relative changes in small area populations

Predicting near-future built-settlement expansion using relative changes in small area populations
Predicting near-future built-settlement expansion using relative changes in small area populations

Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets 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. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000-2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover "Urban Areas" class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011-2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of "urban" or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available "off-the-shelf" datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement expansion. Further, this framework shows promise for predicting near-future settlement extents and provides a foundation for forecasts further into the future.

Built, Forecast, Growth model, Machine learning, Settlement, Time series, Urban
2072-4292
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Steele, Jessica E.
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Kerr, David
0cb7f738-0bf6-463f-b1f1-b380c6b568b8
Carioli, Alessandra
6c08c2a8-db01-49c5-811e-64a41d02845f
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Nieves, Jeremiah J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Steele, Jessica E.
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Kerr, David
0cb7f738-0bf6-463f-b1f1-b380c6b568b8
Carioli, Alessandra
6c08c2a8-db01-49c5-811e-64a41d02845f
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Nieves, Jeremiah J., Bondarenko, Maksym, Sorichetta, Alessandro, Steele, Jessica E., Kerr, David, Carioli, Alessandra, Stevens, Forrest R., Gaughan, Andrea E. and Tatem, Andrew J. (2020) Predicting near-future built-settlement expansion using relative changes in small area populations. Remote Sensing, 12 (10), [1545]. (doi:10.3390/rs12101545).

Record type: Article

Abstract

Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets 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. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000-2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover "Urban Areas" class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011-2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of "urban" or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available "off-the-shelf" datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement expansion. Further, this framework shows promise for predicting near-future settlement extents and provides a foundation for forecasts further into the future.

Full text not available from this repository.

More information

Accepted/In Press date: 11 May 2020
e-pub ahead of print date: 12 May 2020
Keywords: Built, Forecast, Growth model, Machine learning, Settlement, Time series, Urban

Identifiers

Local EPrints ID: 441796
URI: http://eprints.soton.ac.uk/id/eprint/441796
ISSN: 2072-4292
PURE UUID: fa63ff19-dd6c-469a-bdea-58ecdd351a6b
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 26 Jun 2020 16:45
Last modified: 07 Oct 2020 02:05

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Contributors

Author: David Kerr
Author: Alessandra Carioli
Author: Forrest R. Stevens
Author: Andrea E. Gaughan
Author: Andrew J. Tatem ORCID iD

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