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Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night

Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night
Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night
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.
0198-9715
1-14
Nieves, Jeremiah, J.
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Sorichetta, Alessandro
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Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Steele, Jessica
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Stevens, Forrest R.
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Gaughan, Andrea E.
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Carioli, Alessandra
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Clarke, Donna
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Esch, Thomas
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Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Nieves, Jeremiah, J.
2b5f2f25-afc0-4585-8531-dc2acc4b3511
Sorichetta, Alessandro
c80d941b-a3f5-4a6d-9a19-e3eeba84443c
Linard, Catherine
231a1de7-72c2-4dc1-bc4e-ea30ed444856
Bondarenko, Maksym
1cbea387-2a42-4061-9713-bbfdf4d11226
Steele, Jessica
5cbba8c8-f3fd-41ee-82c8-0aa13c04c04d
Stevens, Forrest R.
7c96c2ef-edac-41a1-be26-c4bc5b3256a6
Gaughan, Andrea E.
395221c6-1091-4657-af7e-bd6cb93dbaf9
Carioli, Alessandra
6c08c2a8-db01-49c5-811e-64a41d02845f
Clarke, Donna
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Esch, Thomas
0ebe775b-b9ef-4e31-9736-e097e8a8d5ff
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Nieves, Jeremiah, J., Sorichetta, Alessandro, Linard, Catherine, Bondarenko, Maksym, Steele, Jessica, Stevens, Forrest R., Gaughan, Andrea E., Carioli, Alessandra, Clarke, Donna, Esch, Thomas and Tatem, Andrew (2020) Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. Computers, Environment and Urban Systems, 80, 1-14, [101444]. (doi:10.1016/j.compenvurbsys.2019.101444).

Record type: Article

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.

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Accepted/In Press date: 11 November 2019
e-pub ahead of print date: 27 November 2019
Published date: March 2020

Identifiers

Local EPrints ID: 437171
URI: http://eprints.soton.ac.uk/id/eprint/437171
ISSN: 0198-9715
PURE UUID: 2623bf40-f74d-4a08-9250-781814d77b26
ORCID for Alessandro Sorichetta: ORCID iD orcid.org/0000-0002-3576-5826
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 21 Jan 2020 17:31
Last modified: 07 Oct 2020 02:05

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