Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations Article Swipe
Jeremiah J. Nieves
,
Maksym Bondarenko
,
Alessandro Sorichetta
,
Jessica Steele
,
David Kerr
,
Alessandra Carioli
,
Forrest R. Stevens
,
Andrea E. Gaughan
,
Andrew J. Tatem
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12101545
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/rs12101545
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs12101545
- https://www.mdpi.com/2072-4292/12/10/1545/pdf?version=1589298692
- OA Status
- gold
- Cited By
- 8
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3024216234
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3024216234Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs12101545Digital Object Identifier
- Title
-
Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area PopulationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-12Full publication date if available
- Authors
-
Jeremiah J. Nieves, Maksym Bondarenko, Alessandro Sorichetta, Jessica Steele, David Kerr, Alessandra Carioli, Forrest R. Stevens, Andrea E. Gaughan, Andrew J. TatemList of authors in order
- Landing page
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https://doi.org/10.3390/rs12101545Publisher landing page
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https://www.mdpi.com/2072-4292/12/10/1545/pdf?version=1589298692Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/12/10/1545/pdf?version=1589298692Direct OA link when available
- Concepts
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Human settlement, Settlement (finance), Geography, Population, Physical geography, Environmental resource management, Cartography, Environmental science, Computer science, Archaeology, Sociology, Demography, Payment, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
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8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2022: 3, 2021: 2, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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78Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.“off-the-shelf” | 252 |
| abstract_inverted_index.“human-settlement” | 18 |
| abstract_inverted_index.built-/human-settlements | 9 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5002966902 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I43439940 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.66699466 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |