Data-driven spatial modeling of global long-term urban land development: The SELECT model
Abstract
Built-up land/impervious surface expansion links urbanization and environmental change. To enable large-scale long-term spatially-explicit studies, we took a data-driven approach exploiting newly-available time series of fine-spatial-resolution remote sensing observations, and developed the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Closely calibrated to observational data, SELECT functions at several spatial scales, with multiple design traits capturing local variations of urbanization, and ensuring performance for long-term extrapolations in scenario analyses (e.g. the Shared Socioeconomic Pathways). It showed low estimation residuals, explained high fractions of the response's variations, and scored well in all robustness and generalizability tests we ran. When compared with a typical spatial-interaction-based model for projecting global built-up land in 2030, SELECT allocated more new development to areas with similar characteristics to locations that exhibited expansive urban growth historically, while the example spatial-interaction-based model allocated more new development to areas with high amounts of existing built-up land.
- Authors:
- Publication Date:
- Research Org.:
- Iowa State Univ., Ames, IA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1531176
- Alternate Identifier(s):
- OSTI ID: 1612556
- Grant/Contract Number:
- SC0016438
- Resource Type:
- Published Article
- Journal Name:
- Environmental Modelling and Software
- Additional Journal Information:
- Journal Name: Environmental Modelling and Software Journal Volume: 119 Journal Issue: C; Journal ID: ISSN 1364-8152
- Publisher:
- Elsevier
- Country of Publication:
- United Kingdom
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; SELECT; built-up; impervious surface; urban; SSPs; land cover/land use change
Citation Formats
Gao, Jing, and O'Neill, Brian C. Data-driven spatial modeling of global long-term urban land development: The SELECT model. United Kingdom: N. p., 2019.
Web. doi:10.1016/j.envsoft.2019.06.015.
Gao, Jing, & O'Neill, Brian C. Data-driven spatial modeling of global long-term urban land development: The SELECT model. United Kingdom. https://doi.org/10.1016/j.envsoft.2019.06.015
Gao, Jing, and O'Neill, Brian C. Sun .
"Data-driven spatial modeling of global long-term urban land development: The SELECT model". United Kingdom. https://doi.org/10.1016/j.envsoft.2019.06.015.
@article{osti_1531176,
title = {Data-driven spatial modeling of global long-term urban land development: The SELECT model},
author = {Gao, Jing and O'Neill, Brian C.},
abstractNote = {Built-up land/impervious surface expansion links urbanization and environmental change. To enable large-scale long-term spatially-explicit studies, we took a data-driven approach exploiting newly-available time series of fine-spatial-resolution remote sensing observations, and developed the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Closely calibrated to observational data, SELECT functions at several spatial scales, with multiple design traits capturing local variations of urbanization, and ensuring performance for long-term extrapolations in scenario analyses (e.g. the Shared Socioeconomic Pathways). It showed low estimation residuals, explained high fractions of the response's variations, and scored well in all robustness and generalizability tests we ran. When compared with a typical spatial-interaction-based model for projecting global built-up land in 2030, SELECT allocated more new development to areas with similar characteristics to locations that exhibited expansive urban growth historically, while the example spatial-interaction-based model allocated more new development to areas with high amounts of existing built-up land.},
doi = {10.1016/j.envsoft.2019.06.015},
journal = {Environmental Modelling and Software},
number = C,
volume = 119,
place = {United Kingdom},
year = {Sun Sep 01 00:00:00 EDT 2019},
month = {Sun Sep 01 00:00:00 EDT 2019}
}
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https://doi.org/10.1016/j.envsoft.2019.06.015
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