The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore
Open accessAbstract
Prior research developed a cellular automaton model, that was calibrated by using historical digital maps of urban areas and can be used to predict the future extent of an urban area. The model has now been applied to two rapidly growing, but remarkably different urban areas: the San Francisco Bay region in California and the Washington/Baltimore corridor in the Eastern United States. This paper presents the calibration and prediction results for both regions, reviews their data requirements, compares the differences in the initial configurations and control parameters for the model in the two settings, and discusses the role of GIS in the applications. The model has generated some long term predictions that appear useful for urban planning and are consistent with results from other models and observations of growth. Although the GIS was only loosely coupled with the model, the model's provision of future urban patterns as data layers for GIS description and analysis is an important outcome of this type of calculation.
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Citations
Cites
Cellular automata as models of complexity
Wolfram, S. (1984) · Nature · doi:10.1038/311419a0 · Cited by 1,507
Cited by
Soares-Filho, B. S., Coutinho Cerqueira, G., & Lopes Pennachin, C. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00059-5 · Cited by 587
Li, X., Chen, G., Liu, X., Liang, X., Wang, S., Chen, Y., Pei, F., & Xu, X. (2017) · Annals of the American Association of Geographers · doi:10.1080/24694452.2017.1303357 · Cited by 389
Geographic Information Science and Systems
Longley, P. A., & Frank Goodchild, M. (2020) · International Encyclopedia of Human Geography · doi:10.1016/b978-0-08-102295-5.10557-8 · Cited by 314
The Southern Megalopolis: Using the Past to Predict the Future of Urban Sprawl in the Southeast U.S
Terando, A. J., Costanza, J., Belyea, C., Dunn, R. R., McKerrow, A., & Collazo, J. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102261 · Cited by 255
Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience
Degbelo, A., Granell, C., Trilles, S., Bhattacharya, D., Casteleyn, S., & Kray, C. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5020016 · Cited by 166
Metadata
- Authors
- CLARKE, K. C., & GAYDOS, L. J.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 1998-11
- Volume
- 12
- Issue
- 7
- Pages
- 699-714
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W1999829841
- Cited by
- 1,065
- References
- 1
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. https://app.cochat.ai/papers/10.1080/136588198241617
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- CLARKE, K. C., & GAYDOS, L. J. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science. https://doi.org/10.1080/136588198241617
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
