LARGE-SCALE SPATIOTEMPORAL MODELING OF URBAN GROWTH WITH CYBERINFRASTRUCTURE: A SURROGATE-BASED APPROACH

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Feng, W. (2017). LARGE-SCALE SPATIOTEMPORAL MODELING OF URBAN GROWTH WITH CYBERINFRASTRUCTURE: A SURROGATE-BASED APPROACH. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Spatiotemporal simulations can provide critical insights to understand the underlying mechanisms of complex geographic phenomena. Therefore, spatiotemporal simulations play a vitally important role in solving the global geographic problems such as habitats loss, climate change, and deforestation. Due to complex mechanisms and big spatial data, computational intensity greatly hinders the application of spatiotemporal simulations at large scale. Cyberinfrastructure has been recognized as a promising tool to tackle computational intensity in spatiotemporal simulations. However, a challenge lies in the accurate estimation of its computing performance, which may prevent an efficient utilization of cyberinfrastructure. This dissertation demonstrates a surrogate-based approach to appropriately estimate the computing performance of parallel spatiotemporal simulations within cyberinfrastructure environments. A generalized computational framework is developed to integrate surrogate-based models, spatiotemporal simulations, and cyberinfrastructure. I applied the computational framework to simulate urban growth in North Carolina as a case study. Results show that surrogate-based approaches accurately estimate the computing performance. Kriging has a better prediction performance than linear regression surrogate-based model in this study. With the support of surrogate-based approaches, the computational framework substantially supports spatiotemporal simulations by efficiently handling computational intensity.

Details
Author

Feng, Wenpeng

Title

LARGE-SCALE SPATIOTEMPORAL MODELING OF URBAN GROWTH WITH CYBERINFRASTRUCTURE: A SURROGATE-BASED APPROACH

Physical Description

1 online resource (157 pages) : PDF

Date

2017

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Spatiotemporal simulations can provide critical insights to understand the underlying mechanisms of complex geographic phenomena. Therefore, spatiotemporal simulations play a vitally important role in solving the global geographic problems such as habitats loss, climate change, and deforestation. Due to complex mechanisms and big spatial data, computational intensity greatly hinders the application of spatiotemporal simulations at large scale. Cyberinfrastructure has been recognized as a promising tool to tackle computational intensity in spatiotemporal simulations. However, a challenge lies in the accurate estimation of its computing performance, which may prevent an efficient utilization of cyberinfrastructure. This dissertation demonstrates a surrogate-based approach to appropriately estimate the computing performance of parallel spatiotemporal simulations within cyberinfrastructure environments. A generalized computational framework is developed to integrate surrogate-based models, spatiotemporal simulations, and cyberinfrastructure. I applied the computational framework to simulate urban growth in North Carolina as a case study. Results show that surrogate-based approaches accurately estimate the computing performance. Kriging has a better prediction performance than linear regression surrogate-based model in this study. With the support of surrogate-based approaches, the computational framework substantially supports spatiotemporal simulations by efficiently handling computational intensity.

Genre

doctoral dissertations

Subjects--Topics

Geography

Degree

Ph.D.

Subject Area

Geography & Urban Regional Analysis

Advisor(s)

Tang, Wenwu

Committee Members

Delmelle, Eric
Campbell, Harrison
Pugalee, David

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2017.

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Rights Holder Information

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Identifier

Feng_uncc_0694D_11329

Permalink

http://hdl.handle.net/20.500.13093/etd:649