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Statistical Modeling and Simulation of Land Development Dynamics

Tepe, Emre
http://rave.ohiolink.edu/etdc/view?acc_num=osu1462631559

Abstract Details

2016, Doctor of Philosophy, Ohio State University, City and Regional Planning.
The impacts of neighborhood and historical conditions on land parcel development have been recognized as important to derive a robust understanding of land dynamics. However, dynamic models that incorporate spatial and temporal dependencies explicitly involve challenges in data availability, methodology and computation. Recent improvements in GIS technology and the growing availability of spatially explicit data at disaggregate levels offer new research opportunities for spatio-temporal modeling of urban dynamics. Parameter estimation requires more complicated methods to maximize complex likelihood functions with analytically intractable normalizing constants. Furthermore, working with a parcel-level dataset quickly increases sample size, with additional computational challenges for handling large datasets. In this research, parcel-level urban dynamics are investigated with the geocoded Auditor’s tax database for Delaware County, Ohio. In contrast to earlier research using time series of remote-sensing and land-cover data to derive measures of urban land-use dynamics, the available information on the year when construction took place on each parcel is used to measure these dynamics. A binary spatio-temporal autologistic model (STARM), incorporating space and time and their interactions, is first used to investigate parcel-level dynamics. This model is able to capture the impacts of the contemporaneous and historical neighborhood conditions around parcels, and is a modified version of the autologistic model introduced by Zhu, Zheng, Carroll,and Aukema (2008). Second, a multinomial STARM is formulated as an extension of the binary case in order to estimate the probability of parcel status change to a discrete land-use category. To the best of our knowledge, methods for the estimation of the parameters of binary spatial-temporal autologistic models are not available in any commercial and open source statistical software. A statistical program was written in Python that estimates Monte Carlo Maximum Likelihood parameters of STARM. Parallel processing techniques are used, due to the computational challenges in parameter estimations when using the complete dataset (73,000 parcels). This study contributes to the modeling of land development by demonstrating quantitatively the impacts of contemporaneous and historical neighborhood conditions on land dynamics, while offering a feasible methodological and computational approach.
Jean-Michel Guldmann (Advisor)
Philip A. Viton (Committee Member)
Gulsah Akar (Committee Member)
168 p.
Economics; Land Use Planning; Regional Studies; Statistics; Urban Planning
urban dynamics, spatial and temporal modeling, land use change, urban growth, autologistic regression, big data

Recommended Citations

Citations

  • Tepe, E. (2016). Statistical Modeling and Simulation of Land Development Dynamics [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462631559

    APA Style (7th edition)

  • Tepe, Emre. Statistical Modeling and Simulation of Land Development Dynamics. 2016. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1462631559.

    MLA Style (8th edition)

  • Tepe, Emre. "Statistical Modeling and Simulation of Land Development Dynamics." Doctoral dissertation, Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1462631559

    Chicago Manual of Style (17th edition)

osu1462631559
749
© 2016, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.