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Statistical Modeling and Simulation of Land Development Dynamics
Tepe, Emre

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.

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Tepe, E. (2016). Statistical Modeling and Simulation of Land Development Dynamics. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Tepe, Emre. "Statistical Modeling and Simulation of Land Development Dynamics." Electronic Thesis or Dissertation. Ohio State University, 2016. OhioLINK Electronic Theses and Dissertations Center. 23 Oct 2017.

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Tepe, Emre "Statistical Modeling and Simulation of Land Development Dynamics." Electronic Thesis or Dissertation. Ohio State University, 2016. https://etd.ohiolink.edu/

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