Master of Arts, Miami University, 2021, Geography

The Pacific Golden Chanterelle (Cantharellus formosus) is a widely sought-after mushroom most abundant in the forests of Washington and Oregon, USA. This project used the species to investigate how accurately the species distribution could be modeled using natural history (herbarium) as model training data and citizen science (iNaturalist) as validation data. To combat the potential sampling bias towards population centers an effort variable weighting scheme was used to consider observations in harder to reach areas more than those in easier to access areas. Four models were created and run using the natural history data as training points: Random Forests (RF), Maxent, General Linear Model (GLM), and Artificial Neural Network (ANN); the effort variable was only applied to the ANN and GLM models. Out of these four, RF was found to perform the best with an equitable skill score (ETS) 0.987 when tested against the iNaturalist citizen science validation points. Overall, this project provides a good proof of concept and framework for the use of herbarium and citizen science data for use in biogeographical modeling projects in the future.

Committee: Mary Henry (Advisor); Jessica McCarty (Committee Member); Nicholas Money (Committee Member) Subjects: Geography

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

A two-step approach is developed to build an FEM model of a clamped structure focusing on the boundary stiffness and damping definitions. The approach utilizes FE model correlation with experimentally obtained modal parameter estimates to calibrate the model. Model calibration is first carried out in a free-free state to update the geometry, mass and material properties of the structure. The calibrated free-free model is then updated in two stages to include the boundary stiffness and damping for a clamped state. Stiffness is addressed first followed by damping. The stiffness is defined in terms of contact stiffness definitions in terms of normal and tangential stiffness at the boundary. Contact stiffness is determined by matching analytical and measured natural frequencies of the clamped built-up structure. Calibration is carried out in a mode-wise manner and the system response is calculated in the frequency domain in bands centered around each mode of interest. In the second step, the damping property of the structure is identified based on responses at resonance. The significance of spatial distribution of damping is studied by first developing an approach to include accurate spatial distribution in models based on intuitive knowledge of the location of damping and then using it to compare against models built with traditional damping models such as Rayleigh and modal damping. Accurate representation of spatial distribution of damping in FE models was observed to be not very important for lightly damped structures. The stiffness and damping modeling approaches developed were combined and demonstrated on a clamped steel beam. The calibrated model is then validated by demonstrating its ability to make accurate strain predictions to arbitrary load cases. Random broadband and banded chirp loads were both used to compare the system responses from simulation and testing. Simulated FRF from calibrated system and the force spectra of interest are used to obtain th (open full item for complete abstract)

Committee: Jay Kim Ph.D. (Committee Chair); Randall Allemang Ph.D. (Committee Member); Allyn Phillips Ph.D. (Committee Member); Yongfeng Xu Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science (MS), Bowling Green State University, 2016, Geology

The spatial paleoceanography of the entire Western Interior Seaway (WIS) during the Cenomanian-Turonian Oceanic Anoxic Event has been reconstructed quantitatively for the first time using Geographic Information Systems. Models of foraminiferal occurrences—derived from Dempster-Shafer theory and driven by fuzzy sets of stratigraphic and spatial data—reflect water mass distributions during a brief period of rapid biotic turnover and oceanographic changes in a greenhouse world. Dempster-Shafer theory is a general framework for approximate reasoning based on combining information (evidence) to predict the probability (belief) that any phenomenon may occur. Because of the inherent imprecisions associated with paleontological data (e.g., preservational and sampling biases, missing time, reliance on expert knowledge), especially at fine-scale temporal resolutions, Dempster-Shafer theory is an appropriate technique because it factors uncertainty directly into its models. Locality data for four benthic and one planktic foraminiferal species and lithologic and geochemical data from sites distributed throughout the WIS were compiled from four ammonoid biozones of the Upper Cenomanian and Early Turonian stages. Of the 14 environmental parameters included in the dataset, percent silt, percent total carbonate, and depositional environment (essentially water depth) were associated with foraminiferal occurrences. The inductive Dempster-Shafer belief models for foraminiferal occurrences reveal the positions of northern and southern water masses consistent with the oceanographic gyre circulation pattern that dominated in the seaway during the Cenomanian- Turonian Boundary Event. The water-mixing interface in the southwestern part of the WIS was mostly restricted to the Four Corners region of the US, while the zone of overlap of northern and southern waters encompassed a much larger area along the eastern margin, where southern waters occasionally entered from the (open full item for complete abstract)

Committee: Margaret Yacobucci Dr. (Advisor); Peter Gorsevski Dr. (Committee Member); Andrew Gregory Dr. (Committee Member) Subjects: Earth; Geographic Information Science; Geography; Geology; Marine Geology; Oceanography; Paleoecology; Paleontology; Statistics

PhD, University of Cincinnati, 2013, Engineering and Applied Science: Civil Engineering

The U.S. Environmental Protection Agency is concerned about fine particulate matter (also called as PM2.5 as the average particle size is less than 2.5 µm) pollution and its ill effects on public health. About 80 percent of the mobile-source PM2.5 emissions are released into the urban atmosphere through combustion of diesel fuel by trucks and are composed of road dust, smoke, and liquid droplets. To estimate the regional or local air quality impact of PM2.5 emissions and also to predict future PM2.5 concentrations, we often utilize atmospheric dispersion models. Application of such sophisticated dispersion models with finer details can provide us the comprehensive understanding of the air quality problem, including the quantitative effect of pollution sources. However, in the current practice the detailed truck specific pollution estimation is not easily possible due to unavailability of a modeling methodology with applied supporting data to predict the link-level hourly truck activity and corresponding emission inventory. In the first part of this dissertation, we have proposed a methodology for estimating the disaggregated link-level hourly truck activity based on advanced statistics in light of the AERMOD based dispersion/pollution modeling process. This new proposed truck model consists of following sub models: (a) The Spatial Regression and Optimization based Truck-demand (SROT) model is developed to predict truck travel demand matrices using the spatial regression model-output truck volumes at control locations in the study area. (b) The hourly distribution factor model to convert daily truck volumes to hourly truck volumes (c) The Highway Capacity Manual (HCM) based highway assignment model for assigning the hourly truck travel demand matrices. In the second part of dissertation, we have utilized the link-level hourly truck activity to predict the typical 24-hour and maximum 1-hr PM2.5 pollution in urban atmosphere. In this AERMOD based dispersion/pollution (open full item for complete abstract)

Committee: Heng Wei Ph.D. (Committee Chair); Hazem Elzarka Ph.D. (Committee Member); Mingming Lu Ph.D. (Committee Member); Ala Tabiei Ph.D. (Committee Member) Subjects: Transportation

Doctor of Philosophy, The Ohio State University, 2007, Computer and Information Science

The use of data mining techniques for the classification of shape and structure can provide critical results when applied biomedical data. On a molecular level, an object's structure influences its function, so structure-based classification can lead to a notion of functional similarity. On a more macro scale, anatomical features can define the pathology of a disease, while changes in those features over time can illustrate its progression. Thus, structural analysis can play a vital role in clinical diagnosis. When examining the problem of structural or shape classification, one would like to develop a solution that satisfies a specific task, yet is general enough to be applied elsewhere. In this work, we propose a workflow that can be used to model and analyze biomedical data, both static and time-varying. This workflow consists of four stages: 1) Modeling, 2) Biomedical Knowledge Discovery, 3) Incorporation of Domain Knowledge and 4) Visual Interpretation and Query-based Retrieval. For each stage we propose either new algorithms or suggest ways to apply existing techniques in a previously-unused manner. We present our work as a series of case studies and extensions. We also address a number of specific research questions. These contributions are as follows: We show that generalized modeling methods can be used to effectively represent data from several biomedical domains. We detail a multi-stage classification technique that seeks to improve performance by first partitioning data based on global, high-level details, then classifying each partition using local, fine-grained features. We create an ensemble-learning strategy that boosts performance by aggregating the results of classifiers built from models of varying spatial resolutions. This allows a user to benefit from models that provide a global, coarse-grained representation of the object as well as those that contain more fine-grained details, without suffering from the loss of information or noise effects th (open full item for complete abstract)

Committee: Srinivasan Parthasarathy (Advisor) Subjects: Computer Science

Doctor of Philosophy, University of Toledo, 2023, Spatially Integrated Social Science

The COVID-19 pandemic ranks as the deadliest on the list of disasters in the United States. After community transmission was confirmed in early 2020, federal and state governments introduced varying restrictions on public mobility through various Non-Pharmaceutical interventions (NPIs) in response to the growing pandemic. Through a spatial and temporal perspective, this study aims to investigate the impact of changing mobility patterns, NPI measures, and local weather on the spread of COVID-19 during the early years of the pandemic at the county level. The primary goals of the dissertation were 1) identify the influence of different mobility categories, policy indices, and county-specific weekly average temperature data on COVID-19 case positive rate between 2020-2020 by employing spatial analysis and econometric modeling. 2) leverage the identified spatiotemporal relationship to develop a spatial panel data weekly predictive model for COVID-19 case positivity at the county level and publish an ArcGIS online dashboard. Considering the diverse nature of mobility and NPIs, this study incorporates five different mobility categories and two different measures of policy stringency index and county-specific weekly average temperature data. From 2020 onwards, twelve pandemic phases were progressively evaluated for 104 weeks using Spatial-Autoregressive & Spatially Autocorrelated Errors Fixed Effect Panel Data Models for 2380 counties. The spatial spillover effects on how each county was influenced by its neighbors were also evaluated. Results revealed a positive correlation between all the outdoor mobility categories and COVID-19 case positivity with varying levels of confidence at different times during the pandemic, except for parks and recreational visits, which demonstrated a negative correlation. Policy indices of different containment measures and economic supports exhibited negative correlations, indicating the association between lower policy index value and highe (open full item for complete abstract)

Committee: Kevin Czajkowski (Committee Chair); Bhuiyan Alam (Committee Member); Sujata Shetty (Committee Member); Barbara Saltzman (Committee Member); April Ames (Committee Member); Yanqing Xu (Committee Member) Subjects: Epidemiology; Geographic Information Science; Geography; Public Health; Statistics

Master of Science (MS), Ohio University, 2023, Geography (Arts and Sciences)

This thesis aimed to understand the complex interactions between land-use changes and agricultural production to inform decision-making and maximize crop yields. The research used advanced tools and techniques, including GIS, remote sensing, and spatial modeling, to analyze changes in land cover classes over time. The results showed a significant shift in land cover, with cropland increasing from 43.15% in 2010 to 55.03% in 2016, while grassland decreased from 48.38% in 2010 to 36.69% in 2016. The thesis also explored the impact of environmental factors on maize yields in three Nigerian states, finding that temperature and precipitation was the most sensitive factor influencing yields, and that land cover changes had a moderate influence. The study highlights the importance of using advanced tools and techniques to analyze land use and cover changes and their impact on agricultural production. The findings provide valuable insights for remote sensing and GIS practitioners interested in monitoring land cover and land use changes for natural land preservation, urbanization, agricultural land expansion and sustainability, as well as farmland loss. Furthermore, this thesis demonstrates the importance of considering environmental factors such as temperature, normalized vegetation index, and precipitation alongside land cover changes to better understand the impact of different factors on crop yields. It is recommending the use of both multiple regression models and spatial geographical models to gain a better understanding of how different land cover changes affect crop yields, with the latter providing better results based on AIC and 𝑅2

Committee: Sinha Gaurav (Advisor); Dorothy Sack (Committee Member); Edna Wangui (Committee Member) Subjects: Geographic Information Science; Geography; Remote Sensing

PhD, University of Cincinnati, 2022, Arts and Sciences: Mathematical Sciences

Bayesian hierarchical modeling has a long history but did not receive wide attention until the past few decades. Its advantages include flexible structure and capability of incorporating uncertainty in the inference. This dissertation develops two Bayesian hierarchical models for the following two scenarios: first, spatial data of time to disease outbreak and disease duration, second, large or high dimensional functional data that may cause computational burden and require rank reduction. In the first case, we use cucurbit downy mildew data, an economically important plant disease data recorded in sentinel plot systems from 23 states in the eastern United States in 2009. The joint model is established on the dependency of the spatially correlated random effects, or frailty terms. We apply a parametric Weibull distribution to the censored time to disease outbreak data, and a zero-truncated Poisson distribution to the disease duration data. We consider several competing process models for the frailty terms in the simulation study. Given that the generalized multivariate conditionally autoregressive (GMCAR) model, which contains correlation and spatial structure, provides a preferred DIC and LOOIC results, we choose the GMCAR model for the real data. The proposed joint Bayesian hierarchical model indicates that states in the mid-Atlantic region tend to have a high risk of disease outbreak, and in the infected cases, they tend to have a long duration of cucurbit downy mildew. The second Bayesian hierarchical model smooths functional curves simultaneously and nonparametrically with improved computational efficiency. Similar to the frequentist counterpart, principal analysis by conditional expectation, the second model reduces rank through the multi-resolution spline basis functions in the process model. The proposed method outperforms the commonly used B-splines basis functions by providing a slightly better estimation within a much shorter computing time. The performanc (open full item for complete abstract)

Committee: Emily Kang Ph.D. (Committee Member); Seongho Song Ph.D. (Committee Member); Bledar Konomi Ph.D. (Committee Member); Won Chang Ph.D. (Committee Member) Subjects: Statistics

PhD, University of Cincinnati, 2021, Medicine: Biomedical Informatics

Last-minute surgery cancellation, also known as day-of-surgery cancellation (DoSC), represents a substantial wastage of hospital resources and can cause significant emotional and economic implications for patients and their families. However, only few existing studies attempted to predict risk of cancellation for individual surgical cases, hampering the development of efficient interventions in clinical settings. Also, we currently lack knowledge of actionable factors underlying DoSC and barriers experienced by families (e.g., poor transportation access). The objectives of this dissertation are to 1) identify key predictors and develop machine learning models to predict cancellation for individual surgery schedules, and 2) understand potential underlying contributors to pediatric surgery cancellation at geographic level. In Aim 1, five-year data sets were extracted from the electronic health record (EHR) at Cincinnati Children's Hospital Medical Center (CCHMC). By leveraging patient-specific information and contextual data, a representative set of machine learning classifiers were developed to predict cancellations. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using ten-fold cross-validation. The best performance for predicting all-cause cancellation was generated by gradient-boosted logistic regression models, with AUC 0.793 (95% CI: [0.778, 0.808]) and 0.741 (95% CI: [0.725, 0.757]) for the two campuses. Of the four most frequent individual cancellation causes, no show and NPO violation were predicted better than patient illness or patient/family refusal. Models showed good cross-campus generalizability (AUC: 0.725/0.735, when training on one site and testing on the other). Feature importance techniques were applied to identify key predictors. An online tool for predictive modeling was developed using R Shiny package. In Aim 2, a five-year geocoded data set was extracted from the CCHMC EHR and an equiv (open full item for complete abstract)

Committee: Surya Prasath Ph.D. (Committee Chair); Richard Brokamp Ph.D. (Committee Member); Danny T. Y. Wu (Committee Member); Jayant Pratap (Committee Member); Yizhao Ni Ph.D. (Committee Member) Subjects: Health Sciences

Doctor of Philosophy, The Ohio State University, 2021, Psychology

Recent studies of spatial attention suggest that the distribution of attention mapped by the flanker task shows local suppression of attention at small regions around the target (surround inhibition). I explored how facilitatory and inhibitory selective attention mechanisms form and modulate the distribution of attention, using a newly developed cognitive model that dissociates the two mechanisms assumed to underlie the distribution of attention: facilitation and inhibition. The model assumed that distinct facilitatory and inhibitory control mechanisms modulate the breadths of different parts of the attentional distribution. Two flanker task experiments sought to modulate selectively the operations of the two mechanisms, also reflected by changes in two parameters of a computational model of selective attention in the task. The behavioral and modeling results showed that inhibition and facilitation were not selectively modulated by the experimental manipulations. However, assessment of two alternative measures of selective attention in the flanker task, the magnitude of flanker interference and the breadth of the attentional distribution, showed evidence for distinct facilitatory and inhibitory control mechanisms.

Committee: Mark Pitt (Advisor); Andrew Leber (Committee Member); Jay Myung (Committee Member) Subjects: Cognitive Psychology

PhD, University of Cincinnati, 2018, Arts and Sciences: Mathematical Sciences

Many geophysical processes evolve in space and time, resulting in complicated data including nonstationary and nonseparable covariance structures, and highly complex dynamics. With the advance of new remote-sensing technologies, massive amount of these datasets can be collected at very high spatial resolutions each day from satellite instruments. These data are often noisy and irregularly observed with incompatible supports as well. These challenges require new statistical methods to account for both model flexibility and computational efficiency. In this dissertation, three novel approaches are proposed: 1) the covariance function model that incorporates low-rank representation and spatial graphical model, to allow nonstationarity and robust predictive performance. This leads to a kriging methodology called fused Gaussian process (FGP); 2) the downscaling framework based on FGP to carry out conditional simulation to generate high-resolution fields; 3) the low-cost Bayesian inference framework for an additive covariance function model that combines any type of computational-complexity-reduction methods (e.g., low-rank representation) and separable covariance structure together, to allow nonseparability and good predictive performance. This leads to another kriging methodology called additive approximate Gaussian process (AAGP). The methodology in FGP relies on a small set of fixed spatial basis functions and random weights to model large-scale variation of a nonstationary process, and a Gaussian graphical model to capture remaining variation. This method is applied to analyze massive amount of remotely-sensed sea surface temperature data. Another important application based on FGP is to generate high-resolution nature runs in global observing system simulation experiments, which have been widely used to guide the development of new observing systems, and to evaluate the performance of new data assimilation algorithms. The change-of-support problem is handled exp (open full item for complete abstract)

Committee: Emily Kang Ph.D. (Committee Chair); Bledar Konomi Ph.D. (Committee Chair); Shan Ba Ph.D. (Committee Member); Won Chang Ph.D. (Committee Member); Siva Sivaganesan Ph.D. (Committee Member) Subjects: Statistics

PhD, University of Cincinnati, 2017, Arts and Sciences: Mathematical Sciences

Modeling of spatial data often encounters computational bottleneck for large datasets and change- of-support effect for data at different resolutions. There is a rich literature on how to tackle these two problems but few gives a comprehensive solution for solving them together. This dissertation aims to develop hierarchical models that can alleviate those two problems together in uncertainty quantification and data fusion. For uncertainty quantification, a fully Bayesian hierarchical model combined with the nearest neighbor Gaussian process is proposed to produce consistent parameter inferences at different resolutions for a large spatial surface. Simulation studies demonstrate the ability of the proposed model to provide consistent parameter inferences at different resolutions with only a fraction of the computing time of the traditional method. This method is then applied to a real surface data. For data fusion, we propose a hierarchical model that can fuse two or more large spatial datasets with the exponential family of distributions. The ''change-of-support'' problem is handled along with the computational bottleneck by using a spatial random effect model for the underlying process. Through simulated and real data illustrations, the proposed data fusion method is demonstrated to possess predictive advantage over the univariate-process modeling approach by borrowing strength across processes.

Committee: Emily Kang Ph.D. (Committee Chair); Hang Joon Kim Ph.D. (Committee Member); Bledar Konomi Ph.D. (Committee Member); Siva Sivaganesan Ph.D. (Committee Member); Xia Wang Ph.D. (Committee Member) Subjects: Statistics

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2016, Biological Sciences

Eastern Box Turtles (Terrapene c. carolina) have experienced range-wide declines as the result of extensive habitat loss, fragmentation, and alteration. The Oak Openings Region of northwestern Ohio is a biodiversity hotspot that exists in a highly fragmented landscape and provides a unique case study from which to examine the effects of anthropogenic disturbance on Eastern Box Turtles. In an effort to inform management and conservation efforts in the Oak Openings Region, I initiated a radio-telemetry project with the following objectives: 1) examine the spatial ecology of Eastern Box Turtles over several years to understand how they interact with their habitat in an area as unique as the Oak Openings Region, 2) develop predictive models depicting the temporal distributions of Eastern Box Turtles, 3) examine the impacts of one of the most common management tools in the Oak Openings Region, prescribed fire, on Eastern Box Turtles, and 4) evaluate pattern-recognition software as a low-cost alternative of identifying individual Eastern Box Turtles. Turtles at my study site exhibited larger home ranges than previously reported for this species as well as hierarchical habitat selection at multiple scales. Predicted distributions followed phenological shifts in habitat use and were influenced primarily by habitat type and canopy cover. Prescribed fires have the potential to have a devastating effect on box turtle populations, but management activities that take box turtle ecology into account will minimize these impacts while maintaining a critical disturbance regime. Computer-assisted photo-recognition has a great deal of potential as a supplemental method of identifying box turtles and provides a low-cost means of incorporating citizen science data into mark-recapture studies. My work suggests that conservation for Eastern Box Turtles in the Oak Openings Region should focus on maintenance and restoration of remaining box turtle habitat, connectivity between critical hab (open full item for complete abstract)

Committee: Karen Root Dr. (Advisor); Shannon Pelini Dr. (Committee Member); Jeff Miner Dr. (Committee Member); Enrique Gomezdelcampo Dr. (Committee Member); Salim Elwazani (Other) Subjects: Conservation; Ecology; Wildlife Conservation; Wildlife Management

Doctor of Philosophy, The Ohio State University, 2016, 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 w (open full item for complete abstract)

Committee: Jean-Michel Guldmann (Advisor); Philip A. Viton (Committee Member); Gulsah Akar (Committee Member) Subjects: Economics; Land Use Planning; Regional Studies; Statistics; Urban Planning

PhD, University of Cincinnati, 2016, Engineering and Applied Science: Civil Engineering

The conventional carbon emission-modeling framework focuses on the link-based emissions and then aggregate to regional inventory. This approach is incapable of tracing emissions back to its geographical origin and providing information on areas where adaptive planning policies and strategies are needed. Recent studies also indicate potential deficiencies in converting four-step travel demand outputs into the inputs of emission models. Emission models often rely on four-step models for vehicle activity inputs. However, these models are mostly calibrated and validated using aggregated daily traffic data. No data sources are available to validate the models at the hourly or the most desired second-by-second level for emission estimates. The recent advancement of mobile device sensors and data transmitting technologies provide travel trajectories (e.g., latitude, longitude, speed, acceleration, altitude) collected from the users of smart phones or other GPS-enabled devices. The availability of such data sources will actually provide new opportunities of enhancing our understanding and modeling of the dynamics between land use pattern, travel behavior, and the associated environmental impacts. These trends call for the emergence of quick-response modeling framework that could be supported by the smart data source. In this research, a research question is proposed to well direct the proposed research: is it positively possible to use the Smart-Data structured data sets to unveil the sophisticated dynamics between land use changes and its associated carbon emission impacts, if a smart data adaptive modeling framework for this attempt is well developed? The answer to the research question will benefit the integration of the actual and scenario-based land use visioning and planning, demographic changes, transportation emission analysis, and computer forecasting and evaluation of future scenarios. This research makes it possible to assess the household travel carbon footpri (open full item for complete abstract)

Committee: Heng Wei Ph.D. (Committee Chair); Andrew S. Rohne M.ENG. (Committee Member); Steven Buchberger Ph.D. (Committee Member); Xinhao Wang Ph.D. (Committee Member) Subjects: Transportation

Doctor of Philosophy, The Ohio State University, 2015, City and Regional Planning

Studies of intra-urban migration have identified a number of factors that influence the likelihood that people will move. The driving forces in population and land use vary over time and space while interacting with others. Therefore, various fields of study have been interested in this subject. However, existing land use change models mainly focus on predicting land-use growth so that the literature does not include many studies that predict a decline. The purpose of this study is to model land use change both growth and to decline and to determine which factor influence land use change at a sub-metropolitan scale. Using data from the metropolitan areas of Ohio, we examine the influence of demographic, socioeconomic, housing and neighborhood conditions, physical accessibility, and regional contexts on the rate of neighborhood growth and decline between 1990 and 2000 at the census tract level. These indicators are used to build a set of probabilities for growth and decline. Based on these influential factors, we can determine which variables have positive or negative effects on population changes that are related to land use change. Through the model, we can determine how growing/declining areas characterize. Therefore, these models provide good guidelines for ways to improve the quality of a region. Further, policymakers can take these negative and positive factors into account in order to meet needs of the region and allocate appropriate services or facilities to prepare for an uncertain future.

Committee: Steven Gordon (Advisor); Rachel Kleit (Committee Member); Gulsah Akar (Committee Member) Subjects: Land Use Planning; Urban Planning

Doctor of Philosophy, The Ohio State University, 2015, Statistics

An important statistical challenge lies in the specification of flexible spatio-temporal models capable of capturing complex, nonstationary dependence structure in the underlying process of interest. Popular approaches based on stationary, often parametric, spatial autocorrelation functions or Markov random field constructions (e.g., the conditional autoregression (CAR) or simultaneous autoregression (SAR) models) often yield representations of spatial coherence that are overly simplistic, leading to procedures with limited capacity to reproduce structure suggested by the data. While there is much interest in developing nonstationary extensions of these common approaches, in this work, we propose alternative techniques with which to allow the borrowing of information (over space and time) in a data-driven manner. We develop a Bayesian hierarchical modeling approach in which dependence is modeled via random spatial or spatio-temporal partitioning obtained indirectly via the construction of local random networks in space and time. We demonstrate the approach within the problems of (1) mapping of disease incidence/mortality rates over space and time and (2) interpolation of processes over continuous domains. This is the focus of the first two chapters in this work. In the third chapter, the focus is a separate problem. In evaluating/communicating the human role in Earth's changing climate, it is essential that causal assessments make use of both scientific understanding of the underlying physical mechanisms at work as well as information contained in observations. Making use of the potential outcomes framework for causal inference in counterfactual problems, we propose a Bayesian hierarchical physical-statistical approach in which output from a simple climate model is combined with observational data, allowing fusion of the two sources of information in a statistically principled manner. We demonstrate how counterfactual inference regarding climatic events of inter (open full item for complete abstract)

Committee: Mark Berliner (Advisor); Steven MacEachern (Committee Member); Kate Calder (Committee Member); Peter Craigmile (Committee Member) Subjects: Statistics

MS, University of Cincinnati, 2015, Engineering and Applied Science: Civil Engineering

Travel networks are the backbone of an economy. The urban structure relies heavily on transport systems to grow and interact with the environment. They play an important role in shaping the land use of a region as mobility is instrumental in the process of location choice for residential and non-residential developments. However, land use changes are also governed by economic, governmental and environmental factors besides accessibility and hence, they indirectly affect transport systems too. Therefore, the ability to predict interactions between future land use and social economy for travel demand forecasting is of tremendous significance from the planning perspective. Among widely applied land use models, UrbanSim is gaining edge over others, being an open source system that models changes in spatial characteristics of the households and jobs in accordance with changing travel accessibilities and land prices. It allows for land use change simulation at varied geographic resolutions at parcel level and zone level. While the parcel-level version has been the most widely used in planning for its capability of featuring location-based socioeconomic factors at a disaggregate level, its intense data requirements also pose a big challenge or an extreme difficulty to validation efforts in practice. The relatively less-implemented zone-version of UrbanSim simulates household and employment changes at an aggregated Traffic Analysis Zone (TAZ) level with a relatively lesser data demand. However, the zone version does not account for the land use effect into the modeling approach directly. Instead, it uses only zoning data in one of the input tables in order to calculate development capacities in zones. But such zoning plans are only regulations to preserve land use and are not exactly representative of the actual land use at a location. The aim of this study was to overcome this weakness of the zone-version of UrbanSim with lack of focus on spatial effect of land (open full item for complete abstract)

Committee: Heng Wei Ph.D. (Committee Chair); Andrew Rohne M.ENG. (Committee Member); Hazem Elzarka Ph.D. (Committee Member) Subjects: Transportation

Doctor of Philosophy, University of Toledo, 2014, Biology (Ecology)

Due to recent recognition that ballast water is playing an important role in the spread of invasive species within the Great Lakes, there has been increasing interest in implementing management strategies that include a secondary spread component for ballast discharge. Using ballast water data for ships visiting U.S. ports in the Great Lakes, I created a dynamic spatial model to simulate the spread of invasive species based on recent shipping patterns. My goal in producing this model was to provide information to natural resource managers, scientists, and policy-makers to help effectively regulate invasive species issues. In testing the model, I determined that including the number of discharging ship visits that a location receives from previously infested areas and the ability of an organism to survive in the ballast tank were important in more accurately identifying the past spread of the fish virus, viral hemorrhagic septicemia virus (VHSV), zebra mussel (Dreissena polymorpha), and Eurasian Ruffe (Gymnocephalus cernuus), than discharge location alone. I also included and tested a localized spread distance that simulated the dispersal of an invasive species upon being discharged at a location. I first applied the model to identify if ballast water played a role in the secondary spread of VHSV. Results indicated that ballast water movement has contributed to the spread of VHSV in the Great Lakes, albeit it is not the only vector of secondary spread. However, ballast water management would be an important part of any plan in preventing the future spread of VHSV in an ecosystem. Next, I applied the model to predict the future spread of Eurasian Ruffe, which already occurs in the Great Lakes, and two species that do not, golden mussel (Limnoperna fortune) and killer shrimp (Dikerogammerus villosus). The results of the prediction models are intended to be used to help direct early detection monitoring efforts. The Eurasian Ruffe results are currently being used by Th (open full item for complete abstract)

Committee: Jonathan Bossenbroek PhD (Committee Chair); Peter Lindquist PhD (Committee Member); Christine Mayer PhD (Committee Member); Darryl Moorhead PhD (Committee Member); David Reid PhD (Committee Member) Subjects: Aquatic Sciences; Ecology; Environmental Science

Doctor of Philosophy, The Ohio State University, 2012, Agricultural, Environmental and Developmental Economics

For many decades, the relationship between urban and rural places was well understood. Beginning in the early 20th century, however, this distinct dichotomy broke down as large numbers of businesses and people migrated out of the central city. As a result of this expansion of the urban center and the growth in suburban and exurban development, many urban fringe areas in the U.S. have become characterized by low-density and fragmented development. As a result of these changing land use patterns and the potential for both positive and negative outcomes, researchers and policymakers have become interested in understanding both the demand-side and supply-side incentive mechanisms that have led to this type of fragmented development. The objective of this research is to fill several gaps in the empirical literature on residential land conversion and land use policy by using unique micro-level data on historical subdivision development, land conversion, the platting and subdivision approval process, house prices and policy changes. In our first essay, we build on the growing literature that looks at the effect of regulation on housing supply decisions and focus specifically on the question of whether the expected time to completion affects both the decision to develop as well as the quantity of lots chosen by the individual landowners. Using a unique micro dataset on the timing of subdivision approvals by a local planning agency and a sample selection Poisson model, we test the effects of implicit costs that arise from uncertain subdivision approval on the timing, quantity and pattern of residential subdivision development. Consistent with theory, we find that these regulation-induced implicit costs reduce the probability and size of subdivision development on any given parcel. Our results contribute to the growing supply-side literature on housing and land use, and provide a new explanation of scattered residential development as the outcome of heterogeneous regulatory c (open full item for complete abstract)

Committee: Elena Irwin PhD (Advisor); Mark Partridge PhD (Committee Member); Abdoul Sam PhD (Committee Member) Subjects: Agricultural Economics; Economics; Environmental Economics; Public Policy; Urban Planning