Doctor of Philosophy, The Ohio State University, 2023, Biomedical Engineering

Quantitative colocalization analysis is a standard method in the life sciences used for evaluating the global spatial proximity of labeled biomolecules captured by fluorescence microscopy images. It is typically performed by characterizing the pixel-wise signal overlap or intensity correlation between spectral channels. However, this approach is critically flawed due to its focus on individual pixels which limits assessment to a single spatial scale constrained by the pixel's size, thus making the analysis dependent on the achieved optical resolution and ignorant of the spatial information presented by non-overlapping signals. In this dissertation, I present an improved method for quantifying biomolecule spatial proximity using a novel application of point process analysis adapted for discrete image data, and subsequently utilize it to address two novel cardiac conundrums. The tool, called Spatial Pattern Analysis using Closest Events (SPACE), leverages the distances between signal-positive pixels to statistically characterize the spatial relationship between labeled biomolecules from fluorescence microscopy images. In chapter two, SPACE's underlying theory and its adaption for discrete image-based data is described. Additionally, I characterize its sensitivity to segmentation parameters, image resolution, and signal sample size, and demonstrate its advantages over standard colocalization methods. With this tool, I hope to provide microscopists an improved method to robustly characterize spatial relationships independent of imaging modality or achieved resolution. In chapter three, SPACE is used to elucidate a novel, microtubule-based system for the distributed synthesis of membrane proteins in cardiomyocytes. Canonically, these cells are thought to produce membrane proteins in the peri-nuclear rough endoplasmic reticulum, then leverage the secretory-protein-trafficking pathway to transport nascent proteins to their sites of membrane insertion. By labeling car (open full item for complete abstract)

Committee: Rengasayee Veeraraghavan (Advisor); Przemysław Radwański (Committee Member); Peter Craigmile (Committee Member); Seth Weinberg (Committee Member) Subjects: Biology; Biomedical Engineering; Biomedical Research; Biophysics; Biostatistics; Cellular Biology; Engineering; Scientific Imaging; Statistics

Master of Science, The Ohio State University, 2014, Industrial and Systems Engineering

In today's world of hybrid-electric vehicles (HEVs) adoption analysis, most studies assume `static preferences', which concentrate on analyzing isolated individual characteristics and ignoring the `neighbor interactions'. However, in such studies, the observations may not be independent of one another because the likelihood of adopting a HEV of one household may affected by the HEV adoption behavior in neighboring households. In order to take the `neighborhood effects' into consideration and focus on spatial clustering, this study uses spatial models to analyze the spatial correlations of HEV adoption behavior. Since it is not tractate to do spatial analysis down to individual household level, this study analyzes spatial correlations at the census-tract level. The ultimate goals of this study are to examine whether the spatial correlations exhibit, and to interpret any spatial correlations, explore time lags in spatial model, and calculate the marginal effects of HEV adoption in neighboring census tracts. In this study, the demographic data is collected from 2010 Census Summary File and 2012 American Community Survey 5-year estimates from the U.S census bureau. The vehicle data is provided by Ohio Bureau of Motor Vehicles. The results of this study demonstrate that spatial correlations do exist by comparing the ordinary least square, spatial auto-regressive, spatial error, and general spatial models. Secondly, the marginal effects of an additional HEV in one census tract on its neighboring tracts is found. Finally, this study conducts a time-lagged analysis which reveals HEV from 2001 to 2008 affects HEV adoption since 2009.

Committee: Ramteen Sioshansi (Advisor); Matthew Robert (Committee Member) Subjects: Industrial Engineering; Operations Research

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

The increase in rate of international migration to the United States since the late 1960s, coupled with a generally high rate among minority populations, altered the racial and ethnic composition of America's urban neighborhoods. The changing demography and increase in shares of minority subpopulations underscore the salience of conducting multigroup studies of residential and socioeconomic segregation beyond the traditional white versus black dichotomy. Segregation based on subgroup characteristics (de facto or de jure) is problematic, particularly for racial minorities and low-income residents who are limited in moving to areas they can afford. These minority neighborhoods are associated with physical and socioeconomic disadvantage due to public and private de-investment. The undercurrents of segregation were explored in the racial tipping point and white flight literature where non-Hispanic white majority residents exit old inner and central city neighborhoods when the share of minority populations increase beyond a critical threshold. Due to strong correlations between race and income, white flight also tends to concentrate poverty in the abandoned neighborhoods. Beyond this relationship between personal choice and segregation however, local and federal public policies have also been historically linked with segregating urban America. Federal highway programs, mortgage loan underwriting processes, suburban housing developments, and restrictive local zoning laws have created race and income-based segregated spaces. Also, reinvestment programs aimed revitalizing physical and socially distressed neighborhoods tend to yield minimal outcomes. This is often due to either limited funding compared to the magnitude of the problem or lack of sustained political commitment, overemphasis on market-based ideas which alienate minorities and low-income residents, and emphasis on new urbanism housing designs associated net losses in the public housing stock. In this dissertatio (open full item for complete abstract)

Committee: Daniel Hammel (Committee Chair); Sujata Shetty (Committee Member); Isabelle Nilsson (Committee Member); Neil Reid (Committee Member); Jami Taylor (Committee Member) Subjects: Geographic Information Science; Geography; Public Policy; Urban Planning

Doctor of Philosophy, Case Western Reserve University, 2022, Art History

In the early modern period, elite collectors began amassing magnificent collections of both locally produced and imported objects. Few were as innovative as Amalia van Solms-Braunfels (1602-1675), Princess of Orange. Under Amalia and her husband, Stadtholder Frederik Hendrik (1584-1647), Prince of Orange, the United Provinces flourished as a cultural and global power. The strength and wealth of the country, and by association the House of Orange-Nassau, is embedded in Amalia's cabinets or closets, private spaces where she carefully curated assemblages of locally produced and imported decorative and fine artworks. Under the weight of a historiographic tradition that privileges male rulers, much of the scholarship produced on the princely couple's cultural activities marks Frederik Hendrik or Constantijn Huygens as the deciding factor without discussion or justification. While scholarly interest in Amalia's role as an independent patron and collector has grown over the last two decades, the focus to date on individual, extant objects, while informative, does not provide a comprehensive understanding of Amalia's interests and motivations as a patron and collector-- how she acquired and employed objects, both individually and in decorative ensembles, to construct her various identities. My dissertation focuses on Amalia's cabinets found in the Stadtholder's Quarters (Stadhouderlijk Kwartier) and the Oude Hof (‘Old Court') at Noordeinde, and the objects displayed within. Uniting textual and visual evidence in the form of inventories, correspondence, and objects with novel digital tools, it first applies social network analysis to visualize Amalia's social, global network that provided her with access to other impressive collections and artists, as well as assisted her with acquiring objects originating from outside of Europe. It interrogates how, once acquired, objects were employed by Amalia in ensembles within the most intimate spaces of her residences to construct her (open full item for complete abstract)

Committee: Catherine Scallen (Advisor); Andrea Wolk Rager (Committee Member) Subjects: Art History

Master of Science (MS), Wright State University, 2021, Earth and Environmental Sciences

The non-native invasive shrub Amur honeysuckle, Lonicera maackii (Rupr.) Herder (Gorchov and Trisel, 2003), is one of the most prolific invasive plant species across Midwestern and Northeastern landscapes of the United States. The locations of 2,095 individual Amur honeysuckle stems were geolocated using handheld GPS units in the understory of mixed growth forests at two study sites located approximately 5 km apart in northwestern Greene County, OH. Each site has undergone different levels of anthropogenic disturbance through time. The stem position data was used to measure the spatial clumping distribution and the density of Amur honeysuckle. The spatial clumping of Amur honeysuckle stems was measured using the fractal box counting method at each study site without regard for streams, trails, or elevation. The density of Amur honeysuckle (number of stems per square meter) was measured in zones as a function of the horizontal distance perpendicular to the edge of streams, trails, and within elevation (area between contour lines). Amur honeysuckle density is found to be uncorrelated with its proximity to streams, trails, and elevation. The density of Amur honeysuckle as a function of distance from streams and trails does not reveal an edge effect. The fractal dimension (scaling exponent) was computed to be ~1.5 at each of the two sites which means that the spatial clustering is the same for actively managed (partial Amur honeysuckle removal) and unmanaged sites. These results suggest that the invasion potential of Amur honeysuckle is robust, and its distribution may not be constrained in riparian forests by the variables included in this study.

Committee: Christopher Barton Ph.D. (Advisor); David Peterman Ph.D. (Committee Member); Ryan McEwan Ph.D. (Committee Member) Subjects: Biology; Earth; Ecology; Environmental Science

Doctor of Philosophy, University of Akron, 2015, Civil Engineering

To reduce the occurrence of motor-vehicle crashes, professionals in education, enforcement, and engineering are continually tasked with implementing safety solutions. Identifying locations of high rates of crashes allows safety solutions to more adequately target their intended audience. This research examines advances in identifying hot spots of motor-vehicle crashes. These advancements come from improving: 1) the calculation of spatial autocorrelation and interpolation, 2) the identification of spatio-temporal patterns, and 3) the influence of geographical patterns on the spatial distribution of crashes. Overall, by improving the hot spot analysis, concerned professionals may be better prepared and lower the number of alcohol-related crashes. The location of hot spots is important in the implementation of enforcement campaigns. A lapse in accuracy may allow a vehicle operator suspected of disobeying traffic laws from being properly disciplined. Improvements in the calculation of spatial autocorrelation and interpolation result from the use of network distances instead of Euclidean based distances. Network based distances increase the accuracy of resulting hot spots. With the accuracy of hot spots improved, the optimal times to implement safety campaigns in their identified areas become important. Many hot spots purely analyze crashes as if they all occurred at the same time. By investigating crashes in this manner, some key influences may be lost and the efficiency of the implemented campaign may be reduced. Spatio-temporal hot spot are examined and show that as time progresses, clusters of crashes occur and disappear throughout space. By moving campaign sites as the location of crashes move, the overall efficiency of campaign tactics would benefit. Hot spots of crashes have continually been scrutinized for their focus on areas of large populations. In an effort to rectify this belief, the normalization of hot spot is examined in relation to population dens (open full item for complete abstract)

Committee: William Schneider IV Dr. (Advisor); Stephen Duirk Dr. (Committee Member); Anil Patnaik Dr. (Committee Member); Scott Sawyer Dr. (Committee Member); Mark Fridline Dr. (Committee Member) Subjects: Civil Engineering; Transportation

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

This study investigates the effects of the surrounding environment on crashes, with a focus on crash severity and at-fault drivers characterized by gender and age. Crashes where a vehicle is the guilty party are investigated. The study adopts two approaches: aggregate and disaggregate. In the aggregate approach, the numbers of crashes, classified in terms of severity (fatalities, injuries, property damages only), and gender and age of the driver (with several age groups covering the 15-100 age span), represent the variables to be investigated, and have been derived for the Central Ohio Region from the multiple files of the crash database of the Ohio Department of Public Safety, over the period 2006-2011. These data are aggregated at the level of Traffic Analysis Zones (TAZ). OLS models are first estimated, but spatial autocorrelation tests point the existence of spatial autocorrelation (SA). Spatial econometrics models are then used to eliminate the SA bias: the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). Subsequent analyses are conducted using the SEM estimates, as the SEM model is successful in completely eliminating spatial autocorrelation. The aggregate approach uses a large set of explanatory variables classified into six groups: Regional and Locational, Socio-Economic, Land-Use, Public Transit and Traffic Flow, Circulation and Network, and Physical Characteristics. The results show that variables in all these groups have significant impacts on crash severity and frequencies. The disaggregate approach accounts for more variables that influence crash severity, but cannot be captured in the aggregate approach, such as weather conditions, light conditions, road conditions, type of intersection, and type of vehicle. All these variables are directly related to an individual crash. The logit model is used to explain the probability of a Bodily Injury (BI) crash at the crash scene, where the alternative is Property Damage Only (PDO) crash. (open full item for complete abstract)

Committee: Jean-Michel Guldmann (Advisor); Burkhard von Rabenau (Advisor); Philip Viton (Committee Member) Subjects: Behavioral Sciences; Land Use Planning; Transportation; Transportation Planning; Urban Planning

Master of Science (MS), Bowling Green State University, 2013, Geology

The Late Pleistocene-Early Holocene of Ohio was a period of dynamic changes in climate, flora, and fauna. Climate and flora studies have been greatly aided by palynology research in Ohio's prevalent peat deposits but faunal dynamics, particularly for large (≥ 44 kg) mammals, are less certain. Available Pleistocene-aged fossils are limited and existing databases are largely incomplete. This study adds to the available data through the creation of the Ohio Pleistocene Mammal Database (OPMDB). The database is composed of fossil finds within Ohio of probable Pleistocene age that have been collected from historic sources, i.e., period newspapers, science journals, etc., and compiled into a geographically referenced database. Within this thesis, I describe the scope and breadth of the OPMDB and present preliminary taphonomic and geospatial analyses using the OPMDB. Initial results are consistent with those previously described in the scientific literature, supporting the view that historical reports can be reliable sources for information about fossil mammal occurrences. Clear differences in spatial distribution and preservational potential exist among Late Pleistocene-Early Holocene mammals. Analysis of the distributions of preserved species and individual skeletal elements by sedimentary context revealed that the greatest variety of taxa is preserved in peats. Mastodons dominate Ohio fossil localities, making up 56% of occurrences in the OPMDB, with sites spread throughout the state. Mastodons are found in a variety of depositional contexts, from peats and clays to gravels. In contrast, mammoths are relatively rare in peats and clays, and none are known from the lake plain of northwest Ohio. The peccary record is notably rich, with many complete skeletons; peccaries are most likely to be found in fluvial sands and silts. Other ungulates, including equids, cervids, and bovids, are most often represented in the OPMDB by isolated cranial material (teeth, horns/antlers, (open full item for complete abstract)

Committee: Margaret Yacobucci Dr (Advisor); Peter Gorsevski Dr (Committee Member); Jeff Snyder Dr (Committee Member) Subjects: Animals; Archaeology; Geographic Information Science; Geology; Paleoecology; Paleontology; Zoology

Doctor of Philosophy, The Ohio State University, 2007, Agricultural, Environmental and Development Economics

The main objective of this research is to reveal the effects of water quality on housing values around Lake Erie. Both the first and the second stage of hedonic price analysis are conducted with identified housing submarkets by using Hierarchical Clustering with quantized similarity measures in the region including Erie, Lorain, Ottawa and Sandusky Counties located along Lake Erie. We use both individual houses and census block groups as the smallest building blocks of the clusters and compare the clustering and hedonic results for both cases. Fecal coliform counts and secchi disk depth readings measuring water clarity are used as water quality variables. In order to overcome the spatio-temporal aspects of secchi depth disk reading data, kriging is used for spatial prediction. Robust Lagrange Multiplier test indicates that spatial error models are appropriate for the estimation of hedonic price functions in each submarket. We found that secchi disk depth readings variables are positive significantly influencing housing prices in most of the clusters while mixed results are found for fecal coliform counts. Demand functions with different functional forms are estimated with two-stage least squares with submarket dummy variables. While computed welfare changes for fecal coliform by using non-linear demand functions are very small, the benefit of the improvement of water clarity by 25 centimeters to be estimated 230 dollars per household. We found that the welfare changes are larger for the degradation of water quality compared to the improvements of water quality in the same amount. We further analyzed the welfare changes by using demand functions derived specifically for each household. Welfare changes based on the individual demand functions were computed by integrating under each demand curve for multiple scenarios. If we consider our SIG Fecal data represents 33 percent of entire population in four counties, the total estimated net benefit was derived as 51,934,180 (open full item for complete abstract)

Committee: Timothy Haab (Advisor) Subjects: Economics, General

Doctor of Philosophy (PhD), Ohio University, 2008, Biological Sciences (Arts and Sciences)

This study examined the role of spatial distribution of land cover on water quality and stream fish assemblages in watersheds of low-order streams in the Mississippi coastal plain. I found that the growing proliferation of urbanized land into landscapes with dominant rural or forest character decreases water quality and diversity of aquatic biota. A reconstruction of local land use history identified the contemporary landscape mosaic as legacy of the Southern Lumber Boom and management decisions based on individual land-ownership. Such decisions transformed firedominated longleaf pine savanna into a landscape characterized by active fire suppression and second-growth Southern mixed deciduous forest, non-industrial pine silviculture, and an expanding urban core. Commercial development is concentrated in floodplains and along major transportation routes, while diffuse parcel-size residential development across the study area increases fragmentation of the forest-dominated landscape matrix.Contemporary land cover distribution was evaluated using a new hybrid classification method combining panchromatic aerial photographs, highresolution multispectral remote sensing data, and Landsat5 TM images. A spatially explicit modeling approach using GIS quantified watershed land cover based on distance to streams and relative upstream distance from sampling sites. Water chemistry, stream geomorphology and fish assemblage metrics identified direct and indirect linkages between land cover, landscape features, and stream ecology. In the Mississippi coastal plain land cover influence exceeded geomorphological effects on stream conditions. Fish assemblages varied among sites in composition and diversity, and differed between watersheds with contrasting dominant land cover, suggesting integration of watershed-scale and local-scale influences. Fish assemblage metrics identified species richness, assemblage dominance, trophic guild membership, and perturbation tolerance as best descrip (open full item for complete abstract)

Committee: Glenn R. Matlack PhD (Committee Chair); Kelly Johnson PhD (Committee Member); James Lein PhD (Committee Member); Molly Morris PhD (Committee Member); Philip Cantino PhD (Committee Member) Subjects: Ecology; Geography

Doctor of Philosophy, Miami University, 2024, Gerontology

The COVID-19 pandemic has exposed vulnerabilities within the healthcare system, with nursing homes in under-resourced communities particularly impacted. Numerous studies have linked community socioeconomic status to both care quality and COVID-19 outcomes in these settings, suggesting that nursing homes in disadvantaged neighborhoods provide lower quality care, resulting in poorer health outcomes for residents. However, whether high-quality nursing homes can effectively reduce these health disparities remains unclear. Additionally, conventional long-term care research often overlooks spatial relationships, which can introduce bias into findings. Understanding these dynamics is essential for building resilience against future health emergencies. This study explores the relationships among neighborhood disadvantage, care quality, and COVID-19 outcomes in Ohio nursing homes, examining the potential influence of spatial relationships. While nursing homes in more disadvantaged areas generally exhibit lower quality performance, these quality metrics do not show significant associations with neighborhood socioeconomic status after adjusting for organizational factors and resident characteristics. Instead, quality performance is associated with factors like financial health (e.g., Medicaid payor mix, occupancy rates), stable in-house staffing, consistent leadership, and resident demographics. The study also reveals consistently high mortality risks among residents in nursing homes located in disadvantaged neighborhoods, which are unaffected by quality performance indicators. While COVID-19 incidence rates among residents and staff show no significant associations with neighborhood socioeconomic status, higher CMS Five-Star Staffing ratings and resident satisfaction scores significantly reduce resident COVID-19 incidence rates over the two-year pandemic period. Conversely, higher CMS Five-Star Overall, Health Inspection, and Staffing ratings are linked to increased sta (open full item for complete abstract)

Committee: J. Scott Brown (Committee Chair); Robert Applebaum (Committee Member); Jing Zhang (Committee Member); Saruna Ghimire (Committee Member); Suzanne Kunkel (Committee Member) Subjects: Gerontology

Doctor of Philosophy, The Ohio State University, 2024, Public Health

Background: Otitis media (OM), a prevalent middle ear inflammation, is a leading cause of medical consultations and antibiotic prescriptions among children globally, imposing significant financial burdens on healthcare systems. Untreated acute OM can progress to chronic suppurative OM, a primary cause of pediatric hearing loss and potential cognitive delays, affecting speech acquisition. Environmental factors such as air pollution and socioeconomic conditions contribute to OM's multifactorial etiology, yet research on these associations, especially in low- and middle-income countries (LMICs), remains limited. The impact of preventive measures such as pneumococcal conjugate vaccines (PCVs), known to reduce AOM incidence and subsequent hearing loss, on cognitive development requires further investigation. This dissertation addresses these gaps through an aggregated analysis integrating spatial data, examining OM prevalence, hearing loss, and cognitive development among children in Bohol, Philippines, while identifying demographic, environmental, and socioeconomic correlates. Methods: Utilizing data from a follow-up assessment of a randomized controlled trial (ISRCTN 62323832) evaluating an 11-valent PCV in Bohol, this dissertation integrated demographic, socioeconomic, and health data collected via parental questionnaires, ear assessments, and cognitive tests. This study redefined neighborhoods using a square grid methodology to standardize geographic units. Then I aggregated participant data at the neighborhood level and created maps to visualize the spatial distribution of each outcome. I assessed global spatial autocorrelation using Moran's I index, supplemented by Local Indicators of Spatial Association (LISA) maps to pinpoint local clusters. The three specific aims were: (1) to identify factors associated with OM prevalence using multivariable ordinary least squares (OLS) and spatial regression models; and to assess spatial heterogeneity in associations using ge (open full item for complete abstract)

Committee: Maria Gallo (Advisor); Bridget Freisthler (Committee Member); Jeffrey Wing (Committee Member); Sarah Anderson (Committee Member) Subjects: Epidemiology; Public Health

Bachelor of Arts (BA), Ohio University, 2024, Anthropology

In this thesis I examine Stoney Hill Rockshelter, a prehistoric site near Lancaster, Ohio. The site was occupied during the Paleoindian, Adena, and Late Woodland/Fort Ancient periods. I used lithic analysis to understand the temporal and spatial distribution of artifacts. The site was most often utilized as a generalized foraging location. It is unique for its fluted point component.

Committee: Joseph Gingerich (Advisor) Subjects: Archaeology

PhD, University of Cincinnati, 2023, Design, Architecture, Art and Planning: Architecture

Over time childbirth practices evolve, and at the present time giving birth in hospital, rather than at home or in midwife led clinics, is the preferred option. While this shift has been beneficial for high-risk pregnancies, women with low-risk pregnancies frequently report low satisfaction levels. Research in Western contexts has revealed that spatial design impacts human behavior and social interaction during childbirth. However, there is a lack of research on how the physical environment affects the childbirth experiences of women in the Middle East. Thus, there is a need to provide data regarding the Middle East, as it has a unique culture. This study analyzes the impact of the birth environment, excluding sensory factors, on childbirth experiences in Saudi Arabia, integrating spatial analyses, women's perceptions, and cultural considerations. This study utilized a Case Study-Mixed Methods Research (CS-MMR) design, incorporating both quantitative and qualitative methods (Quan + qual). The Quan aspect employed a Space Syntax (SS) analysis to examine the arrangements on Labor and Birth Wards (LBWs) at three hospitals. A satisfaction questionnaire was also administered to women who had given birth at these hospitals, requesting that they rate their experiences on a Likert scale. The qual aspect involved in-depth interviews with 31 Saudi women who had recently given birth at one of the three hospitals. The study identified noticeable connections between average spatial values and women's satisfaction levels. For instance, Labor/Birth Rooms (LBRs) with higher mean integration values, indicating lower privacy, resulted in lower satisfaction levels overall (Tau (3) = -1, p=.0001), as well as with the physical room (Tau (3) = -1, p=.0001). Conversely, LBRs with lower mean step depth, indicated by more staff and entries, resulted in higher satisfaction with the services (Tau (3) = -1, p=.0001) but had a negative association with privacy. Women's satisfacti (open full item for complete abstract)

Committee: Pravin Bhiwapurkar Ph.D. (Committee Chair); Joori Suh Ph.D. (Committee Member); Ann Black M.A. (Committee Member); Danielle Bessett Ph.D. (Committee Member) Subjects: Architecture

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

Doctor of Philosophy, The Ohio State University, 2022, Evolution, Ecology and Organismal Biology

Desert plant communities throughout the arid Southwest are being impacted by a rapidly changing climate. In the Mojave and Sonoran Deserts, severe drought, linked to global climate change, is causing widespread mortality of long-lived species. Biotic interactions, both competitive and facilitative, mediate plant responses to stressful conditions. Consequently, the spatial pattern of plants on the landscape, which determines the intensity of interactions between individuals, is a legacy of past conditions, a moderator of present drought mortality, and a driver of future community change. To better understand how interactions between adjacent individuals affects the rates of growth, survival, and mortality of desert shrubs in a changing climate, in Chapter One I investigated the spatial demography of the numerically dominant species, Ambrosia dumosa (Asteraceae), using a size and neighbor-classified matrix model parameterized with twenty years of data from a permanent one-hectare site in Joshua Tree National Park that spanned periods of historically average climate and extreme drought. I classified 9,215 Ambrosia individuals into six size classes and two neighbor states. Differences in the demography of isolated and neighbored population subsets of this species shifted with drought, illustrating how spatial pattern mediates the impact of climate change. High interannual and intra-annual variability in rainfall challenges desert shrub seedlings with a tradeoff between drought tolerance and competitive ability. I hypothesized that Ambrosia seedlings can acclimate to wetter or drier conditions by modifying their proportion of roots and leaves, based on early-life moisture cues. In Chapter Two I performed a greenhouse experiment to investigate how root/shoot allocation of Ambrosia was affected by variation in the timing of water availability. Seedlings received the same total quantity of water, differing only in the timing of water delivery. Seedlings lacking wa (open full item for complete abstract)

Committee: Maria Miriti (Advisor); Stephen Hovick (Committee Member); G. Matthew Davies (Committee Member); Elizabeth Marschall (Committee Member) Subjects: Biostatistics; Conservation; Demography; Ecology

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

Master of Science (MS), Ohio University, 2021, Geography (Arts and Sciences)

Despite considerable progress made over recent decades in scientific understanding of the structure, evolution, and dynamics of supercell thunderstorms and tornadoes, there are still aspects of their evolution, including tornadogenesis and decay, that have yet to be understood. One area in which scientific understanding is particularly incomplete is how land surface heterogeneity or complex topography interacts with and/or affects tornadoes. Past investigations embarked on the endeavor to quantify the relationship between tornado events with land cover or elevation, with inconclusive or conflicting results. The purpose of this study is to identify the genesis and decay points of tornadoes over an eighteen-year timeframe of 2000-2017 within the state of Arkansas and a ten-year timeframe of 2008-2017 within the State of Oklahoma to determine whether elevation and/or surface roughness of the nearby land cover relative to the broader landscape might impact these events. This study was completed utilizing Storm Prediction Center tornado reports, National Land Cover Data, and USGS digital elevation models (DEMs), with ArcGIS Pro and Microsoft Excel as the modes of data visualization and analysis. A combination of buffer analyses, Mann - Whitney U tests and Getis-Ord Gi* hotspot analyses were incorporated to assess the relationship between genesis/dissipation locations and surface roughness and complex terrain. The qualitative and quantitative results herein suggest a local increase in surface roughness heterogeneity favors genesis events, while regions defined by complex terrain may act to dissipate tornado events. Furthermore, hotspot analyses suggest regions with complex terrain and increased roughness values favor tornado activity.

Committee: Jana Houser (Advisor); Gaurav Sinha (Committee Member); Ryan Fogt (Committee Member) Subjects: Atmospheric Sciences; Geographic Information Science; Geography; Meteorology

MS, University of Cincinnati, 2021, Engineering and Applied Science: Civil Engineering

Energy burden, the proportion of household income spent on utilities, has become a topic of interest for researchers, policy makers, and nonprofit organizations as these groups recognize the importance of energy justice and the fair distribution of energy related resources. However, energy burden is a complex phenomenon that is driven by a variety of intersecting socioeconomic, demographic, and physical factors which may also vary spatially across different geographies. This key spatial component is oftentimes not addressed, resulting in an incomplete picture of how energy burden functions within a given scale. Additionally, energy burden is usually analyzed using modeled data, which may not accurately reflect the actual level of energy consumed. The purpose of this study is to examine energy burden through metered utility data and socioeconomic/physical characteristic variables at the census block group level to determine which drivers of energy burden are most impactful and understand how they vary spatially at the citywide scale. Cincinnati is used as a case study through which bivariate analysis, ordinary least squares regression, and multiscale geographically weighted regression are applied to better understand the correlational and spatial determinants of energy burden. The results show that not only do multivariate spatial models using both socioeconomic and physical predictor variables perform better than non-spatial models, but they also highlight that economic predictors are the most impactful drivers of energy burden.

Committee: Amanda Webb Ph.D. (Committee Chair); Hazem Elzarka Ph.D. (Committee Member); Leah Hollstein Ph.D. (Committee Member) Subjects: Civil Engineering

Master of Science, University of Toledo, 2020, Civil Engineering

Rising pedestrian and bicycle crashes in the United States (US) due to poor infrastructure have created an unsafe environment for non-motorized users. They are numerous reasons like high vehicular traffic, under-funding, insufficient bicycle infrastructure, pollution, and zoning laws which have resulted in low bicycle users in the US as compared to their European counterparts. In the last decade or so, a lot of study is being done to understand these crashes and find proper solution to overcome it. This study uses four spatial statistical methods to analyze bicycle crashes that resulted in injuries and fatalities across Ohio at three spatial scale: county, census tract and Traffic Analysis Zone (TAZ). Among the four methods, it was found that Local Indicators of Spatial Analysis (LISA) gave the most convincing results as compared to others. Results from the current analysis indicated cities or counties that are highly prone to bicycle crashes and therefore require immediate mitigation measures. These cities include Toledo, Cleveland, Akron, Columbus, and Cincinnati as these some of the big cities in the state and are home to big universities. Based on the analysis and findings, the author have suggested some counter measures that can be implemented to reduce the chances of such crashes, specifically in the metropolitan cities and future research recommendations.

Committee: Bhuiyan Alam (Advisor) Subjects: Civil Engineering