Doctor of Philosophy, The Ohio State University, 2010, City and Regional Planning

Criteria air pollutants, such as nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), particulate matter (PM), and ozone (O3), are characterized by temporal and locational hot spots in urban areas, frequently violating pollution standards, and, as a result, threatening the health and well-being of the population. Several factors, such as the intensity and duration of emissions, the chemical reactions among pollutants, the uptake and assimilation of pollutants by urban vegetation, and the meteorological factors that induce chemical reactions and atmospheric dispersion, have been considered as explanatory variables in air quality models. Among them, emissions from motor vehicles turn out to be a key determinant of the spatial and temporal patterns of ambient pollution concentrations. The purpose of this research is to formulate and estimate (1) metropolitan-wide time-series air quality models and (2) land-use regression (LUR) air quality panel models, in order to explain spatio-temporal variations in pollution concentrations. Using the Seoul Metropolitan Area as a case study, traffic counts, vehicle-kilometers-traveled (VKT), land uses, and meteorological factors, such as solar radiation, temperature, humidity, wind speed and wind direction, are used as explanatory variables. An extensive understanding of atmospheric pollutants chemistry is reflected in the formulation of these models. Differences in concentrations measured at air quality monitoring stations (AQMs) across the week (weekdays vs. Sunday) and geographical locations (roadside vs. background), are also investigated using dummy variables and the product of these variables with the original variables. The results of the time-series models and panel regression models indicate that traffic counts and VKT are significant in explaining the concentrations of both directly emitted pollutants, such as NO2, CO, SO2, and PM, and O3, a secondary pollutant. The concentrations of the directly emitted poll (open full item for complete abstract)

Committee: Jean-Michel Guldmann PhD (Committee Chair); Steven Gordon PhD (Committee Member); Philip Viton PhD (Committee Member); Gulsah Akar PhD (Committee Member) Subjects: Urban Planning

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

Master of Science, The Ohio State University, 2018, Civil Engineering

As urbanization increases in cities prone to earthquakes, increasing disaster resilience, or the ability to absorb shock of the disaster, is increasingly important to preserve the integrity of critical infrastructure and save human lives. This study explores the response and resilience of Haiti following the 2010 M7.0 earthquake. Traditional methods of measuring resilience following a major earthquake require census data. Census data is seldom available at a great level of detail. As an alternative to census data, satellite imagery provides an objective measurement of the history of the earth, consistent both in temporal and spatial resolution. The currently available Very High Resolution (VHR) remote sensing sensors observe objects on the ground as small as 0.3 meters. The additional dimensions of volume and shape of the buildings provide the ability to distinguish building functions when compared to the traditional two-dimensional data. From the land cover and land use classification results for each year, a time series analysis analyzes the changes through the years of the individual buildings and building types. Using the building type classification results, the changes in resilience indicators are analyzed by year. Elasticity, amplitude, and malleability are the three indicators used to measure resilience. Elasticity refers to the recovery duration of the city to a stable state after the earthquake; Amplitude refers to the changes in the built-up area caused by the earthquake, essentially how much the city is impacted by the earthquake; finally, malleability refers to the city's new development after the earthquake, compared to its original state. The results are compared to census data to illustrate the correlations between the observed dynamics and the given data, as well as to draw conclusion about the recovery processes. Using satellite images to characterize the resilience of a built-up area is feasible, and change detection analysis can be used to (open full item for complete abstract)

Committee: Rongjun Qin (Advisor); Desheng Liu (Advisor); Alper Yilmaz (Committee Member) Subjects: Civil Engineering

Master of Science, The Ohio State University, 2018, Environmental Science

Climate mitigation strategies to combat global warming recognize the roles of terrestrial ecosystems in sequestering carbon and lowering atmospheric CO2 levels. Reducing forest loss and improving forest management have been widely acknowledged as effective measures to improve climate benefits. However, forestry activities and land use changes, such as deforestation, reforestation and afforestation, will not only change biological carbon uptake but alter land biophysical processes concerning evapotranspiration, surface albedo, land surface roughness, land-air interactions, all of which affect climate. Forestry activities, though enhancing carbon sequestration, may have negative climatic consequences and further accelerate global warming. Understanding climate response to forestry activities and their biogeochemical and biophysical mechanisms is important in formulating policies to optimize climate benefits of forestry or land management activities. This work seeks to quantify the biophysical forcing and climatic impacts of land use and land cover changes across the entire globe through the combined use of remotely sensed observations and climate model simulations. The emphasis is on albedo (i.e., how much sunlight is reflect back) and radiative forcing (i.e., how much more energy is kept or lost in the climate system for a given land change). The focus is on four vegetation types, evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), grasslands (GRA), and croplands (CRO). Surface albedo was compared among the four vegetation types. Radiative forcingwas calculated for potential land conversions from GRA or CRO to ENF or DBF. The shadow price of carbon and albedo was estimated using DICE model as a first-order approximation of economic benefits or losses associated with biophysical climate regulation of land management activities. Forests generally have lower albedo than adjacent grasslands or croplands, particularly where snow is frequent. Conse (open full item for complete abstract)

Committee: Kaiguang Zhao (Advisor); Karen Mancl (Committee Member); Brent Sohngen (Committee Member) Subjects: Environmental Science

Doctor of Philosophy, University of Toledo, 2017, Spatially Integrated Social Science

One of the fundamental but critical barriers of brownfields redevelopment (BR) is the lack of information on the brownfield locations. Planners, policy-makers and developers need to know this information before they can create effective policies and legislation for redevelopment. However, relatively few studies have been conducted on creating effective methods to identify brownfield sites and to provide decision support for BR. Brownfileds' information is still collected with traditional methods using a combination of tax record information, site visits, and other records, which is typically a time and labor consuming process. This study used the City of Toledo as a case study to explore an efficient method to identify brownfields automatically as a part of the brownfields inventory and then determine the best use for that parcel. Based on object-based classification, this study made a map of potential brownfields using LiDAR imagery and Color-Infrared (CIR) imagery using the eCognition software. Then, a GIS-based land use suitability analysis model (LSAM) for green space was created using the brownfield layer in addition to proximity to residences, air/noise pollution, high land surface temperature to explore which brownfields could be converted into green spaces in Toledo.

Committee: Kevin Czajkowski Ph.D (Committee Chair); Wentworth B. Clapham Ph.D (Committee Member); Daniel J. Hammel Ph.D (Committee Member); Sujata Shetty Ph.D (Committee Member); Sumei Zhang Ph.D (Committee Member) Subjects: Environmental Management; Geographic Information Science; Remote Sensing; Urban Planning

Doctor of Philosophy, The Ohio State University, 2017, City and Regional Planning

Research on travel behavior continues to be one of the most prominent areas in the transportation area. Planners and policymakers try to understand and manage travel behavior. Making and implementation of travel demand management (TDM) policies greatly rely on the understanding of the determinants of activity-travel patterns and travel demand. Among the activity-travel patterns, trip chaining and joint travel have received much research interest. Trip chaining is typically viewed as a home-based tour that connects multiple out-of-home activities. Joint travel is commonly defined as traveling with others. Travel demand is generally measured by trip generation and travel distances. Investigating different aspects of travel behavior helps us better understand the links between activity participation and mobility, and improves the evaluation of the transportation infrastructure investments and policies such as high occupancy vehicle (HOV) lanes and vehicle miles traveled (VMT) reduction programs. Several studies have regarded trip chaining, joint travel, trip generation, and travel distances as different dimensions of travel behavior to be examined in terms of various socio-demographics and urban form factors. However, limited work has been done to use activity-travel patterns as mediating variables and analyze how trip chaining and joint travel shape the resulting travel demand. Furthermore, relationships between travel behavior and urban form factors at out-of-home activity locations remain unclear. Based on the 2012 travel survey data from the Cleveland Metropolitan Area, this study first investigates the relationships among trip chaining, joint travel, home-based tour generation, and travel distances at three different levels: tour, individual, and household levels. Second, the influences of socio-demographics and urban form factors at tour origins and destinations on travel behavior are examined simultaneously. Lastly, while using trip chaining and joint travel a (open full item for complete abstract)

Committee: Gulsah Akar (Advisor); Zhenhua Chen (Committee Member); Jean-Michel Guldmann (Committee Member) Subjects: Land Use Planning; Transportation; Transportation Planning

PhD, University of Cincinnati, 2016, Medicine: Biostatistics (Environmental Health)

Particulate matter (PM) has long been known to have a negative effect on public health. Epidemiological studies associating air pollution and other sources of PM often rely on land use modeling for exposure assessment. This approach relies on the association of characteristics of the surrounding land with PM concentrations. Land use regression (LUR) is the most commonly implemented land use model and has several drawbacks, including model instability due to correlated predictors and an inability to capture non-linear relationships and complex interactions. Here, I utilize the machine learning random forest model within a land use framework to generate a novel land use random forest (LURF) model. Using ambient air sampling data from the Cincinnati Childhood Allergy and Air Pollution (CCAAPS) study, I developed LURF and LUR models for eleven elemental components of particulate matter, including Al, Cu, Fe, K, Mn, Ni, Pb, S, Si, V, Zn. We show that LURF models utilized a higher number and more diverse selection of land use predictors than the LUR models. Furthermore, the LURF models were more accurate and precise predictors of all elemental PM concentrations, except for Fe, Mn, and Ni. To extend the usability of the LURF models, I utilized the recent application of the infinitesimal jackknife (IJ) to the random forest model in order to estimate the prediction variance. The IJ theorems were originally verified under the assumptions of traditional random forest framework, namely using CART trees and bootstrap resampling. Alternatives to the traditional random forest, such as subsampling instead of bootstrap resampling and conditional inference trees instead of CART trees have been shown to increase the accuracy of the random forest algorithm and eliminate its variable selection bias. Here, I conduct simulation experiments to show that the IJ performs well when using these random forest variations. Specifically, using the conditional inference tree instead of the C (open full item for complete abstract)

Committee: Patrick Ryan Ph.D. (Committee Chair); Roman A. Jandarov PH.D. (Committee Member); Marepalli Rao Ph.D. (Committee Member) Subjects: Biostatistics

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

Master of Environmental Science, Miami University, 2015, Environmental Sciences

Bees provide a crucial ecosystem service in crop pollination. Maintaining healthy managed and wild bee populations is critical to continue this important service that also affects many crops and thus the food we eat. This study focuses on the ecosystem service provided by managed bees in the Prairie Pothole region of North Dakota, an area characterized by intensive agriculture and remnant grassland, wetlands, and prairies. A spatially explicit model was used to determine the suitability of the landscape for managed bees using floral resource quality estimates, foraging distances, and land use land cover data for 2008-2012. The model outputs are akin to a floral resource quality index for managed bees. This index was compared with honey production, which is being used as an indicator of managed bee health. The results show a strong positive relationship between honey production and the floral resource quality index in 2008, and a weaker positive relationship in 2009 and 2010. Results for 2012 were inconclusive. A linear mixed effect model was used to account for the sampling of the same locations over multiple years. The models were better than the null models and proved to be statistically significant, indicating the importance of land use land cover suitability for managed bees. The presumed factors contributing to the variability of the relationship between land use land cover and honey production between years are discussed. These include land use land cover changes, Colony Collapse Disorder, pesticide application, beekeeper intervention, and interannual weather variations.

Committee: Amélie Davis (Advisor); Sarah Dumyahn (Committee Member); Mary Henry (Committee Member) Subjects: Agriculture; Conservation; Entomology; Environmental Science; Environmental Studies; Geography; Land Use Planning

Master of Science, The Ohio State University, 2015, Environment and Natural Resources

Northern bobwhites (Colinus virginianus; hereafter, bobwhites) are gallinaceous gamebirds that were once ubiquitous across the eastern United States, but have declined throughout the 20th century. This research was conducted to evaluate the impact of woodlot edge-feathering and land use change on density and survival of bobwhites in Midwestern agricultural landscapes. I examined covey density, survival, and habitat use on 4 private-land study sites in southwestern Ohio to further understanding of winter ecology of bobwhites in relation to habitat characteristics and targeted woodlot edge management. Non-breeding season survival rate was at levels capable of stabilizing the population during the moderate winter of 2012 – 2013 (Ŝ = 0.393, 95 % CI = 0.215 - 0.596), but was well below the stabilizing rate during the severe winter of 2013 – 2014 (Ŝ = 0.075, 95 % CI = 0.037 - 0.145). I did not find a relationship between macro- and microhabitat characteristics and weekly survival outcomes during weeks with snow cover, although bobwhites restricted habitat use to areas of high woody stem density with increasing snow depths. This and persistently low survival rates in severe winters suggest that habitat conditions are homogenously poor and are not capable of stabilizing bobwhite populations, even at currently low densities. I examined the predicted probability of use and usable space by examining the proximity of focal habitat types. Loss of early-successional habitat features on the Fee, Thurner, and Wildcat study sites reduced their predicted probability of use and resulted in a net decline in usable space. Targeted successional management of woodlot edges (i.e. edge-feathering) on the Peach Orchard study site increased the predicted probability of use and the proportion of usable space. A reduction in the extent and mean predicted probability of use for herbaceous habitats on Fee indicate that loss of herbaceous habitat resulted in a coarser-grained (open full item for complete abstract)

Committee: Robert Gates (Advisor); Steven Matthews (Committee Member); Stanley Gehrt (Committee Member) Subjects: Ecology; Environmental Science; Forestry; Natural Resource Management; Wildlife Conservation; Wildlife Management; Zoology

PhD, University of Cincinnati, 2002, Arts and Sciences : Geography

Currently, relatively little information is known about the use of land management options as a tool to adapt to the water quality impacts of global climate change. The goal of this dissertation is to investigate the combined impacts of land-use and climate changes on water quality in the Little Miami River (LMR) watershed. This project uses current and future land-use development plans from the counties comprising the LMR watershed to form a future land-use scenario for the watershed. Climate change is simulated using results from two prominent Global Circulation Models to develop four hypothetical climate scenarios. These scenarios simulate "worst-case" scenarios which depict the warm-dry and warm-wet events which can affect the hydrological cycle. The hydrological impacts of these climate scenarios, along with those of the future land-use changes, are modeled using the Soil and Water Assessment Tool (SWAT). Through this methodology, individual and combined impacts of the land-use and climate changes on water quality can be examined. This results indicate that the changes in runoff, nutrient, and sediment loads under future climate changes are large enough to require a significant planning response. In addition, low-density residential developments can result in higher water quality than agricultural land, when both soil type and land-use type are taken into consideration. Modeling results indicate that the nutrient enrichment problem in the watershed is due to an overabundance of phosphorus; sedimentation is also a problem. When land-use changes are implemented in light of the impending climate change, both phosphorus loads and sediment loads can be drastically decreased. Therefore, the use of land management schemes can be a powerful, flexible, and adaptive tool to mitigate the adverse water quality impacts of global climate change.

Committee: Dr. Susanna T.Y. Tong (Advisor) Subjects: Geography

Doctor of Philosophy, The Ohio State University, 2011, Agricultural, Environmental and Developmental Economics

Currently, there is substantial concern over global warming, greenhouse gas emissions and their potential effects on society. Much research is concentrating on afforestation, reforestation and reducing deforestation and degradation to diminish atmospheric carbon emissions. My dissertation includes three essays that discuss land-use change in Ecuador, global leakage after implementing several carbon policies and the latest developments related to REDD (Reduced Emissions from Deforestation and Degradation). Firstly, I will present a land-use change model for the country of Ecuador where I will estimate the effect of different site characteristics on the percentage of land used for farming (temporary and permanent crops), pasture, and forestry. With the results of this model, I will simulate the effects of several carbon sequestration scenarios to obtain land supply functions and the marginal costs of carbon sequestration. The second essay utilizes a forestry and land use model to compare the implications of different types of carbon policies on global leakage caused by timber price adjustments to a baseline (no-policy scenario) also established by the model. Finally, I define and evaluate the factors that affect REDD costs with a specific focus on its sensitivity to changes in some of its key components.

Committee: Douglas Southgate (Advisor); Brent Sohngen (Committee Member); Elena Irwin (Committee Member) Subjects: Economics; Environmental Science; Forestry

Doctor of Philosophy, The Ohio State University, 2008, Geography

An urban growth model is conceptualized as a metropolitan change model consisting of multiple scales: global, regional and local. The baseline model operates in a free trade environment, in a space initially without consideration of the regulatory and redistributive forces of national and state governmental levels. Space in this study is abstracted as a metropolitan envelope, which is defined to start at the beginning of the twentieth century with the emergence of the New York, London and Tokyo metropolitan systems, and expanded one hundred years later into a system of four hundred major central cities and their associated commuter hinterlands. The expectation is that this system will continue to expand in the twenty-first century, as the primary engine of global economic diffusion and development. The purpose of this research is to model economic spatial interactions that generate investment flows that in turn convert into economic activity after the construction and placement of private and public infrastructure. The global model provides a set of allocated investment flows to regions, and the regional model provides employment and residential allocations to the local model, which displays land use changes. One major goal is to test the systems ability (or not) to achieve partial convergence of per capita incomes across the set of metropolitan spaces over multiple scales. For a variety of tested scenarios, temporal convergence and rank-size rule metrics can be evaluated at multiple spatial scales.

Committee: Morton O'Kelly (Advisor); Mei-Po Kwan (Committee Member); Darla Munroe (Committee Member) Subjects: Geography