PHD, Kent State University, 2024, College of Arts and Sciences / Department of Geography

The U.S. military-industrial complex is among the most notorious phenomena in the field of international political economy. As a key source of American power and a driver of technological change, it has attracted generations of critique and fascination across academic and popular cultures. Yet the geographical dimensions of military-industrial innovation economies remain heavily undertheorized. In this dissertation, a large-scale study asks whether and how the domestic geography of defense spending – specifically, investment in different industrial contexts – affects the overall pace of technological change. I find that while military spending is linked to higher rates of innovation in less-developed areas, it appears to dampen innovation in more advanced locales by crowding out more efficient types of spending. This result is consistent with Anderson's (1972) notion of the “broken symmetries” that lie at the heart of complex systems theory and suggests that a better understanding of the military-industrial complex would coincide with improvements in our ability to model such systems. Motivated by current limits to complex systems modelling, the dissertation goes on to provide a prototype of a GIS that uses the principles of quantum information theory to expand our dynamic spatial modelling toolkit. An application of this prototypical quantum-centric GIS to the military-industrial complex system reveals some of the key political-economic dynamics underlying the spatial distribution of spending. Namely, mid-term Congressional election cycles that alternately channel defense dollars to more urban or more rural local economies. Together with the results of the first study, this finding points to an overlooked role of the military-industrial complex in U.S. national security: its potentially equilibrating effect on domestic spatial disparities that might otherwise threaten the viability of the state.

Committee: David Kaplan Ph.D. (Committee Chair); Timothy Assal Ph.D. (Committee Member); Michael Ensley Ph.D. (Committee Member); Daniel Hawes Ph.D. (Committee Member); Jay Lee Ph.D. (Committee Member) Subjects: Geographic Information Science; Geography

Master of Arts, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Science, The Ohio State University, 2005, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Arts, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Arts, The Ohio State University, 2007, Graduate School

Committee: Not Provided (Other) Subjects:

Master of Arts, Miami University, 2024, Geography

Rapid urbanization and the Urban Heat Island (UHI) effect are key global challenges, particularly in the Global South. Dhaka City, Bangladesh, one of the fastest growing and most densely populated cities in this region, facing increasing heat trends which threaten its public health, social and environmental conditions. Mitigating future UHI requires a geospatial analysis of urbanization and temperature trends at the neighborhood-scale. However, proprietary geospatial data and applications are often prohibitively costly for planners. This study combines cost-effective, publicly-accessible geospatial applications and time series satellite data from 2003 - 2023 to analyze land use and land cover (LULC) and land surface temperature (LST) changes in Dhaka City at regional and neighborhood-scales. For monitoring LULC, Landsat images were analyzed through supervised classification at a regional scale, and spectral mixture analysis (SMA) helped understand complex urban development patterns at the neighborhood-scale. This study analyzed MODIS daily LST images to understand diurnal temperature trends and found a strong positive correlation between urban development intensity and increased day and nighttime temperatures, contributing to neighborhood-specific UHI impacts. The study emphasizes the importance of developing publicly-accessible and inexpensive geospatial methods to support UHI mitigation planning that can benefit other similar cities.

Committee: David Prytherch Dr. (Advisor); Jessica McCarty Dr. (Committee Member); Robbyn Abbitt Ms. (Committee Member); John Maingi Dr. (Committee Member) Subjects: Climate Change; Environmental Studies; Geographic Information Science; Geography; Remote Sensing; Urban Planning

MDES, University of Cincinnati, 2022, Design, Architecture, Art and Planning: Design

The proper development of an electric vehicle charging infrastructure will become paramount to how quickly the United States transitions from Internal Combustion Engine (ICE) vehicles to Electric Vehicles (EVs). The research on Next Generation Charging Infrastructure highlights both a methodological and theoretical approach to that infrastructure problem, which in years to come will only become more acute as the percentage of registered EVs starts to grow. With Geographic Information Systems (GIS), this research identifies sites within the City of Cincinnati, that can be further developed into future charging points. This initial methodology can be applied to other cities that meet the same or better qualifications and become the backbone for designing a template that exists in a much larger framework for the development of EV infrastructure. The second methodology in this work highlights primary research on user preferences that was conducted through surveys. The primary research is used to develop user values and design principles, and identify the greatest user needs. While this thesis focuses on the City of Cincinnati as a case study, the combined research and methodologies can help guide the infrastructure architecture in any city or region.

Committee: David Edelman Ph.D. (Committee Member); Yong-Gyun Ghim M.Des. M.S. (Committee Member) Subjects: Design

PHD, Kent State University, 2021, College of Arts and Sciences / Department of Geography

Cancer epidemiology has a long history of applying geographic thinking to address long-standing place-based disparities. This dissertation adds new knowledge to geospatial approaches to social determinants of cancer outcomes. It establishes a framework consisting of three dimensions in evaluating, identifying, and prioritizing spatially heterogeneous risk factors of cancer outcomes. The first dimension is protection. Using a space-time statistic, the first study evaluated whether a non-spatial healthcare policy, Medicaid expansion, has offered protection targeting spatially vulnerable populations against adverse cancer outcomes such as breast cancer late-stage diagnosis. The second dimension is phenotype. Using a classification and regression tree, the study disentangled how risk factors of late-stage breast cancer diagnosis were conceptualized and capsulized as phenotypes that labeled groups of homogenous geographic areas. It provides a novel angle to uncover cancer disparities and to provide insights for cancer surveillance, prevention, and control. The third dimension is priority. Using a geographic random forest along with several validation methods, the study emphasized the importance of the competing effect among risk factors of cancer mortality that are specific to geographic areas. The findings from this study can be used directly for priority settings in addressing the most urgent issues associated with cancer mortality. This dissertation demonstrated that geographic methodologies and frameworks are useful and are imperative to cancer epidemiology.

Committee: Jay Lee (Committee Chair); Jun Li (Committee Member); James Tyner (Committee Member); Xinyue Ye (Committee Member) Subjects: Epidemiology; Geographic Information Science; Geography; Health; Health Care; Health Care Management; Oncology; Public Health; Public Policy; Statistics

Master of Science in Engineering, Youngstown State University, 2021, Department of Mechanical, Industrial and Manufacturing Engineering

The World Health Organization declared COVID-19 a global pandemic in March 2020. Many countries and economies were greatly affected, including the United States of America. Many people were greatly affected causing them to go into critical care resulting in some eventual fatalities. Some of the factors that could have led to the widespread of infections can be attributed to the socio-demographic determinants, including gender, race/ethnicity, income, urban-rural location, access to healthcare and age. This study is aimed at exploring and examining patterns of COVID-19 infections by considering age, gender, health insurance coverage, race/ethnicity and income factors. Data from the Center for Disease Control (CDC), Department of Health and Human Services (HSS), the COVID tracking Project, and the U.S. Census Bureau (USCB) were used in this study. A Bayesian Conditional Autoregressive (CAR) model was used to explore the association between COVID-19 infection rates, hospitalizations and deaths, and socio-demographic variables using Open BUGS for the states of Ohio, Michigan and Kentucky. At the beginning of March 2020, the number of COVID-19 cases reported by the CDC for the USA was 123,498 infections.

Committee: Nazanin Naderi PhD (Advisor); Peter Kimosop PhD (Committee Member); Hojjat Mehri PhD (Committee Member) Subjects: Geographic Information Science; Health Care; Industrial Engineering; Social Research; Statistics

Ph.D., Antioch University, 2020, Leadership and Change

The former United Nations Secretary-General Ban Ki-Moon (2014), repeated the core promise in the 1986 UN Declaration on the Right to Development, in which the General Assembly called for an approach guaranteeing meaningful participation of everyone in development and the fair distribution of the benefits of that development. To this end, partnerships are central and can lead to the dignity of the citizens involved as they participate in the development of their own communities. This dissertation research conducted in Manyatta A and B in the Port City of Kisumu, Kenya sought to do just that. The purpose of this study is to demonstrate the role of participatory development planning and collaborative technology platforms of geographic information systems (GIS) and GeoDesign in strengthening sustainable development and enhancing of human dignity. The study used a multimethod design comprised of participatory action research, situational analysis, problem tree analysis, and stakeholder analysis approaches in partnership with the government, academia, business, civil society, and other stakeholders. The study shows how the newly formed government structure, post devolution, provides a functional framework to assist county and city governments to better determine and envision the future they want. This vision can be realized more rapidly through integrated planning to achieve poverty eradication and social, economic, and environmental sustainability, which are the three pillars of the 2030 Agenda for Sustainable Development. The citizens of informal settlements represent those who are farthest behind and who should be given priority. This study demonstrated the potential of inclusive and participatory development planning in restoring the dignity of those groups. This dissertation is available in open access at AURA: Antioch University Repository and Archive, http://aura.antioch.edu/, and OhioLINK ETD Center, https://etd.ohiolink.edu

Committee: S. Aqeel Tirmizi Ph.D. (Committee Chair); Elizabeth Holloway Ph.D. (Committee Member); Amor Laaribi Ph.D. (Committee Member) Subjects: Area Planning and Development; Environmental Management; Environmental Philosophy; Geographic Information Science; Geography; Information Systems; Information Technology; International Relations; Land Use Planning; Landscape Architecture; Political Science; Sanitation; Sub Saharan Africa Studies; Transportation Planning; Urban Planning

Doctor of Philosophy, University of Toledo, 2019, Spatially Integrated Social Science

The goal of this study was to identify areas that were prone to nutrient transport from land application of manure based on environmental conditions including length of streams and flood hazard potential in those areas. Additionally, the study aimed at developing an economic utility for producers in transporting manure in the Maumee Watershed in North-west Ohio targeted at reducing the potential environmental impacts that may arise from over application. The initial basic feasible solution of the Hitchcock transportation model was used to simulate the distribution of manure from 31 dairy and swine concentrated animal feeding facilities to agricultural census block groups (soybeans and corn) in the Maumee Watershed within NW Ohio. The model considered the supply and demand capacity of nearby livestock operations (origin) and agricultural census block groups (destinations) respectively. The second objective was to identify areas that were prone to nutrient transport as determined from the model results based on environmental conditions related to floodplain and length of streams dataset using the Getis-Ord GI* statistic. Finally, using the objective function of the transportation problem, the transportation costs associated with hauling manure from the source to the destinations was calculated. The distribution of manure showed an unbalanced transportation problem such that available farmland that could receive manure exceeded the supply of the livestock operations. The findings suggest there is adequate agricultural land for manure distribution in the watershed. Additionally, areas indicating clustering in distribution of manure were further examined to determine potential for nutrient transport off the land and into nearby waterbodies based on the environmental conditions used. Approximately 98% of receiving agricultural census block groups fell in the EC-1 classification, which indicates a very low potential for environmental conditions to influence nutrient (open full item for complete abstract)

Committee: Patrick Lawrence (Advisor); Peter Lindquist (Committee Member); Kevin Czajkowski (Committee Member); Kenneth Kilbert (Committee Member); Daryl Dwyer (Committee Member) Subjects: Geography

Doctor of Philosophy, University of Toledo, 2019, Civil Engineering

The availability of adequate clean water in urban areas is rapidly decreasing due to population growth, land modification, and changing weather patterns. Solutions for poor urban water quality have included the implementation of green stormwater infrastructure (GSI) such as rain gardens. The observed benefits from GSI vary due to site-specific conditions including native soils, climate, and quality of construction. It is critically important to select optimal sites for GSI installation, where the desired benefits are achievable. This can be accomplished through water quality modeling coupled with GSI performance modeling prior to installation. In this research, hydrologic/ water quality simulation was used to generate hotspot maps to identify the parts of a watershed that are likely to produce the highest contaminant loads (e.g., suspended solids). The creation of the water quality models requires significant site and contaminant data including imperviousness, native soil type, land use, elevation, infrastructure dimensions, buildup and washoff properties, and local rain data. In this study, hotspot maps were used to visualize problem areas and to target those areas for implementing GSI, which resulted in simulations producing reduced runoff pollution (total suspended solids load). Green stormwater infrastructure is static and unable to adapt to changing conditions. Incorporating active control allows manipulation of a feature (e.g. system inlet or outlet), which could result in improved performance. It was determined in this research that controlling the outlet of a rain garden (RG) (i.e. closing outlet underdrain valve) can be beneficial because it generally causes increased exfiltration and decreased contaminant release to nearby waterways. Exfiltration volume increased for larger RGs (exfiltration increased as area ratio increased). No benefit was observed for larger storms in undersized systems (area ratio < 0.50); as most of the stormwater was lost due to (open full item for complete abstract)

Committee: Gruden Cyndee (Advisor); Kumar Ashok (Committee Member); Pniewski Michael (Committee Member); Becker Richard (Committee Member); Xu Yanqing (Committee Member) Subjects: Civil Engineering

Master of Science (MS), Ohio University, 2019, Environmental Studies (Voinovich)

Coal mining activities can result in a variety of environmental issues and, worldwide, one of the greatest threats from coal mining is acid mine drainage (AMD). In the eastern U.S. coal bearing regions, AMD is a wide spread environmental impairment to waterways, especially from abandoned or closed underground coal mines. Pollutional discharge can result from flooding of underground mines, or mine pools, resulting in reactions that create AMD and discharge to surface water. Research has focused on improving reclamation and treatment methods for AMD to address ongoing pollution problems, but there is a need for more reliable prediction methods for use in continued permitting of lands for coal mining. Under the Surface Mining Control and Reclamation Act (SMCRA), coal companies are required to estimate the post-mining water levels to determine if a mine pool will form and if there may be a pollutional discharge, but there is a lack of a science-based method for determining the hydrologic response to mining. This thesis sought to address the gap in prediction by expanding previously explored parameters of mine pool formation in post-SMCRA mines through expanding previous multivariate statistical analyses. Analyses were done in both the Unscrambler X and Neuroshell. An algorithm produced in Neuroshell, an artificial neural network program, resulted in the least amount of error and was incorporated into a tool for modeling post-mining potentiometric head elevation through ArcGIS Pro model building function. The predictive tool developed in ArcGIS Pro was made to output points of predicted post-mining water levels. The tool only requires input of data that would be required for an underground mine permit application. This work has continued the work of an ongoing project to provide mine companies and regulators with a predictive ArcGIS tool that determines if a mine pool will form and discharge to the surface. This project's final output is an empirically predictive Arc (open full item for complete abstract)

Committee: Natalie Kruse (Committee Chair); Dina Lopez (Committee Member); Gaurav Sinha (Committee Member); Daniel Che (Committee Member) Subjects: Environmental Engineering; Environmental Geology; Environmental Management; Environmental Science; Environmental Studies; Geographic Information Science; Geography; Geological; Geology; Hydrologic Sciences; Hydrology; Information Science; Mining; Natural Resource Management; Water Resource Management

Master of Science in Biological Sciences, Youngstown State University, 2018, Department of Biological Sciences and Chemistry

Land use and cover patterns, such as forest vs. farmed lands (which in Northeast Ohio include both technological modern farms and traditional Amish properties), can greatly influence ecological functioning at multiple scales. Too often, alterations in land use have been made with little or no consideration of potential impacts on adjacent systems, including streams. The objective of this thesis was to evaluate influences of land cover on habitat for fish and other aquatic vertebrates within tributaries of the Grand River in Ashtabula, Trumbull, and Geauga Counties. I used Geographic Information System tools to delineate watersheds of 8 tributaries, and to determine percentages of forested, wetland, and farmland in each. I used a combination of land parcel search by common Amish surnames, in addition to ground trothing to differentiate Amish vs non-Amish properties. I conducted Qualitative Habitat Evaluation Indices (QHEI) at publicly assessable points on each stream, and also calculated stream gradient above assessment sites. I used Pearson Correlation and Principle Components Analysis (PCA) to investigate associations among land cover, stream gradient, and habitat quality variables. Watersheds closest to the Grand River were predominantly forest and wetlands. There was a distinct spatial separation between Amish and non-Amish farms, with Amish farms concentrated in uplands to the west of the Grand River Valley near the village of Middlefield. QHEI scores ranged from 47 (poor/fair) to 80 (excellent). In-stream factors such as sediment heterogeneity and riffle-pool development contributed the most to high QHEI scores. High Gradient streams also scored the highest in habitat quality. PCA also revealed these patterns in land cover, interestingly suggested that land cover was not strongly influencing stream habitat quality. Habitat assessment sites located substantial distances from farms, so perhaps the natural land cover in between may be sufficiently buffering impact (open full item for complete abstract)

Committee: Thomas Diggins PhD (Advisor); Ian Renne PhD (Committee Member); Peter Kimosop PhD (Committee Member); Bradley Shellito PhD (Committee Member) Subjects: Aquatic Sciences; Biology; Environmental Science; Geographic Information Science

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

The Earth stands on the precipice of the sixth mass extinction. This extinction risk has triggered a growing crisis and urgent need to save the world's biodiversity. Considering the accelerated rates of biodiversity loss and extinction, we need simple but efficient methods to quickly identify threatened areas. This dissertation research was undertaken with this in mind—to benefit the conservation community, either through the delivery of biogeographic methods or information to further the restoration or maintenance of biodiversity. As a primary goal, this dissertation endeavored to fill those research gaps and offer some simpler and more effective useful and usable geospatial techniques for biodiversity conservation analyses. Secondary goals of the research were (1) to contribute to specific conservation programs for critically endangered species, (2) to inform about the status of habitat, and (3) to address top conservation research priorities. While not a specific objective, the research outcomes may influence public policy. This three-article dissertation introduces two novel techniques: (1) development of a habitat suitability model in ArcGIS using kernel density estimation and a mortality-risk weighting factor on road density, the delimiting variable; and (2) a rapid hybrid change detection technique using ENVI's SPEAR Vegetation Delineation tool or classifying live green vegetation and ArcGIS to compare and quantify changes in time. For the latter, two studies incorporated the change detection technique. The pilot study performed the change detection with color-infrared aerial photography, while the follow-up investigation tested the feasibility of the method to handle high resolution multi-sensor data, given the difficulty obtaining data from the same or similar sensors. These studies represent the first of their kind. This dissertation research provides widely applicable, practical, and employable geospatial models to perform habitat assessment (open full item for complete abstract)

Committee: Tak Yung Tong Ph.D. (Committee Chair); Richard Beck Ph.D. (Committee Member); Theresa Culley Ph.D. (Committee Member); Nicholas Dunning Ph.D. (Committee Member); Hongxing Liu Ph.D. (Committee Member) Subjects: Geography

Doctor of Philosophy, The Ohio State University, 2017, Anthropology

Medieval Europe was highly-stratified with wealthy elites and clergy supported by a large and economically-challenged peasantry. Historical evidence suggests that there were significant differences between various segments of the medieval population, particularly relating to socioeconomic status and sex. Additionally, evidence suggests that burial locations in and around churches had different degrees of significance and that these locations related to the social hierarchy (e.g., burial locations near areas of religious significance, like the altar, are more prestigious). While historical texts outline how lifestyles and diet would vary between different segments of the population, data from skeletons are rarely incorporated into those discussions. This bioarchaeological project examines the health and diet of individuals buried within two medieval cemeteries in Italy to examine mortuary behavior and how it reflects societal inequality. These two cemeteries, the site of Pieve di Pava (Siena) and Trino Vercellese (Vercelli), represent rural Italian parish cemeteries that date from approximately the 8th to 13th centuries. This project combines skeletal, stable isotopic, and geospatial analyses to study patterns of hierarchical burial behavior in these medieval cemeteries to examine the impacts of societal inequality on individuals during life and after death in the Middle Ages. Skeletal markers on these individuals provide a record of lived events, such as stress, health, and workload, to better understand the lived experiences of these medieval Italians. Stable isotopic analysis of carbon and nitrogen examines the chemical composition of bone to elucidate the medieval diet and identify differential access to resources. Finally, geospatial analyses place the individual skeletons, and their associated skeletal and isotopic data, into a spatial context so that burial patterns and how these indicators of health and diet are distributed spatially across this medieval c (open full item for complete abstract)

Committee: Clark Larsen Ph.D. (Advisor); Sam Stout Ph.D. (Advisor); Julie Field Ph.D. (Committee Member); Giuseppe Vercellotti Ph.D. (Committee Member) Subjects: Archaeology; Medieval History; Physical Anthropology

Doctor of Philosophy, The Ohio State University, 2001, Graduate School

Committee: Not Provided (Other) Subjects: Geography

Master of Science in Environmental Science, Youngstown State University, 2015, Department of Physics, Astronomy, Geology and Environmental Sciences

Northeastern Ohio bats are important in maintaining insect populations, thus minimizing crop damage. However, because bat populations are decreasing due to factors such as habitat loss and White Nose Syndrome, attention should be placed on evaluating habitat requirements to maximize conservation efforts. Understanding anthropogenic disturbances such as ecological light pollution, as an indicator of bat habitat quality, has been historically neglected. Studies have shown that the presence of light pollution can alter the activity of several animals, including bats (Frank 1988; Svensson and Rydell 1998; Yurk and Trites 2000; Rydell 2006; Santos et al. 2010). To better understand the effects of light pollution on bat activity, this study examined frequency of bat activity along a light gradient at a field site in Portage County, Ohio in the summer of 2014. Spatial modeling of bat pass frequency suggested greatest activity in regions of medium light intensities, versus high and low light intensities. A preliminary species identification study indicated the presence of hoary, eastern red, and big brown bats, all of which tend to prefer lit environments and thus are likely foraging in the lit areas at the field site. As activity was lowest in the brightest areas, a threshold might exist beyond which light-tolerant bats at this site will avoid. Light-avoiding species present in Portage County and neighboring Summit and Trumbull Counties may be deterred by the light gradient at the field site. This study suggests that artificial light may be a variable of concern for bat populations at this location and that the effects of light should be studied further to better understand how different bat species react to light. Moreover, bat species composition should be determined at the field site in order to promote holistic bat habitat management plans.

Committee: Dawna Cerney Ph.D. (Advisor); Colleen McLean Ph.D. (Committee Member); Peter Kimosop Ph.D. (Committee Member); Emariana Widner (Committee Member) Subjects: Environmental Science

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

This report summarizes my activities as Planning Intern for the Miami Valley Regional Planning Commission (MVRPC) from May 2014 through May 2015. The following report is organized by a history of Metropolitan Planning Organizations, MVRPC, and common aspects involved in transportation planning. This is followed by the role that MVRPC plays in environmental planning, transportation safety planning and the work that I contributed to the programs in that area. Major work that I contributed includes a preliminary analysis of traffic safety data in the Miami Valley Region for the years 2011 to 2013. Finally, I discuss how MVRPC may enhance safety planning as well as how the Institute for the Environment and Sustainability (IES) program at Miami University has impacted my education and career goals.

Committee: Amélie Davis (Advisor); Mary Henry (Committee Member); Steven Elliot (Committee Member) Subjects: Environmental Science; Geographic Information Science; Transportation Planning

Doctor of Philosophy, University of Toledo, 2014, Geography

Optimizing the supply chain has been increasingly important for the success of both manufacturers and retailers. This optimization has reduced costs for companies that are involved in the transport process and reduced cost to the end consumers, which brings benefits to both sides of the profit chain. Under difficult economic conditions, such as high fuel prices, mass congestion on major highway corridors, and strict reliability requirements for specific commodity types, the tangible as well as the intangible costs of freight transportation has been increasing rapidly. It swallows profits from the industry, increasing the cost for customers. Intermodal freight transportation has been introduced in recent years and has been more frequently selected by logistics companies, third-party logistics companies, and manufacturers to optimize the whole freight transport process. The trend of using more intermodal freight transportation is discussed in a qualitative perspective in the first part of this dissertation. Then the dissertation introduces a new shortest path algorithm entitled Tree Spanning Method (TSM) for large network processing. The new TSM algorithm is used as the main route planning algorithm throughout the dissertation for software development as well as freight demand forecasting. Due to the lack of specialized intermodal freight planning software in the industry, this dissertation discusses the key techniques in creating a specialized freight planning software, and a beta version of the software entitled "RouteInfo" is developed and introduced. The software works in a fashion similar to the Spatial Decision Support System. It enables users to be fully involved in the decision-making process and is able to determine the least expensive path between origins and designations on an integrated intermodal network. Real data includes USA highways and Canadian highways, and rail and maritime networks are integrated into the graphical user interface. Freight (open full item for complete abstract)

Committee: Peter Lindquist Ph.D. (Committee Chair); Daniel Hammel Ph.D. (Committee Member); Eddie Yein Juin Chou P.E. (Committee Member); Neil Reid Ph.D. (Committee Member); Yue Zhang Ph.D. (Committee Member) Subjects: Geographic Information Science; Transportation; Transportation Planning