College of Behavioral & Social Sciences

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..

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Subject: search.filters.subject.Geographic information science and geodesy

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Now showing 1 - 10 of 24
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    Efficient terrain analysis of point cloud datasets on a decomposition-based data representation
    (2024) Song, Yunting; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    With a modern focus on LiDAR (Light Detection and Ranging) technologies, which generate precise three-dimensional measurements from the Earth’s surface, the amount of spatial data in the form of massive point clouds has dramatically increased. This dissertation addresses the problem of direct terrain analysis using large LiDAR point clouds without interpolating them into gridded Digital Elevation Models (DEMs). Unlike gridded DEMs, Triangulated Irregular Networks (TINs) maintain full information of point clouds and can represent terrains with variable resolution. When using TINs to represent large terrains, the major challenges are the high storage and time costs. To address these, this dissertation introduces a family of decomposition-based data structures, named Terrain trees family, for encoding TINs. A Terrain tree employs a nested subdivision strategy, partitioning the domain of the triangle mesh into several leaf blocks. Each leaf block contains the minimum amount of information required for extracting all connectivity relations that are needed for TIN navigation and terrain analysis. A new library for terrain analysis, the Terrain trees library (TTL), is developed based on the Terrain trees. Performance evaluation of TTL shows that a Terrain tree can encode the same terrain with ~36% less storagethan the state-of-art, compact data structure while maintaining good computing performance in extracting connectivity relations. Despite the highly efficient data structure, managing large TINs on local machines remains challenging, particularly for complex analyses or simulations. Mesh simplification methods are commonly applied to reduce TIN sizes to enable downstream processing. However, these simplification methods can modify the topology of the underlying terrain in an uncontrolled manner, which affects the results of terrain analysis applications. To address this issue, a topology-aware mesh simplification method based on Terrain trees is proposed. A parallel version of this simplification method is also developed, which simplifies different leaf blocks at the same time using a shared-memory implementation. A leaf-locking strategy is employed to avoid conflicts among leaf blocks during parallel computing. TTL and the topology-aware mesh simplification methods on Terrain trees effectively lower the memory and time requirements for terrain analysis on TINs. This dissertation demonstrates the effectiveness of TIN-based models in real-world applications using sea ice topography as an example. Studying sea ice topography is crucial as it enhances our ability to monitor sea ice volume changes and comprehend sea ice processes. Besides, timely and precise assessments of sea ice dynamics are critical in the context of climate change and its impacts on polar regions. TIN-based surface models are employed to represent the sea ice surface, and methods are developed for extracting important sea ice topographic features, such as density, regions without measurements, roughness, and pressure ridge structures, from TINs.
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    VISUALIZATION, DATA QUALITY, AND SCALE IN COMPOSITE BATHYMETRIC DATA GENERALIZATION
    (2024) Dyer, Noel Matarazza; De Floriani, Leila; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Contemporary bathymetric data collection techniques are capable of collecting sub-meterresolution data to ensure full seafloor-bottom coverage for safe navigation as well as to support other various scientific uses of the data. Moreover, bathymetry data are becoming increasingly available. Datasets are compiled from these sources and used to update Electronic Navigational Charts (ENCs), the primary medium for visualizing the seafloor for navigation purposes, whose usage is mandatory on Safety Of Life At Sea (SOLAS) regulated vessels. However, these high resolution data must be generalized for products at scale, an active research area in automated cartography. Algorithms that can provide consistent results while reducing production time and costs are increasingly valuable to organizations operating in time-sensitive environments. This is particularly the case in digital nautical cartography, where updates to bathymetry and locations of dangers to navigation need to be disseminated as quickly as possible. Therefore, this dissertation covers the development of cartographic constraint-based generalization algorithms operating on both Digital Surface Model (DSM) and Digital Cartographic Model (DCM) representations of multi-source composite bathymetric data to produce navigationally-ready datasets for use at scale. Similarly, many coastal data analysis applications utilize unstructured meshes for representing terrains due to the adaptability, which allows for better conformity to the shoreline and bathymetry. Finer resolution along narrow geometric features, steep gradients, and submerged channels, and coarser resolution in other areas, reduces the size of the mesh while maintaining a comparable accuracy in subsequent processing. Generally, the mesh is constructed a priori for the given domain and elevations are interpolated to the nodes of the mesh from a predefined digital elevation model. These methods can also include refinement procedures to produce geometrically correct meshes for the application. Mesh simplification is a technique used in computer graphics to reduce the complexity of a mesh or surface model while preserving features such as shape, topology, and geometry. This technique can be used to mitigate issues related to processing performance by reducing the number of elements composing the mesh, thus increasing efficiency. The primary challenge is finding a balance between the level of generalization, preservation of specific characteristics relevant to the intended use of the mesh, and computational efficiency. Despite the potential usefulness of mesh simplification for reducing mesh size and complexity while retaining morphological details, there has been little investigation regarding the application of these techniques specifically to Bathymetric Surface Models (BSMs), where additional information such as vertical uncertainty can help guide the process. Toward this effort, this dissertation also introduces a set of experiments that were designed to explore the effects of BSM mesh simplification on a coastal ocean model forced by tides in New York Harbor. Candidate vertices for elimination are identified using a given local maximum distance between the original vertices of the mesh and the simplified surface. Vertex removal and re-triangulation operations are used to simplify the mesh and are paired with an optional maximum triangle area constraint, which prevents the creation of new triangles over a specified area. A tidal simulation is then performed across the domain of both the original (un-simplified) and simplified meshes, while comparing current velocities, velocity magnitudes, and water levels over time at twelve representative locations in the Harbor. It was demonstrated that the simplified mesh derived from using even the strictest parameters for the mesh simplification was able to reduce the overall mesh size by approximately 26.81%, which resulted in a 26.38% speed improvement percentage compared to the un-simplified mesh. Reduction of the overall mesh size was dependent on the parameters for simplification and the speed improvement percentage was relative to the number of resulting elements composing the simplified mesh.
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    Revisiting Chert Preferences and Lithic Supply Zones of Early Archaic Northwestern Ohio: A Least Cost Path Analysis
    (2024) Bell, Meagan; Palus, Matthew; Anthropology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Newly documented lithic data resulting from reconnaissance-level cultural resource management surveys recently conducted in Erie and Huron Counties, Ohio raised questions regarding northwestern Ohio’s existing model of Early Archaic chert preferences and lithic supply zones. An initial examination of the artifact assemblages revealed a scarcity of non-local Upper Mercer chert which opposed the current premise on Early Archaic chert utilization and population movements in Ohio. This study attempts to understand the implications of the scarcity of Upper Mercer chert in these northwestern Ohio Early Archaic artifact assemblages by synthesizing regional data and conducting Least Cost Path analyses with Geographic Information System software.
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    A SYNTHETIC APERTURE RADAR (SAR)-BASED GENERALIZED APPROACH FOR SUNFLOWER MAPPING AND AREA ESTIMATION
    (2023) KHAN, MOHAMMAD ABDUL QADIR; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The effectiveness of remote sensing-based supervised classification models in crop type mapping and area estimation is contingent upon the availability of sufficient and high-quality calibration or training data. The current challenge lies in the absence of field-level crop labels, impeding the advancement of training supervised classification models. To address the needs of operational crop monitoring there is a pressing demand for the development of generalized classification models applicable for various agricultural areas and across different years, even in the absence of calibration data. This dissertation aims to explore the potential of the C-band Sentinel-1 Synthetic Aperture Radar (SAR) capabilities for building generalized crop type models with a specific focus on identifying and monitoring sunflower crop in Eastern Europe. Globally, the sunflower ranks as the fourth most important oilseed crop and stands out as the most profitable and economically significant oilseed crop. It is extensively cultivated for the production of vegetable oil, biodiesel, and animal feed with Ukraine and Russia as the largest producer and exporter in the world. In the first step, this study explores the interaction of Sentinel-1 (S1) SAR signal with sunflower to identify and monitor phenological stages of sunflower. The analysis examines SAR backscattering coefficients and polarizations in Vertical-Horizontal (VH), Vertical-Vertical (VV) and VH/VV ratio, highlighting differences between ascending and descending orbits due to sunflower directional behavior caused by heliotropism. Based on the unique SAR-based signature of sunflower the study introduces a generalized model for sunflower identification and mapping which is applicable across time and space. It was observed that the model based on features acquired from S1-based descending orbits outperforms the one based on ascending orbit because of the sunflower’s directional behavior: user’s accuracy (UA) of 96%, producer’s accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). This model was generalized and validated for selected sites in Ukraine, France, Hungary, Russia and USA. When the model is generalized to other years and regions it yields an F-score of > 77% for all cases, with F-score being the highest (>91%) for Mykolaiv region in Ukraine. The generalized approach to map sunflower was applied to assess the impact of the Russian full-scale invasion of Ukraine on national sunflower planted areas. The sunflower planted areas and corresponding changes in 2021 and 2022 were estimated using a sample-based approach for area estimation. Sunflower area was estimated at 7.10±0.45 million hectares (Mha) in 2021 which was further reduced to 6.75±0.45 Mha in 2022 representing a 5% decrease. The findings suggest spatial shifts in sunflower cultivation after the Russian invasion from southern/south-eastern Ukraine under Russian controlled to south-central region under Ukrainian control. The first objective of this dissertation highlights the difference of ascending and descending S1 orbits for sunflower monitoring due to its directional behavior, an aspect not fully researched and documented previously. The implemented generalized approach based on sunflower phenology proves to be an accurate and space-time generalized classifier, reducing time, cost and resources for operational sunflower mapping for large areas. Also, the disparity between sample-based area estimates and SAR-based mapped areas caused due to speckle were substantially reduced emphasizing the role of S1/SAR in global food security monitoring.
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    Spatiotemporal Analysis of Vehicle Mobility Patterns using Machine Learning Approaches
    (2023) Zhu, Guimin; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Vehicle mobility is important to a diverse range of disciplines, e.g., geography, transportation, and public health. Machine Learning algorithms have been applied in geospatial analysis related to vehicle mobility and travel pattern research, which provided researchers with more flexibility and capabilities for complex mobility pattern analyses. This dissertation aims to explore how different Machine Learning models (e.g., regression and clustering) can be applied to enhance the interpretability of vehicle mobility patterns by conducting explanatory analyses on factors that may impact different mobility patterns (i.e., trip volume changes and travel times) over space and time (e.g., different stages of the COVID-19 Pandemic at regional and nationwide scales). In this dissertation, three studies were undertaken to investigate the spatiotemporal trends of vehicle trip changes and travel behaviors, using passively-collected mobile device data. The first study examined mobility patterns over different time periods during the summer 2020 when COVID-19 cases were spiking in Florida(locations with large numbers of vulnerable individuals) and analyzed a set of underlying drivers for mobility and how these factors changed over time using Machine Learning approaches. The second study investigated changing mobility patterns across the U.S. during 2021 when COVID-19 vaccinations were becoming available to understand whether changing vaccination rates led to a change in the rate of trips using Machine Learning clustering methods. The third study investigated reasons impacting travel times for two origin-destination pairs using a Machine Learning approach to better understand how different factors can affect travel times over different trip purposes and different trip lengths in Maryland. The contributions of this dissertation are that it provided new insights into how different types of mobility patterns evolved over space and time, especially during a major public health crisis, and the results are useful for policy and planning implications for local and regional officials, e.g., mobility restriction measurements, decision support for economic recovery, and public health strategies. The integration of diverse data sources (e.g., passively-collected mobility data and other mobility data from different public and private sources) and the utilization of multiple Machine Learning models enhanced the interpretability of vehicle mobility patterns.
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    CYCLING AROUND THE CLOCK: MODELING BIKE SHARE TRIPS AS HIGH-FREQUENCY SPATIAL INTERACTIONS
    (2023) Liu, Zheng; Oshan, Taylor; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Spatial interactions provide insights into urban mobility that reflects urban livability. A range of traditional and modern urban mobility models have been developed to analyze and model spatial interaction. The study of bike-sharing systems has emerged as a new area of research, offering expanded opportunities to understand the dynamics of spatial interaction processes. This dissertation proposes new methods and frameworks to model and understand the high-frequency changes in the spatial interaction of a bike share system. Three challenges related to the spatial and temporal dynamics of spatial interaction within a bike share system are discussed via three studies: 1) Predicting spatial interaction demand at new stations as part of system infrastructure expansion; 2) Understanding the dynamics of determinants in the context of the COVID-19 pandemic; and 3) Detecting events that lead to changes in the spatial interaction process of bike share trips from a model-based proxy. The first study proposes a hybrid strategy to predict 'cold start' trips by comparing flow interpolation and spatial interaction methods. The study reveals 'cold start' stations with different classifications based on their locations have different best model choices as a hybrid strategy for the research question. The second study demonstrates a disaggregated comparative framework to capture the dynamics of determinants in bike share trip generation before, during, and after the COVID-19 lockdown and to identify long-term bike share usage behavioral changes. The third study investigates an event detection approach combining martingale test and spatial interaction model with specification evaluation from simulated data and explorative examination from bike share datasets in New York City, Washington, DC, and San Francisco. Results from the study recognize events from exogenous factors that induced changes in spatial interactions which are critical for model evaluation and improvement toward more flexible models to high-frequency changes. The dissertation elaborated and expanded the spatial interaction model to more effectively meet the research demands for the novel transportation mode of bike-share cycling in the context of a high-frequency urban environment. Taken as a whole, this dissertation contributes to the field of transportation geography and geographic information science and contributes methods toward the creation of improved transport systems for more livable cities.
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    RECENT TIMBERING ACTIVITY AS A VARIABLE IN PREDICTIVE MODELING
    (2023) Plent, Samuel Gerard; Palus, Matthew M; Anthropology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This thesis explores how the presence of recent timbering activities affects the predictive power of predictive models in regard to precontact archaeological sites. Predictive models have been used to assess the likelihood of identifying cultural resources in a given area for decades. A county-wide predictive model has not been created for any county in the state of Georgia. This research applies what is known about predictive modeling to Henry County, Georgia and assesses its accuracy. It then seeks to test predictive power of another environmental variable in order to further refine the process of predicting the location of precontact archaeological sites. The thesis focuses its efforts in Henry County, Georgia, which has multiple instances of pine silviculture areas that have been surveyed for cultural resources after being harvested. Timber has been an important natural resource in Georgia since the nineteenth century. The management of forests for the timber industry began in 1875 with the establishment of the American Forestry Association. The timbering and replanting of these areas can occur as often as every 15 to 30 years. This process can disturb soils and buried resources. Elevation, soil, and hydrology data was collected from multiple public sources including the United State Geological Survey (USGS) and the United States Department of Agriculture Natural Resources Conservation Service (USDA, NRCS). Archaeological site and previous survey data was taken from the Georgia Natural Archaeological and Historic Resources Geographic Information Systems (GNAHRGIS). The environmental data was combined to create a set of predictive models for predicting the likelihood of an area to contain a precontact archaeological site using Geographic Information Systems (GIS). Each predictive model was tested for accuracy using previously collected archaeological data. The predictive model found to be most accurate was analyzed within multiple areas containing recent timbering activities that have been previously surveyed for cultural resources. It appears that the presence of recent timbering activities does not negatively affect the predictive power of a predictive model regarding precontact archaeological sites. This is demonstrated by showing that a predicative model for the entirety of Henry County, Georgia does not lose accuracy when applied to multiple areas that have been timbered prior to survey for cultural material. Predictive models can be powerful tools in the Cultural Resource Manager’s toolkit. However, many may be reticent to apply these tools to areas that have seen large-scale industrial ground disturbing activities. This thesis has demonstrated that predictive models can still be useful tools in areas recently affected by large-scale timbering activities. While systematic survey is still necessary, this can be helpful in matters of scoping, budgeting, and planning
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    Tracking the dynamics of the opioid crisis in the United States over space and time
    (2022) Xia, Zhiyue; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Millions of adolescents and adults in the United States suffer from drug problems such as substance use disorder, referring to clinical impairments including mental illnesses and disabilities caused by drugs. The Substance Abuse and Mental Health Services Administration reported the estimated number of illicit drug users increased to 59.3 million in 2020, or 21.4% of the U.S. population, which made drug misuse one of the most concerning public health issues. Opioids are a category of drugs that can be highly addictive, including heroin and synthetic drugs such as fentanyl. Centers for Disease Control and Prevention (CDC) indicated that about 74.8% of drug overdose deaths involved opioids in 2020. The opioid crisis has hit American cities hard, spreading across the U.S. beginning with the west coast, and then expanding to heavily impact the central, mid-Atlantic, and east coast of the U.S. as well as states in the southeast. In this dissertation, I work on three studies to track the dynamics of the opioid crisis in the U.S. over space and time from a geographic perspective using spatiotemporal data science methods including clustering analysis, time-series models and machine learning approaches. The first study focused on the geospatial patterns of illicit drug-related activities (e.g., possession, delivery, and manufacture of opioids) in a typical U.S. city (Chicago as a case study area). By analyzing more than 52,000 reported drug activities, I built a data-driven machine learning model for predicting opioid hot zones and identifying correlated built environment and sociodemographic factors that drove the opioid crisis in an urban setting. The second study of my dissertation is to analyze the opioid crisis in the context of the global pandemic of SARS-CoV-2 (COVID-19). In 2020, COVID-19 outbroke and affected hundreds of millions of people across the globe. The COVID-19 pandemic is also impacting the community of opioid misusers in the U.S. The major research objective of Study 2 is to understand how the opioid crisis is impacted by the COVID-19 pandemic and to find neighborhood characteristics and economic factors that have driven the variations before and during the pandemic. Study 3 focuses on analyzing the crisis risen by synthetic opioids (including fentanyl) that are more potent and dangerous than other drugs. This study analyzed the geographic patterns of synthetic opioids spreading across the U.S. between 2013 and 2020, a period when synthetic opioids rose to be a major risk factor for public health. The significance of this dissertation is that the three studies investigate the opioid crisis in the U.S. in a comprehensive manner and these studies can facilitate public health stakeholders with effective decision making on healthcare planning relating to drug problems. Tracking the dynamics of the opioid crisis by drug type, including modeling and predicting the geographic patterns of opioid misuse involving particular opioids (e.g, heroin and synthetic opioids), can provide an important basis for applying further treatment services and mitigation efforts, and also be useful for assessing current services and efforts.
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    THE SPATIAL ANALYSIS OF OPIOID-RELATED HEALTH OUTCOMES AND EXPOSURES IN THE UNITED STATES OPIOID OVERDOSE CRISIS
    (2022) Sauer, Jeffery Charles; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The United States continues to endure the Opioid Overdose Crisis. Yet the burden of the crisis is not experienced uniformly across the United States. The discipline of geography offers a framework and spatial analysis methodology that are direct ways to investigate placed-based differences in opioid-related outcomes, exposures, and proxy measures. This dissertation combines the contemporary frameworks of health geography and geographic information science to provide novel studies on both the geographic patterns in opioid-related health measures at different scales across the United States as well as the actual spatial analytic methods that provide evidence on the Opioid Overdose Crisis. Three main research objectives are addressed over the course of the dissertation: 1) Model the space-time risk of heroin-, methadone-, and cocaine-involved emergency department visits in the greater Baltimore metropolitan area from January 2016 to December 2019 at the Zip Code Tabulation Area-level; 2) Estimate the local and neighboring relationship between prescription opioid volume and treatment admissions involving a prescription opioid across the United States from 2006 to 2014 at the county-level; and 3) Investigate and provide a framework as to how geographic information science has been used to provide knowledge over the duration of the crisis from 1999 to 2021. The first study demonstrates how a recently proposed spatio-temporal Bayesian model can produce disease risk surfaces for opioid-related health outcomes in data constrained scenarios. The second study executes spatial lag of X models on a nationwide prescription opioid distribution dataset, allowing for estimates on the relationship between neighboring prescription opioid volume and nonfatal treatment admissions involving a prescription opioid at the county-level. The third and final study of the dissertation developed and implemented a scoping review methodology, ultimately analyzing the study design and geographical elements of 231 peer-reviewed publications using geographic information science on research questions related to the crisis. Examination of the geographical components of these studies reveals a lack of evidence available at sub-state scales and in the Midwest, north Rocky Mountains, and non-continental United States. Several important future research directions - such as geographic meta-analyses and geographical machine learning - are identified. Taken as a whole, the dissertation provides a contemporary geographical framework to understand the ongoing United States Opioid Overdose Crisis.
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    THE CLOISTERED INFRASTRUCTURE OF THE OHIO & ERIE CANAL: AN ANALYSIS AND INVENTORY OF THE CANAL WITH A THEORETICAL LANDSCAPE ARCHAEOLOGY AND HISTORICAL GEOGRAPHIC INFORMATION SYSTEMS APPROACH
    (2022) Waugh, Mason Richard; Palus, Matthew M; Anthropology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The period of the 1820s and 1830s experienced a burst of canal construction across Ohio. The Ohio & Erie Canal connected the Cuyahoga River to Akron, and thence southward to Portsmouth along the Ohio River. The opening of the canal allowed early settlers within Ohio to easily transport products, effectively lowering the costs of goods and increasing the profitability of businesses utilizing the thoroughfare. Towns near the canal flourished as commodities previously difficult to obtain were now brought from long distances. These improvements that the Ohio & Erie Canal brought, as well as the context and significance of the canal, have been thoroughly documented in historical literature. A few intact portions of the Ohio & Erie Canal are currently included on the National Register of Historic Places (NRHP) and listed on the Ohio State Historic Preservation Office (SHPO) online Geographic Information System (GIS) mapping system. Several Cultural Resource Management (CRM) compliance surveys have also identified and documented canal remnants. However, most portions of the canal are not inventoried or listed on the SHPO online GIS mapping system. Few components of the canal are listed on the NRHP and within Scioto County there are only two locks represented on the NRHP. The general location of the Ohio & Erie Canal is well documented on historical maps; however, the placement of stream crossings and ancillary components (culverts, weirs, bridges) are poorly understood or perhaps cloistered, communicating little to the outside world as they are currently known. A series of plat maps was recorded in the early 1900s by the Canal Commission of the State of Ohio. Plat maps of the Ohio & Erie Canal in Scioto County were obtained for this project and were provided by the Ohio History Connection (2022). No large-scale effort to my knowledge has been made to georeference the plat maps of the Ohio & Erie Canal and analyze archaeological potential using Historical GIS (hGIS), which uses historical documents such as plat maps to answer questions about the past or to inventory canal features based on their location. To address the lack of recorded ancillary structures on the southern descent of the Ohio & Erie Canal, a total of 35 separate portions of the canal plat maps were georeferenced to the modern landscape to identify archaeological potential, ancillary structure locations, and to support recommendations for new contributing resources to the NRHP-listed historic districts. Seven separate categories of ancillary canal components or features which could be extrapolated from the canal plat maps were assigned GPS coordinates. The seven categories consisted of aqueducts, buildings, bridges, culverts, inlets, locks, and waste weirs. These components represent 70 individual features correlating to what was indicated on the canal plat maps through stations 1770-2660 in Scioto County. The inventory of these features breaks down the Ohio & Erie Canal component types and lists coordinates to increase accessibility of the information for future researchers and planners. A cross comparison of the portions of the canal currently listed on the NRHP and the SHPO online GIS mapping system is also completed and contained in this thesis. With the previously inventoried canal components and the newly georeferenced portions of the canal analyzed, this thesis assists further studies in assessing archaeological potential along the canal. Lastly, a recommendation is made suggesting which ancillary components along the canal could be contributing elements to the discontinuous or incomplete NRHP listing. This thesis attempts to provide interested researchers a better understanding of the ancillary components of the canal and how these components should be evaluated for NRHP eligibility. The Ohio & Erie Canal was not simply a historical waterway providing transportation of commodities, but also an early historical engineering feat containing a culmination of various structures whose design was to maintain water levels and one of the first mass engineering attempts in Ohio to manage the landscape and communities around the canal. Culverts along the canal are not only important, but they are also necessary for understanding how the Ohio & Erie Canal operated, how it adapted to certain topographical challenges, and were essential to the functioning of the canal. Removing culverts along the canal would not have allowed the canal to function due to the necessity of proper water levels. The public dissemination of the georeferenced data included in this thesis is intended to be a lasting benefit to gongoozlers, historians, researchers, and planners alike. As such this data will be made available by allowing the georeferenced maps and associated layers available through ArcGIS Pro. The map package in ArcGIS Pro is available upon request by contacting the author of this thesis.