Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
Browse
19 results
Search Results
Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.Item 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.Item EXAMINING THE ENVIRONMENTAL, ECONOMIC, SOCIETAL, AND SUSTAINABILITY POTENTIAL OF SHARED MICROMOBILITY USAGE IN THE U.S.(2021) Younes, Hannah Nicole; Baiocchi, Giovanni; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Transportation became the leading sector of carbon dioxide emissions in the United States in 2017 according to the Environmental Protection Agency (EPA). The urgency of reducing emissions from the transportation sector was manifested in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report. Moreover, inequality in income and access to resources continues to increase. From an equity and societal standpoint, transportation modes should be affordable, accessible, and convenient. Developments in technology, communication, and mobile computing have shown great potential in managing resources and increasing efficiency. Innovative research is needed to find ways to reduce such emissions. The following dissertation research focuses on a subset of shared mobility called shared micromobility which include station-based bikeshare (SBBS) and dockless e-scooter and bicycle share (DSS & DBS). The first study establishes a relationship between shared micromobility and public transportation. During three planned transit disruptions, close to 1000 additional bikeshare rides were taken. This finding shows promise that a shift to active, low-carbon mobility is possible. The second study focuses on the temporal determinants and environmental impacts of micromobility. Scooter users tend to be less sensitive to whether conditions than bike users, making scooters more competitive with public transit and auto travel. Moreover, scooter users were more sensitive to gasoline price increases, suggesting a potential shift in auto users in favor of micromobility. The third study examines the access of micromobility in six U.S. cities. In cities with well-established micromobility, higher proportions of minorities and higher poverty rates were associated with fewer trips. The implications for societal equity for this low-carbon mobility are discussed. While micromobility is sustainable and has the potential to compete with more established modes of transportation, like public transit and auto travel, there still remain inequities in access among underserved communities to be addressed.Item UNDERSTANDING GEOSPATIAL DYNAMICS OF PARASITE MIGRATION AND HUMAN MOBILITY AS FACTORS CONTRIBUTING TO MALARIA TRANSMISSION IN THE GREATER MEKONG SUBREGION(2021) Li, Yao; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Much effort has been made to control malaria over the past decades in South-East Asia Confirmed cases of Plasmodium falciparum (P.f.) and Plasmodium vivax (P.v.) malaria were reduced by 46%, and mortality by 60%. However, malaria remains a major problem in the Greater Mekong Subregion (GMS) with the emerging resistance to the artemisinins and their partner drugs. This raises concerns that the usefulness of first-line malaria treatments may be diminishing in the GMS, and that drug resistance could spread worldwide. Estimating malaria parasite migration patterns is crucial for malaria elimination as well as understanding the role that human mobility plays in malaria transmission. This dissertation will focus on the GMS, especially Cambodia and Myanmar which have been widely regarded as the epicenter of emerging resistance to artemisinin-based combination therapies. This dissertation is structured as three separate studies that look first at the movement of malaria parasites across a region, and then two studies that focus on human movement and how these movements can lead to increased exposure as well as transmission of malaria. In the first study, a semi-automatic workflow was developed to select the optimal number of demes that will maximize model accuracy and minimize computing time when computing estimated effective migration surfaces. A validation analysis showed that the optimized grids displayed both high model accuracy and reduced processing time compared to grid densities selected in an unguided manner. In the second study, an agent-based simulation model was built to estimate and simulate the daily movements of local populations in Singu and Ann Townships in Myanmar in order to identify how two townships in different parts of Myanmar compared with respect to mobility and P.v. and P.f. positivity. The third study examined mobility patterns of local village populations in Singu Township, Myanmar when they traveled longer distances outside of Singu, and discuss these patterns of regional travel in the context of daily mobility within the township.Item ADDRESSING GEOGRAPHICAL CHALLENGES IN THE BIG DATA ERA UTILIZING CLOUD COMPUTING(2020) Lan, Hai; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Processing, mining and analyzing big data adds significant value towards solving previously unverified research questions or improving our ability to understand problems in geographical sciences. This dissertation contributes to developing a solution that supports researchers who may not otherwise have access to traditional high-performance computing resources so they benefit from the “big data” era, and implement big geographical research in ways that have not been previously possible. Using approaches from the fields of geographic information science, remote sensing and computer science, this dissertation addresses three major challenges in big geographical research: 1) how to exploit cloud computing to implement a universal scalable solution to classify multi-sourced remotely sensed imagery datasets with high efficiency; 2) how to overcome the missing data issue in land use land cover studies with a high-performance framework on the cloud through the use of available auxiliary datasets; and 3) the design considerations underlying a universal massive scale voxel geographical simulation model to implement complex geographical systems simulation using a three dimensional spatial perspective. This dissertation implements an in-memory distributed remotely sensed imagery classification framework on the cloud using both unsupervised and supervised classifiers, and classifies remotely sensed imagery datasets of the Suez Canal area, Egypt and Inner Mongolia, China under different cloud environments. This dissertation also implements and tests a cloud-based gap filling model with eleven auxiliary datasets in biophysical and social-economics in Inner Mongolia, China. This research also extends a voxel-based Cellular Automata model using graph theory and develops this model as a massive scale voxel geographical simulation framework to simulate dynamic processes, such as air pollution particles dispersal on cloud.