Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
Browse
4 results
Search Results
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 Parameterized and Machine Learning Methods for Estimating Evapotranspiration from Satellite Data(2019) Carter, Corinne Minette; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The studies in this dissertation present evaluation of and improvement to parametric and machine learning regression methods for estimating evapotranspiration from remote sensing. It includes three main parts. The first part is an assessment of parametric regression methods for obtaining evapotranspiration from vegetation index and other variables. It was found that including more variables tends to improve results, but the form of the regression formula does not make a large difference. Algorithm performance is not as good for wetland and agricultural sites as for other land cover types. Re-training of algorithms for those surface type results in some improvement. The second part consists of an evaluation of ten machine learning techniques for retrieval of evapotranspiration from surface radiation and several other variables. It is found that the best results are obtainable using all available input variables to train the bootstrap aggregation tree, random kernel, and two- and three- hidden layer neural network algorithms. Performance is again found to be weaker for wetland and agricultural surface types than for other surface types. However, separate training of the machine learning algorithms with data from those surface types does not significantly improve performance. The third part consists of further refinement to the machine learning algorithms and application of the bootstrap aggregation tree method to generate evapotranspiration maps of the continental United States for 2012. It is found that separating snow and non-snow data points improves performance. Performance for all tested algorithms was similar against the validation data set, but best for the bootstrap aggregation tree using an independent test data set. Monthly mean maps of the continental United States are generated for the drought year 2012 using the bootstrap aggregation tree. Evapotranspiration levels are lower than those shown in comparison data sets for the growing season in the eastern United States, resulting from a low bias at high evapotranspiration values. Retraining with the training data set weighted towards higher evapotranspiration values reduces this discrepancy but does not eliminate it. It is clear that machine learning evapotranspiration algorithm results have a significant dependence on training data set composition.Item Local Information Landscapes: Theory, Measures, and Evidence(2019) Lee, Myeong; Butler, Brian S; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors by taking a socio-technical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that constantly shape people’s access to information. When it comes to local community settings where local information is embedded in diverse material entities such as urban places and technical infrastructures, the effect of information landscapes should be taken into account in addition to particular strategies for solving information-seeking issues. However, characterizing the information landscape of a local community at the community level is a non-trivial problem due to diverse contexts, users, and their interactions with each other. One way to conceptualize local information landscapes in a way that copes with the complexity of the interplay between information, contexts, and human factors is to focus on the materiality of information. By focusing on the material aspects of information, it becomes possible to understand how local information is provided to social entities and infrastructures and how it exists, forming structures at the community level. Through an extensive literature review, this paper develops a theory of local information landscapes (LIL Theory) to better conceptualize the community-level, material structure of local information. Specifically, the LIL theory adapts a concept of the virtual as an ontological view of the interplay between technical infrastructures, spaces, and people as a basis for assessing and explaining community-level structures of local information. By complementing existing theories such as information worlds and information grounds, this work provides a new perspective on how information deserts manifest as a material pre-condition of information inequality. Using this framework, an empirical study was conducted to examine the explicit effects of information deserts on other community characteristics. Specifically, the study aims to provide an initial assessment of LIL theory by examining how the fragmentation of local information, a form of information deserts, is related to important community characteristics such as socio-economic inequality, deprivation, and community engagement. Building upon previous work in sociology and political science, this study shows that the fragmentation of local information (1) is shaped by socio-economic deprivation/inequality that is confounded with ethnoracial heterogeneity, (2) the fragmentation of local information is highly correlated to people's community gatherings, (3) the fragmentation of local information moderates the effects of socio-economic inequality on cultural activity diversity, and (4) the fragmentation of local information mediates the relationship between socio-economic inequality and community engagement. By making use of three local event datasets over 20 months in 14 U.S. cities (about two million records) and over 3 months in 28 U.S. cities (about 620K records), respectively, this study develops computational frameworks to operationalize information deserts in a scalable way. This dissertation provides a theorization of community-level information inequality and computational models that support the quantitative examination of it. Further theorizations of the conceptual constructs and methodological improvements on measurements will benefit information policy-makers, local information system designers, and researchers who study local communities with conceptual models, vocabularies, and assessment frameworks.Item Quantifying the Spatial and Temporal Variation of Land Surface Warming Using in situ and Satellite Data(2019) Rao, Yuhan; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The global mean surface air temperature (SAT) has demonstrated the “unequivocal warming”. To understand the impact of the global warming, it is very important to quantify the spatial and temporal patterns of the surface air temperature change. Currently, most observational studies rely on in situ temperature measurements over the land and ocean. But the uneven and sparse nature of these temperature measurements may cause large uncertainty for the climate analysis especially at local and regional scales. With the rapid development of satellite data, it is possible to estimate spatial complete surface air temperature from satellite data using advanced statistical models. The satellite data-based estimation can serve as a better data source for local and regional climate analysis to reduce analysis uncertainty. In this dissertation, I firstly examined the uncertainty of four mainstream gridded SAT datasets over the global land area (i.e., BEST-LAND, CRU-TEM4v, NASA-GISS, NOAA-NCEI). The comprehensive assessment of these datasets concludes that different data coverage may cause remarkable differences (i.e., -0.4 ~ 0.6°C) of calculated large scale (i.e., global, hemispheric) average SAT anomaly using different datasets. Moreover, these datasets show even larger differences at regional and local scale (5°×5°). The local and regional data differences can lead to statistically significant differences on linear trends of SAT estimated using different datasets. The correlation analysis shows strong relationship between the uncertainty of estimated SAT trends and the density of in situ measurements across different regions. To reduce the uncertainty of surface air temperature data, I developed a statistical modelling framework which can estimate daily surface air temperature using remote sensing land surface temperature and radiation products. The framework uses machine learning models (i.e., rule-based Cubist regression model and multivariate adaptive regression spline) to characterize the physical difference between land surface temperature and surface air temperature by including radiation products at both surface and the top of the atmosphere. The model was firstly developed for the Tibetan Plateau using Cubist model trained with Chinese Meteorological Administration station measurements. Comprehensive evaluation show that the Cubist model can estimate the surface air temperature with nearly zero degree Celsius bias and small RMSEs between 1.6 °C ~ 2.1 °C. The estimated SAT over the entire Plateau for 2000-2015 show that the warming of the western part of the Plateau has been more prominent than the rest of the region. This result show the potential underestimation of conventional station measurements based studies because there are no station measurements to represent the rapid warming region. The machine learning model is then extended to the northern high latitudes with necessary modification to account for the regional difference of the diurnal temperature cycle as well as the large data volume of the northern high latitudes. The MARS model trained using data over the northern high latitudes from the Global Historical Climatology Network daily data archive show a reasonable model performance with the bias of around -0.2 °C and the RMSE ranging between 2.1 – 2.6 °C. Further evaluation shows that the model performs worse over permanent snow and ice surface due to the insufficient training data to represent this specific surface conditions. Overall, this research demonstrated that leveraging advanced statistical methods and satellite products can help generating high quality surface air temperature data which can provide much needed spatial details to reduce the uncertainty of local and regional climate analysis. The model developed in this research is generic and can be further extended to other regions with proper modification and training using high quality local data.