Geography Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2773
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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 PEOPLE AND PIXELS: INTEGRATING REMOTELY-SENSED AND HOUSEHOLD SURVEY DATA FOR FOOD SECURITY AND NUTRITION(2020) Cooper, Matthew William; Hansen, Matthew C; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)For several decades now, the study of environmental impacts on human well-being has been informed by what are called "People and Pixels'' methods: the combining of remotely sensed data about environmental conditions with geolocated data from household surveys about health and nutrition. However, much of this work has been conducted at the scale of individual countries and often relies on only one or two survey waves, which creates substantial issues around spatial autocorrelation and endogeneity. Furthermore, much of this work uses simple linear regression as its analysis technique, which is limited in its ability to describe spatial variation as well as non-linearities in the relationship between the environment and human well-being. Thus, this dissertation uses several insights from the emerging field of data science to advance these methods. First, this analysis draws on large, multinational datasets from dozens of surveys, making it possible to better estimate the non-linear effects of climate extremes on human well-being as well as examine spatial heterogeneities in vulnerability. Secondly, this analysis uses techniques at the boundary between traditional econometric regression models and more complex machine learning models, such as using Generalized Additive Models (GAMs) as well as LASSO estimation. This permits the creation of spatially-varying terms as well as nonlinear effects. Applying these techniques, the dissertation has yielded several insights that could be beneficial to policymakers in governments, non-profits, and multinational organizations. The initial chapters analyze the effects of rainfall anomalies on food security and malnutrition, finding that the effect of an anomaly varies considerably depending on the local socioeconomic and environmental contexts, with low-income, poorly-governed, and arid countries, such as Somalia and Yemen, being the most vulnerable. The latter chapters look at the role of ecosystem services in improving human livelihoods, as well as how land cover is associated with dependence on local provisioning ecosystem services.Item Multi-dimensional measures of geography and the opioid epidemic: place, time and context(2019) Cao, Yanjia; Stewart, Kathleen; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The opioid crisis has hit the United States hard in recent years. Behavioral patterns and social environments associated with opioid use and misuse vary significantly across communities. It is important to understand the geospatial prevalence of opioid overdoses and other impacts related to the crisis in order to provide a targeted response at different locations. This dissertation contributes a framework for understanding spatial and temporal patterns of drug prevalence, treatment services access and associated socio-environmental factors for opioid use and misuse. This dissertation addresses three main questions related to geography and the opioid epidemic: 1) How did drug poisoning deaths involving heroin evolve over space and time in the U.S. between 2000-2016; 2) How did access to opioid use disorder treatment facilities and emergency medical services vary spatially in New Hampshire during 2015-2016; and 3) What were the relations between socio-environmental factors and numbers of emergency department patients with drug-related health problems over space and time in Maryland during 2016-2018. For the first study, this dissertation developed a spatial and temporal data model to investigate trends of heroin mortality over a 17-year period (2000-2016). The research presented in this dissertation also involved developing a composite index to analyze spatial accessibility to both opioid use disorder treatment facilities and emergency medical services and compared these locations with the locations of deaths involving fentanyl to identify possible gaps in services. In the third study for this dissertation, I utilized socially-sensed data to identify neighborhood characteristics and investigated spatial and temporal relationships with emergency department patients with drug-related health problems admitted to the four hospitals in the western Baltimore area in Maryland during 2016 to 2018, in order to identify the dynamic patterns of the associations in terms of various socio-environmental factors.