Geography
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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 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 A Spatial-Temporal Analysis of Wetland Loss and Section 404 Permitting on the Delmarva Peninsula from 1980 to to 2010(2017) Stubbs, Quentin A.; Yeo, In-Young; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Geospatial approaches for wetland change analyses have emphasized the evaluation of landscape change on a local level, but have often neglected to examine and integrate regional trends and patterns of land use and land cover change as well as the impacts of wetland management policies. This study attempts to bridge the gaps by integrating a geospatial assessment of land cover change and a geostatistical analysis of the physical and anthropogenic drivers of wetland change. The aim is to demonstrate how urban development, conservation, and climate change policy decisions influenced wetland change trends and patterns on the Delmarva Peninsula from 1980 to 2010. Historical data on the nine counties on the Delmarva Peninsula illustrated the dynamism of population growth, sprawl, and different wetland management strategies. Data sets from the National Oceanic and Atmospheric Administration, the Chesapeake Bay Program, the U.S. Army Corps of Engineers, the U.S. Fish and Wildlife Service, and the U.S. Census Bureau, and other sources were gathered and assessed. A land cover database was developed and analyzed using geospatial techniques, such as cross tabulation matrices and hot spot density analyses, in order to quantify and locate land cover change between 1984 and 2010. The results highlighted that anthropogenic drivers such as urbanization and agriculture were associated with the loss of wetlands in coastal areas as well as in upland, forested, suburban areas that were at low risk to flooding, but required deforestation in order to expand residential and commercial development. The greatest quantity and percentage of loss occurred between 1992 and 2001, and it was likely the result of increases in tourism and suburban sprawl (e.g., the Housing Boom and roadway expansion). The majority of wetland loss tapered off in 2000, except on coastal areas suffering from sea level rise and shoreline erosion. The results also reinforced the need to address the negative impacts from certain activities related to agriculture and silviculture, which are exempt from Section 404 of the Clean Water Act, have on wetlands. Physical drivers and processes like inundation from sea level rise and soil erosion from surface runoff force communities to simultaneously adapt to multiple drivers of wetland loss and alteration. This study supports the hypothesis that an increase in development and wetland permitting indicates an increased a risk of wetland loss. In the end, the study demonstrates that geostatistical modelling techniques can be used to predict wetland loss, and that model performance and accuracy can be improved by reducing the multicollinearity of independent variables. Planners and policymakers can use these models to better understand the wetland locations that are at greatest risk to loss, as well as the drivers and landscape conditions that have the greatest influence on the probability of wetland loss.Item DETERMINING CONSERVATION PRIORITIES AND PARTICIPATIVE LAND USE PLANNING STRATEGIES IN THE MARINGA-LOPORI-WAMBA LANDSCAPE, DEMOCRATIC REPUBLIC OF THE CONGO(2012) Nackoney, Janet; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Deforestation and forest degradation driven largely by agricultural expansion are key drivers of biodiversity loss in the tropics. Achieving sustainable and equitable management of land and resources and determining priority areas for conservation activities are important in the face of these advancing pressures. The Congo Basin of Central Africa contains approximately 20% of the world's remaining tropical forest area and serves as important habitat for over half of Africa's flora and fauna. The Government of the Democratic Republic of the Congo (DRC) is currently laying the foundation for a national land use plan for conservation and sustainable use of its forests. Since 2004, the African Wildlife Foundation (AWF) has led efforts to develop a participatory land use plan for the Maringa-Lopori-Wamba (MLW) Landscape located in northern DRC. The landscape was recognized in 2002 as one of twelve priority landscapes in the Congo Basin targeted for the establishment of sustainable management plans. This dissertation focuses on the development of geospatial methods and tools for determining conservation priorities and assisting land use planning efforts in the MLW Landscape. The spatio-temporal patterns of recent primary forest loss are analyzed and complemented by the development of spatial models that identify the locations of 42 forest blocks and 32 potential wildlife corridors where conservation actions will be most important to promote future viability of landscape-wide terrestrial biodiversity such as the bonobo (Pan paniscus). In addition, the research explores three scenarios of potential agricultural expansion by 2050 and provides spatially-explicit information to show how trade-offs between biological conservation and human agricultural livelihoods might be balanced in land use planning processes. The research also describes a methodological approach for integrating spatial tools into participatory mapping processes with local communities and demonstrates how the resulting spatial data can be used to inform village-level agricultural land use for resource planning and management. Conclusions from the work demonstrate that primary forest loss is intensifying around agricultural complexes and that wildlife corridors connecting least-disturbed forest blocks are most vulnerable to future forest conversion. Conservation of these areas is possible with the development of land use plans in collaboration with local communities.Item Identifying landslide hazards in a tropical mountain environment, using geomorphologic and probabilistic approaches(2007-12-17) Roa, Jose G; Kearney, Michael S.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The objective of this study is the performance, assessing, comparison and validation of a set of three landslide hazard maps: The geomorphological, the multicriteria evaluation (MCE) and the probabilistic (weights of evidence); in order to evaluate its accuracy, advantages and limitations, and finally state its reliability. These approaches were tested in a tropical mountain environment located in the central Venezuelan Andes. The scale of this study is regional. A landslide inventory map was generated through aerial-photointerpretation and by the processing of two sets of Landsat imagery via contrast-widening color composite, given as result the outline of 493 landslide polygons, then given the main role played for a digital elevation model (DEM) as data input, a DEM for the study area was built through remotely sensed data obtained from the shuttle radar topographical mission (SRTM) and optical stereographic imagery provided by the advanced spaceborne thermal emission and reflection radiometer (ASTER) system. Because of the comparative nature of this study, these data was preliminary processed via density analysis in order to establish a common background on the landsliding process - passive factors relationship, which was used later to set up the criteria applied in the geomorphological and multicriteria evaluation (MCE) approaches. As a way of validation, the accuracy and error rate of the three landslide hazard maps were performed by its comparison to the landslide inventory map. It was concluded that although the geomorphological approach achieved a better landslide predictive power for this study area at a regional scale, the remaining procedures can play a complementary role, for example the MCE plays a crucial role in an early assessment of landslide hazard which highlights the needs and improving necessary to achieve a better probabilistic approach, which can be later incorporated in a more objective geomorphological assessment. Results also showed that any methodology can be improved and even empowered by the development of better and more integrated standards for factor maps collection rather that the simplification of them, in that way, further studies at regional scale must explore the remotely sensed imagery capacities for generation of data bases addressing regional susceptibility to landsliding process.