College of Behavioral & Social Sciences
Permanent URI for this communityhttp://hdl.handle.net/1903/8
The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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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.Item HUMAN-INDUCED VEGETATION DEGRADATION IN A SEMI-ARID RANGELAND(2017) Jackson, Hasan; Prince, Stephen D; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Current assessments of anthropogenic land degradation and its impact on vegetation at regional scales are prone to large uncertainties due to the lack of an objective, transferable, spatially and temporally explicit measure of land degradation. These uncertainties have resulted in contradictory estimates of degradation extent and severity and the role of human activities. The uncertainties limit the ability to assess the effects on the biophysical environment and effectiveness of past, current, and future policies of land use. The overall objective of the dissertation is to assess degradation in a semi-arid region at a regional scale where the process of anthropogenic land degradation is evident. Net primary productivity (NPP) is used as the primary indicator to measure degradation. It is hypothesized that land degradation resulting from human factors on the landscape irreversibly reduces NPP below the potential set by environmental conditions. It is also hypothesized that resulting reductions in NPP are distinguishable from natural, spatial and temporal, variability in NPP. The specific goals of the dissertation are to (1) identify the extent and severity of degradation using productivity as the primary surrogate, (2) compare the degradation of productivity to other known mechanisms of degradation, and (3) relate the expression of degradation to components of vegetation and varying environmental conditions. This dissertation employed the Local NPP Scaling (LNS) approach to identify patterns of anthropogenic degradation of NPP in the Burdekin Dry Tropics (BDT) region of Queensland (14 million hectares), Australia from 2000 to 2013. The method started with land classification based on the environmental factors presumed to control NPP to group pixels having similar potential NPP. Then, satellite remotely sensing data were used to compare actual NPP with its potential. The difference, in units of mass of carbon fixed in NPP per unit area per monitoring interval and per year, also its percentage of the potential, were the measures of degradation. Degradation was then compared to non-green components of vegetation (e.g. wood, stems, leaf litter, dead biomass) to determine their relationship in space and time. Finally, the symptoms of degradation were compared to land management patterns and the environmental variability (e.g. drought, non-drought conditions). Nearly 20% of the region was identified as degraded and another 7% had significant negative trends. The average annual reduction in NPP due to anthropogenic degradation was -17% of the non-degraded potential, although the severity of degradation varied substantially throughout the region. Non-green vegetation cover was strongly correlated with the inter-annual and intra-annual temporal trends of degradation. The dynamics of degradation in drought and non-drought years provided evidence of multiple stables states of degradation.Item Restrictive Deterrence and the Severity of Hackers' Attacks on Compromised Computer Systems(2014) Wilson II, Theodore Henry; Maimon, David; Criminology and Criminal Justice; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is a lack of consensus within the literature assessing whether surveillance is effective in reducing the seriousness of criminal events, with almost no prior study investigating its operation in cyberspace. This thesis seeks to address both of these domains while drawing on the deterrence perspective. Data were obtained from an experiment conducted over seven months at a large, public university within the United States. Specifically, a series of virtual computers with known vulnerabilities were deployed into the university's computer network as part of a randomized controlled trial. This thesis seeks to examine 1) whether a surveillance banner reduces the severity of offending through inhibiting hackers from escalating to active engagement with the system upon gaining access on the first session and 2) whether the deterrent effect of a surveillance banner persists beyond the first session. This surveillance banner produced a restrictive deterrent effect for the first and second sessions.