This thesis investigates spatial patterns of income-based residential segregation at the neighborhood level in the Russian capital city Moscow within new administrative boundaries, which have received relatively little attention in prior studies. It is argued that Moscow faces high levels of income inequality exacerbated by growing levels of spatial segregation between the affluent and prestigious Center – South-West and poor industrial South – South-East. Applying a whole set of quantitative methods complemented with computer mapping techniques, based on the latest 2013 data by the City of Moscow Territorial Branch of the Federal State Statistics and 2010 Census data, this study provides new insights into spatial differentiation processes and elaborates policy solutions aimed at addressing economic disparities in the city. A key finding of this thesis is that income segregation in the study area has been driven to a larger extent by the isolation of very poor neighborhoods from middle- and upper-income areas.

The Online Transportation Option System (OTOS) is a Geographic Information System (GIS) that addresses many of the limitations associated with traditional dynamic ridematching applications. The main improvements are in the areas of trip scheduling and match searching. OTOS is unique in its ability to accept trips with schedules that can not be expressed in terms of a regular weekly trip. OTOS also distinguishes itself in its use of spatial analysis techniques to locate matches. Specifically, the use of a shortest path solver enables the ridematching algorithm to perform a search along the path of a users trip, in addition to the customary radial search around the endpoints. The shortest path solver is also used to calculate the driving distance between the user and a match. This provides a more accurate measurement than the straight-line distance used by other algorithms, especially in the presence of barriers.

The prevalence rate of the Human Immunodeficiency Virus (HIV) in Brazil has stabilized since the year 2000 at approximately 0.35 percent of the total population (600,000 people). Most researchers and political actors agree that the success in HIV management has been highly correlated with some of the policies that the Brazilian government has implemented concerning the HIV/ AIDS positive population (Levi et al 2002; Dourado 2006; Parker 2009). With worldwide recognition of this accomplishment, one must wonder why it is that the North and Northeast regions of Brazil have been experiencing trends of increasing HIV/ AIDS incidence in the past decade (Nunn et al 2009). This study concentrates on the spatial distribution of HIV incidence in the year 2000, as it uncovers how HIV distribution can be related to aspects of marginalization in the second-most populous Northeastern municipality; Fortaleza, Brazil. The central hypothesis of this research states that HIV incidence is positively correlated with rate of marginalization. Marginalization is considered as the sector of population without access to basic social services, such as education, running water, and appropriate housing. Spatial patterns of HIV and marginalization are examined and interpreted in the context of the Brazilian Model. This research suggests that although marginalization has a strong spatial pattern, HIV is not demographically or geographically discriminatory. / text

The current study uses a combination of quantitative and spatial analysis to examine the impact of affordable housing administered by the Texas Department of Housing Affairs on the neighborhood crime rate, in Dallas, Texas. Pre and post construction period analysis, for duration of five years from 2000 to 2004 provided an in-depth view on the direct impact of affordable housing at the neighborhood level. The crime rates were measured alongside with the sociodemographic characteristics of the area to see any association between the two. The findings suggest that the affordable housing were mostly located in areas with higher concentration of minority population and low median household income. The results of this study showed negligible increase in crime rates but a through analysis could provide an in-depth analysis of the issue. / text

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Improvements in GPS tracking technologies have resulted in the collection of high resolution movement datasets for a range of wildlife species. In combination with new high resolution remote sensing products, researchers now have the ability to ask complex questions regarding animal movement in heterogeneous landscapes. However, there currently exists a dearth of analytical approaches to combine movement data with environmental variables. Developing methods to examine wildlife movement-environment interactions are particularly relevant given our unprecedented access to high resolution data; however, the analytical and technical challenges of integrating two disparate data types have yet to be effectively overcome. In the analyses presented in this thesis, I examine current approaches for linking wildlife movement to the physical environment, and introduce a data-driven method for examining wildlife movement-environment interactions. The first analysis consists of a review of existing tools in wildlife movement analysis, specifically tools supported within R statistical software, to highlight any existing methodological opportunities and limitations associated with relating movement to landscape features. The review highlights R’s strengths as an integrated toolbox for exploratory analyses, and the current lack of applications for linking high density telemetry datasets with environmental variables. AdehabitatLT was the most functional package available, offering the greatest variety of analysis options. Due to the comprehensive nature of adehabitatLT, I recommend that future method development be implemented through its package specific framework. Extending the first analysis, the second portion of this research introduces a data-driven method, based in semivariogram modelling, for quantifying wildlife movement patterns relative to linear features. The semivariogram-based method is applied to grizzly bear telemetry data to quantify how grizzly bear movement patterns change in relation to roads. The semivariogram-based method demonstrated that the bears’ spatial scale of response ranged from 35 m- 90 m from roads but varied by age, sex, and season. Applying the scales of response to link near-road movement patterns to survival and mortality, revealed that bears that were killed displayed less-risk adverse movements near roads than bears that survived (i.e., longer step lengths and more day light movements around roads). Given this pattern, my data suggest a minimum vegetation buffer of 90 m to serve as screening cover along roadsides to help mitigate the effects of roads on grizzly bear populations in west-central Alberta, Canada. Through the development of data-driven methods in wildlife movement analysis, I can realize the full potential of high resolution telemetry datasets. Data-driven methods reduce the subjectivity within movement analyses, providing more relevant measures of wildlife response to environment. The semivariogram-based method can identify definitive zones of influence around linear disturbance features in any wildlife system, thereby, providing managers with spatially explicit, data-driven insights to reduce impacts on wildlife in multi-use landscapes. / Graduate

published_or_final_version / abstract / toc / Computer Science and Information Systems / Master / Master of Philosophy

The spatial arrangements of artifacts and features within archaeological sites have often been used to isolate activity areas and draw inferences about site function. This approach assumes that objects found in close proximity were used for the same task, and that artifacts are usually discarded where they were used. However, the location of artifact abandonment often has more to do with patterns of discard and use/reuse of the site throughout time than with the function or location of activities. This dissertation uses a comparative framework to address how the observed spatial structure of Middle Paleolithic sites in France sites was formed through centrifugal dispersion of lithic artifacts, i.e. the displacement of artifacts between their creation and the final location of their abandonments. Seven Middle Paleolithic sites were included in this study. The sites were excavated over large areas, from 200 to more than 2000 m². They range from small single component occupation sites to lithic raw material workshops with assemblages of more than 15,000 artifacts. The movement of artifacts is tracked through an analysis of sets of refitted lithics and through comparisons of the distributions of multiple classes of artifact across areas of the sites with differing artifact densities. Studying the distribution of lithic technological classes and tracking their movement through refitting sets provides new perspectives on the ways Paleolithic archaeological assemblages and sites were formed. The temporality of site use had a much greater impact on site structure than did activities that took place at any one point during a site's occupation. These data enabled me to assess the relative lengths and numbers of occupations for the seven sites in this study. The approach taken in this study not only provides a clearer understanding of site formation and structure than do studies that strive to isolate "activity areas," but it also provides information about the sizes of past human groups and the ways they moved among different localities on the landscape. Such insights are integral to the study of land use, mobility and economic adaptations among Paleolithic hunter-gatherers.

Digital soil mapping supervised and unsupervised classification methods were evaluated to aide soil survey of unmapped areas in the western United States. Supervised classification of landscape into mountains and basins preceded unsupervised classification of data chosen by iterative data reduction. Principal component data reduction, ISODATA classification, Linear combination of principal components, Zonal averaging of linear combination by ISODATA class, Segmentation of the image into polygons, and Attribution of polygons by majority ISODATA class (PILZSA process) comprised steps isolating unique soil-landscape units. Input data included ASTER satellite imagery and USGS 30-m elevation layers for environmental proxy variables representing soil forming factors. Results indicate that PILZSA captured general soil patterns when compared to an existing soil survey while also detecting fluvial soils sourced from different lithologies and unique mountain areas not delineated by the original survey. PILZSA demonstrates potential for soil pre-mapping, and sampling design efforts for soil survey and survey updates.

This study sought to provide empirical evidence regarding the use of spatial analysis in enrollment management to predict persistence and graduation. The research utilized data from the 2000 U.S. Census and applicant records from The University of Arizona to study the spatial distributions of enrollments. Based on the initial results, stepwise logistic regression was used to identify spatially associated student and neighborhood characteristics predicting persistence and graduation.The findings of this research indicate spatial analysis can be used as a valuable resource for enrollment management. Using a theoretical framework of the forms of capital and social reproduction, cultural and social capital characteristics were found to influence persistence at statistically significant levels. Most notably, the social capital proxy of neighborhood education levels, and the cultural capital proxies of the number of standardized tests a student has taken, and when the application for admission is submitted all significantly influenced a student's probability to persistence and graduate. When disaggregating by race and ethnicity, resident Hispanic students from highly Hispanic neighborhoods were found to persist at higher levels in the first year of college attendance. Also, resident Native Americans were found to have a higher probability to persist when evidencing cultural capital characteristics. Since spatially based student and neighborhood characteristics can be quantified and mapped, target populations can be identified and subsequently recruited, resulting in retention-focused admissions.