This study explores how the use of complex decorative geometric patterns in Islamic architecture spatially relates to advances in the fields of science and philosophy in the Islamic world between the ninth and fourteenth centuries. This project examines hypotheses developed by vario~s scholars on the forces that shaped the use of these patterns (known as the geometric mode) in Islamic architecture. The prevailing assumption that advances in mathematics contributed to the use of the geometric mode is used as a starting point for subsequent analysis. For this study, two spatial databases were created. One contains over two hundred and twenty monuments of Islamic architecture exhibiting the geometric mode, while the other contains over one hundred records of activity in the sciences and philosophy. From these databases, decorative geometric pattern types were classified and ranked, and scholarly activities were classified. Density maps were developed from these classes and ranks for each century, and were compared in a series of analytical overlay maps. Each map depicts the spatial relationships of the activities in question over a span of three centuries, enabling a spatio-temporal analysis of the connections between disciplines within the context of the broader cultural elements at work. These maps allow for examination of these disciplines in a new way; there has never been a spatial analysis testing the existing hypotheses until now. The density overlay maps show that some of the prevailing hypotheses are partially supported by the data, but the primary hypothesized relationship-that activity in mathematics prompted use of the geometric mode-is not applicable to all regions of the Islamic world during this time period. The spatial analysis exposes the previously overlooked possibility that the geometric mode could have influenced activity in the sciences and philosophy. This study provides tools to better understand the complex relationships among art, science, and philosophy: two spatial databases, a geographic information systems (GIS) model, and resulting analytical overlay maps. The maps produced in this project reveal examples where the quality of contact among disciplines in these very specific times and places is worth examining in greater detail.

The Pacific Northwest is dependent on seasonal snowmelt for water resources that support a significant portion of its economy. Increased temperatures resulting from higher concentrations of atmospheric greenhouse gases may cause disruptions to these resources because of reductions in the annual snowpack and variations of the timing of snowmelt. This study reconstructs and applies a GIS-based distributed hydrologic model at a monthly scale to assess the effects of future climate change on runoff from the Upper Clackamas River Basin (located near Portland, Oregon). Historic flow data and snow measurements are used to calibrate and test the perfonnance of the hydro logic model for a contemporary period (1971-2000), and the model is run for two future scenarios (2010-2039 and 2070-2099) using IS92 climate change scenarios from two global climate circulation models (Hadley and Canadian Centre for Climate) as inputs. The results forecast that mean peak snowpack in the study area will drop dramatically (36% to 49% by 2010-2039, and 83% to 88% by 2070-2099), resulting in earlier runoff and diminished spring and summer flows. Increases to mean winter runoff by by the 2070-2099 period vary from moderate (13.7%) to large (46.4%), depending on the changes to precipitation forecasted by the global climate circulation models. These results are similar to those of other studies in areas dependent on snowpack for seasonal runoff, but the reductions to snowpack are more severe in this study than similar studies for the entire Columbia Basin, presumably because the elevations of much of the Upper Clackamas Basin are near the current mid-winter snow line.

The main scope of this project is to rebuild and deploy web applications that will help share historical artifacts related to the Santa Ana watershed. This project is designed to give a consistent user interface and add extra tools to enhance the functionality of existing applications at the Water Resources Institute (WRI) at California State University San Bernardino (CSUSB). The purpose of this project is to migrate all applications to a single server and update the applications using ArcGIS Server 9.2. Also to give a consistent look to the applications and to make them user friendly.

This mixed-methods study addresses the relationship between the availability of food and realized food access by studying the retail food landscape of Holyoke, Massachusetts – a small, socio-economically diverse city. While a large body of empirical research finds that low-income communities and communities of color are especially likely to lack adequate access to healthy foods and experience increased vulnerability to food insecurity, few studies explore urban food environments through a mixed-methods case study approach. Through the use of food store mapping, store audits, and resident interviews, this research is a nascent attempt to articulate how the unique development histories and cultural politics of urban neighborhoods affect food access. The analysis finds that local food environments in Holyoke vary by social and spatial context. The study further considers how health and stability of a community is affected by the distribution and variety of food retail stores. In particular the study articulates the constructs of race and class in the food environment via the spatial mismatch of preferred food stores, mobility challenges, and the role of small urban food stores in the context of Holyoke’s foodscape. Implications for local food security policy are discussed.

Modern agricultural systems have been criticized for their detrimental effects on the environment and a general emphasis on crop yield rather than long-term sustainability. Traditional forms of agriculture may provide case-specific examples of sustainable alternatives for contemporary societies. In the seasonally inundated savannas of the Llanos de Mojos, pre-Columbian Indians piled earth into ‘large raised field platforms’ elevated high enough above the floodplain to allow crops to grow. Archaeological evidence indicates that raised field agriculture supported much larger populations than those found in the Beni today. The examination of satellite imagery has revealed more than 40,000 individual fields spread across an area of approximately 7,500 square kilometers. This study created a digitized map of large raised fields to search for spatial patterns in their distribution. A GIS analysis was conducted in which fields were distributed into organizational groups based on characteristics such as proximity and orientation to cardinal direction. These groups represent potential ‘social units’ involved in the organization of labor required to construct raised fields. This study demonstrated the consistent presence of these units throughout the entirety of the agricultural system. Patterns in the distribution of these groups allowed the study area to be divided into two distinct regions representing a larger scale of organization within a seemingly uniform system. A transitional zone between these two regions was identified on the river Omi, providing a clear area of interest to target in future archaeological excavations. Further archaeological investigations of raised field agriculture have the potential of demonstrating the overall productivity of the system as well as how it was incorporated into the social systems of those who managed it.

To increase capacity for monitoring and surveillance of tick-borne diseases, publicly available tick distribution and climate change datasets are required to create accurate predictive distribution models. It is difficult, however, to assess model accuracy and utility when using incomplete datasets.  The more recent development of comprehensive tick databases for Europe and availability of climate change scenarios from multiple IPCC Assessment Reports allows for improved modeling efforts. Multiple tick datasets were combined and three climate change projections were compared by predicting current and future distributions of Ixodes ricinus ticks in Europe using the MaxEnt species distribution model. Overall, much of Europe contains suitable habitat for the Ixodes ricinus tick, both now and under future climate change projections.  Contraction of habitable areas is predicted to occur at lower latitudes and altitudes, while expansion is predicted to occur at higher altitudes in mountainous regions and the higher latitudes, primarily in northern Scandinavia.

The purpose of this dissertation is to examine the experiences of St. Bernard Parish, La., residents as they coped with the impact of the catastrophe of Hurricane Katrina on August 29, 2005. An estimated 50,000 St. Bernard Parish residents relocated to a new home one year after Katina in 2006, and many of those residents moved again. This study examines the effects of the decisions of St. Bernard residents to relocate or to return on their social connections. The utility, adaptability and durability of social networks of these residents will be explored to enrich our knowledge about the social effects of recovery and the role that distance plays in the way residents connect to each other six years after Hurricane Katrina. It also examines the applicability of disaster theory as it relates to this case and develops a methodology for examining the impact of geographic dispersal on social networks.

This thesis outlines a technique for rapid documentation of historic sites in volatile cultural landscapes. Using Holt Cemetery as an exemplary case study, a workflow was developed incorporating RTK terrain survey, UAS aerial imagery, photogrammetry, GIS, and smartphone data collection in order to create a multifaceted database of the material and spatial conditions, as well as the patterns of use, that exist at the cemetery. The purpose of this research is to create a framework for improving the speed of data creation and increasing the accessibility of information regarding threatened cultural resources. It is intended that these processes can be scaled and adapted for use at any site, and that the products generated can be utilized by researchers, resource management professionals, and preservationists. In utilizing expedited methods, this thesis specifically advocates for documentation of sites that exist in coastal environments and are facing imminent destruction due to environmental degradation.

Before the creation of Mammoth Cave National Park, this area was home to numerous communities, each with a sense of identity. To prepare for the creation of the National Park, all residents living within these communities were relocated, and many of these communities were lost to the passage of time. Today, public memory of these lost communities is being fostered by the descendents of the pre-park area. Through the use of a Historical Geographic Information System, 1920 Edmonson County manuscript census data, and statistical analysis, the demographic composition of these lost communities was explored. This project not only brought to light a past that is not well known, but also built interest in sustaining public memory of the Mammoth Cave pre-park area through the use of historical GIS and public participation.

Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.