Understanding human mobility patterns is important for severe weather warning since these patterns can help identify where people are in time and in space when flash floods, tornados, high winds and hurricanes are occurring or are predicted to occur. A GIS (Geographic Information Science) data model was proposed to describe the spatial-temporal human activity. Based on this model, a metric was designed to represent the spatial-temporal activity intensity of human mobility, and an index was generated to quantitatively describe the change in human activities. By analyzing high-resolution human mobility data, the paper verified that human daily mobility patterns could be clearly described with the proposed methods. This research was part of a National Science Foundation grant on next generation severe weather warning systems. Data was collected from a specialized mobile app for severe weather warning, called CASA Alerts, which is being used to analyze different aspects of human behavior in response to severe weather warnings. The data set for this research uses GPS location data from more than 300 APP users during a 14 month period (location was reported at 2 minutes interval, or at based on a 100m change in location). A targeted weather warning strategy was proposed as a result of this research, and future research questions were discussed.

Presidio Los Adaes (16NA16), located in present day northwest Louisiana, served as the capital of Spanish Texas for much of the 18th century. Maps and historical documents provide architectural plans for the fort and other buildings on the site but differ in the size and location of several buildings. In 2009, a geophysical survey of the site with ground penetrating radar, magnetometry, electrical resistance, and electromagnetic induction showed good preservation of architectural foundations. Visual assessment of the maps and geophysical data, information from archaeological excavations, and spatial statistics suggests that the fort was originally built as specified by the architect, but over time changed as the needs of the settlement evolved and the population dwindled.

Ground-penetrating radar (GPR) and magnetometry were used at Tel Shimron, an archaeological site in Israel’s Jezreel Valley. GPR primarily measures electric properties while magnetometry measures magnetic properties, making them complementary methods for subsurface prospection. Magnetometry can be collected and processed quickly, making it an ideal landscape-scale reconnaissance tool. It takes more time to collect, process, and interpret GPR data, but the result is a higher resolution dataset. In addition, GPR often works better than magnetometry in desert environments such as the Jezreel Valley. Conventional wisdom suggests that GPR should not be used as a landscape-scale reconnaissance tool unless there is ample time to process and interpret the data. Despite this, GPR was used at Tel Shimron with standardized, semi-automated processing routines and eight field technicians to produce an end product. The GPR survey revealed more about the subsurface than magnetometry, including three potential dwellings and a Bronze Age city gate.

Although recent years have seen a dramatic increase in research on the relationship between space and crime, few studies have examined the impact of space on the juvenile justice outcomes of treatment non-completion and recidivism. Fewer yet have investigated how such effects might differ on those outcomes when disaggregated by the reason for non-completion and recidivism offense type. This study seeks to address those theoretical gaps by determining the effects of distance to treatment for juvenile offenders on type of treatment non-completion and recidivism. By estimating the effects of not only linear and temporal distance, but also social distance, this analysis represents a valuable inquiry into the influence of space on juvenile offenders. Data on juvenile offenders adjudicated in Philadelphia's Family Court to attend community-based treatment from 1996 through 2002 provide this study with 6,208 individual units of analysis. Data describing the neighborhoods in which the juveniles live and the programs that they attend are also included in this analysis. The use of hierarchical linear models allows for the simultaneous estimation of multiple levels of control variables when modeling the effects of distance to treatment. Separate models are constructed to estimate the direct effects of distance to treatment on treatment non-completion and then the subsequent, indirect effects of distance to treatment on juvenile recidivism. Results from two-level models that control for both neighborhood and program context indicate that distance to treatment does influence the likelihood of both treatment non-completion and recidivism. In general, distance to treatment was found to increase the likelihood of both treatment non-completion and recidivism as distance to treatment increases. Interaction effects that represent the joint effects of distance and race indicate that the impact of distance is generally greater for non-Whites, as they are more likely than Whites to fail to complete treatment as distance increases. Results from cross-classified models reveal limitations of the data related to statistical power and noise. Findings from this analysis contribute to several bodies of literature, including criminology and geography, and strongly support the consideration of distance to treatment by policymakers within the juvenile justice system. / Criminal Justice

Aedes aegypti is the principal vector for dengue transmission across large areas of the world. Understanding where this critically important vector lives is paramount to preventing the continued expansion of dengue globally. Using 1981-2010 bioclimatic (bioclim) variables, as well as additional environmental variables and elevation data, multiple MaxEnt models were constructed for Ae. aegypti within the South American continent to identify ecologically important variables and areas of persistent high suitability. All models had acceptable AUC scores (> 0.70), indicating accurate model fitting. Annual mean precipitation and temperature were found to be important model variables; however, when added, humidity became the more predictive variable. Elevation had minimal, if any, impact on model construction predictability. When utilizing MaxEnt models, non-traditional climatic variables, i.e., humidity, were significantly more predictive for Ae. aegypti than the standard bioclim variables. Optimal variable selection is critical to understanding current and potential future areas of high transmission risk.

Epidemic obesity in the U.S. has prompted exploration of causal factors related to the built environment. Recent research has noted statistical associations between the spatial accessibility of retail food sources, such as supermarkets, convenience stores, and restaurants, and individual characteristics such as weight, socioeconomic status, and race/ethnicity. These studies typically use residential proximity or neighborhood density to food sources as the measure of accessibility. Assessing food environments in this manner, however, is very limiting. Since most people travel outside of their neighborhood on a daily basis, the retail food sources available to individuals residing in the same area could vary widely. This research developed new techniques for describing food accessibility or food environments based upon individuals’ activity and travel patterns, or their activity spaces. Researchers have previously used travel diaries to study activity and travel behavior, but these are burdensome for participants, and are prone to recall error and other inaccuracies. This study explored use of global positioning system (GPS) to identify participants' activity spaces, and employed a geographic information system (GIS) to assess the retail food sources located within these spaces. This produced ‘activity-based’ measures of individual retail food accessibility that do not rely on areal units, nor require travel diaries. Participants included 121 residents of a census tract in Lexington, Kentucky who agreed to carry GPS trackers for three workdays, and complete surveys regarding weight, socioeconomic and demographic characteristics, and diet and food purchasing habits. The types and relative frequencies of food locations within their activity spaces were compared to those within close proximity to the census tract. Dietary and food purchasing habits were subsequently analyzed in relation to activity-based food environment measures. The results of this study demonstrate substantial potential for misclassification bias in food accessibility research based on residential proximity or neighborhood density. Furthermore, this study observed statistically significant relationships between the new activity-based food accessibility measures and some personal characteristics and food-related behaviors. Despite some limitations, the techniques developed in this research show great potential for future research, which should be explored further in a variety of contexts.

Digital Elevation Models (DEMs) are digital representations of surface topography or terrain. Collection of DEM data can be done directly through surveying and taking ground control point (GCP) data in the field or indirectly with remote sensing using a variety of techniques. The accuracies of DEM data can be problematic, especially in rugged terrain or when differing data acquisition techniques are combined. For the present study, ground data were taken in various protected areas in the mountainous regions of Nepal. Elevation, slope, and aspect were measured at nearly 2000 locations. These ground data were imported into a Geographic Information System (GIS) and compared to DEMs created by NASA researchers using two data sources: the Shuttle Radar Topography Mission (STRM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Slope and aspect were generated within a GIS and compared to the GCP ground reference data to evaluate the accuracy of the satellitederived DEMs, and to determine the utility of elevation and derived slope and aspect for research such as vegetation analysis and erosion management. The SRTM and ASTER DEMs each have benefits and drawbacks for various uses in environmental research, but generally the SRTM system was superior. Future research should focus on refining these methods to increase error discrimination.

There is much potential to decrease energy consumption in the U.S. by encouraging compact, centralized development. Although many studies have examined the extent to which built environment and demographic factors are related to household energy use, few have considered both building and transportation energy together. We hypothesized that residents living further from city centers, or urban cores, consume more energy for both purposes than their inner city counterparts, resulting in a direct relationship between building and transportation energy usage. This hypothesis was tested with two case studies. The first focused on New York City. Annual building energy per unit of parcels, or tax lots, containing large multi-family structures was compared to the daily transportation energy use per household of traffic analysis zones (TAZs) (estimated with a regional travel demand model). Transportation energy showed a strong spatial pattern, with distance to urban core explaining 63% of variation in consumption. Building energy use was randomly distributed, resulting in a weak negative correlation with transportation energy. However, both correlation with distance to urban core and transportation energy became significant and positive when portion of detached single-family units for TAZs was used as a proxy for building energy. Structural equation models (SEMs) revealed a direct relationship between log lot depth and both uses of energy, and inverse relationship between portion of attached housing units and transportation energy. This supports the notion that sprawling development increases both the building and transportation energy consumption of households. For the second analysis, annual building and automobile energy use per household were estimated for block groups across the 50 most populous U.S. metropolitan regions with Esri Consumer Expenditure Data. Both forms of energy consumption per household were lowest in inner cities and increased at greater distances from urban cores. Although there may be some error in estimates from modeled expenditure data, characteristics associated with lower energy use, such as portion of attached housing units and commuters that utilize transit or pedestrian modes, were negatively correlated with distance to urban core. Overall, this work suggests there are spatial patterns to household energy consumption, with households further from urban cores using more building and transportation energy. There is the greatest gain in efficiency to be had by suburban residents.

Geospatial data almost always contains some amount of uncertainty due to inaccuracies in its acquisition and transformation. While the data is commonly visualized (e.g. on digital maps), there are unanswered needs for visualizing uncertainty along with it. Most research on effectively doing this addresses uncertainty in data values at geospatial positions, e.g. water depth, human population, or land-cover classification. Uncertainty in the data’s geospatial positions themselves (positional uncertainty) has not been previously focused on in this regard. In this thesis, techniques were created for visualizing positional uncertainty using World Vector Shoreline as an example dataset. The techniques consist of a shoreline buffer zone to which visual effects such as gradients, transparency, and randomized dots were applied. They are viewed interactively via Web Map Service (WMS). In clutter testing with human subjects, a transparency-gradient technique performed the best, followed by a solid-fill technique, with a dots-density-gradient technique performing worst.

In this study, I modeled the suitability of habitat on the Mississippi Sandhill Crane National Wildlife Refuge for the federally endangered Mississippi Sandhill Crane (Grus Canadensis pulla). Habitat type and suitability changed over time due to seasonality of vegetation and succession in the absence of burning. Cranes used highly suitable habitat more in the non-growing than in the growing season, and may have been more constrained by resource availability during winter months. Cranes used some less-suitable areas including cypress drains, which provide roosting sites, and supplemental food plots. The mismatch between predicted quality and crane use suggests that no single habitat provides all resources required for the population to persist. Prescribed burning to maintain grassland habitat is essential for maintaining high quality habitat for cranes. The relative availability of food on supplemental food plots and grasslands, as well as the behavior of cranes toward roads, require additional investigation.