The objective of this study was to deploy a conceptual framework developed by M. Forbes using a geographic information system (GIS) approach to assess the functionality of wetlands in the Galveston Bay Area of Texas. This study utilized geospatial datasets which included National Wetland Inventory maps (NWI), LiDAR data, National Agriculture Imagery Program (NAIP) imagery and USGS National Land Cover data to assess the capacity of wetlands to store surface water and remove pollutants, including nitrogen, phosphorus, heavy metals, and organic compounds. The use of LiDAR to characterize the hydrogeomorphic characteristics of wetlands is a key contribution of this study to the science of wetland functional assessment. LiDAR data was used to estimate volumes for the 7,370 wetlands and delineate catchments for over 4,000 wetlands, located outside the 100-yr floodplain, within a 2,075 square mile area around Galveston Bay. Results from this study suggest that coastal prairie freshwater wetlands typically have a moderate capacity to store surface water from precipitation events, remove ammonium, and retain phosphorus and heavy metals and tend to have a high capacity for removing nitrate and retainremove organic compounds. The results serve as a valuable survey instrument for increasing the understanding of coastal prairie freshwater wetlands …

This paper presents an investigation of sedimentary deposition, soil formation, and pedoturbation in the Animas River Valley to determine the provenience of archaeological deposits in an open field at Aztec Ruins National Monument, NM outside of the Greathouse complex. Four stratigraphic pedounits correlated with active fan deposition have been proposed for the lower terrace in the project area with only one of these units retaining strong potential for buried archaeological deposits from the Anasazi late Pueblo II/Pueblo III period. The distal fan on the lower terrace and the Animas River floodplain appear to show poor potential for archaeological deposits either due to shallow sediment overburden with historic disturbance or alluvial activity during or after occupation. Based on these findings, four other zones of similar fan development have been identified throughout the Animas Valley and are recommended for subsurface testing during future cultural resource investigations.

In this paper, I will show how applying a spatially explicit context to an existing environmental health surveillance framework is vital for more complete surveillance of disease, and for disease prevention and intervention strategies. As a case study to test the viability of a spatial approach to this existing framework, the risk of human exposure to Lyme disease will be estimated. This spatially explicit framework divides the surveillance process into three components: hazard surveillance, exposure surveillance, and outcome surveillance. The components will be used both collectively and individually, to assess exposure risk to infected ticks. By utilizing all surveillance components, I will identify different areas of risk which would not have been identified otherwise. Hazard surveillance uses maximum entropy modeling and geographically weighted regression analysis to create spatial models that predict the geographic distribution of ticks in Texas. Exposure surveillance uses GIS methods to estimate the risk of human exposures to infected ticks, resulting in a map that predicts the likelihood of human-tick interactions across Texas, using LandScan 2008TM population data. Lastly, outcome surveillance uses kernel density estimation-based methods to describe and analyze the spatial patterns of tick-borne diseases, which results in a continuous map that reflects disease rates based …

A traditional two dimensional (planar) statistical analysis was used to identify the clustering types of North Central Texas traffic fatalities occurring in 2001-2006. Over 3,700 crash locations clustered in ways that were unlike other researched regions. A two dimensional (x and y coordinates) space was manipulated to mimic a one dimensional network to identify the tightest clustering of fatalities in the nearly 400,000 crashes reported from state agencies from 2003-2006. The roadway design was found to significantly affect crash location. A one dimensional (linear) network analysis was then used to measure the statistically significant clustering of flow variables of after dark crashes and daylight crashes. Flow variables were determined to significantly affect crash location after dark. The linear and planar results were compared and the one dimensional, linear analysis was found to be more accurate because it did not over detect the clustering of events on a network.

This research focuses on the global expansion of the transnational retail industry. Globalization is a phenomenon experienced by many industries in the present global economy. The global production network (GPN) framework can be used to explain and interpret the phenomenon of transnational firms' adaptation strategies. Due to market saturation in their home countries, retailers began to expand into East Asia in the 1980s. However, cultural differences and legislative limitations created barriers and restrictions for the transnational retailers making this transition. How do firms overcome these challenges? Through a case study of Costco in Taiwan, this research investigates the ways in which retailers adapt their strategies with regard to three concerns: site decisions, product mix selection, and supply network consolidation. The results shows that Costco opted for a strategy of lesser localization in all three domains. This research provides evidence to support this characterization along with examples of Costco's localization strategies via a case study and focuses on the issue of the balance between localization and standardization in the GPN framework.

Academic institutions have recently organized to address their campus' greenhouse gas emissions. Along those lines, the University of North Texas (UNT) pledged to minimize the campus' environmental impact, and conducted a transportation survey in May 2009. The analyses confirm that commuting to campus was the second highest source (29%) of UNT's greenhouse gas emissions, following purchased electricity (48%). Students, faculty and staff drive over 89 million miles per year, 84% of which comes from students. Forty‐two percent of student driving trips originate in the primary and secondary core areas surrounding Denton, which are partially served by buses. However, because these core areas are in close proximity to the campus, they contribute only 8% of the total student driving distance. Beyond the Denton core, the inner periphery of Denton County contributes another 22% of driving mileage. Students living in the outer periphery (outside Denton County) contribute the remaining 70% of total driving distance, and carpooling is currently their only alternative.

This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.

The analysis of protein residues recovered from archaeological artifacts provides a unique opportunity to reveal new information about past societies. However, many scientists are currently unwilling to accept protein-based results due to problems in method development and a basic lack of agreement regarding the ability of proteins to bind to, and preserve within, artifacts such as pottery. In this paper, I address these challenges by conducting a two-phase experiment. First, I quantitatively evaluate the tendency of proteins to sorb to ceramic matrices by using total organic carbon analysis and spectrophotometric assays to analyze samples of experimentally cooked ceramic. I then test a series of solvent and physical parameters in order to develop an optimized method for extracting and preparing protein residues for identification via mass spectrometry. Results demonstrate that protein strongly sorbs to ceramic and is not easily removed, despite repeated washing, unless an appropriate extraction strategy is used. This has implications for the future of paleodietary, conservation ecology and forensic research in that it suggests the potential for recovery of aged or even ancient proteins from ceramic matrices.