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 …

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.