In North Texas, the Barnett Shale underlies large areas of the Dallas-Fort Worth Metroplex (DFW), which magnifies debates about the externalities of shale gas development (SGD). Continued demand for natural gas and expansive urbanization in DFW will cause more people to come in contact with drilling rigs, gas transport, and other urban shale gas landscapes. Thousands of gas wells within the DFW region occupy a large, yet scattered land surface area. DFW city planners, elected officials, and other stakeholders must deal with current and future urban growth and the surface impacts that are associated with gas development. This research examines how shale gas landscapes affect urban land uses, landscapes, and patterns of development in DFW. The study focuses on multiple fast growing DFW municipalities that also have high numbers of gas well pad sites. This study asks what are the spatial characteristics of gas well production sites in DFW and how do these sites vary across the region; how do gas well production sites affect urban growth and development; and how are city governments and surface developers responding to gas well production sites, and what are the dominant themes of contestation arising around gas well production sites and suburban growth?

Disease maps are powerful tools for depicting spatial variations in disease risk and its underlying drivers. However, producing effective disease maps requires careful consideration of the statistical and spatial properties of the disease data. In fact, the choice of mapping method influences the resulting spatial pattern of the disease, as well as the understanding of its underlying population characteristics. New developments in mapping methods and software in addition to continuing improvements in data quality and quantity are requiring map-makers to make a multitude of decisions before a map of disease burdens can be created. The impact of such decisions on a map, including the choice of appropriate mapping method, not been addressed adequately in the literature. This research demonstrates how choice of mapping method and associated parameters influence the spatial pattern of disease. We use four different disease-mapping methods – unsmoothed choropleth maps, smoothed choropleth maps produced using the headbanging method, smoothed kernel density maps, and smoothed choropleth maps produced using spatial empirical Bayes methods and 5-years of zip code level HIV incidence (2007- 2011) data from Dallas and Tarrant Counties, Texas. For each map, the leading population characteristics and their relative importance with regards to HIV incidence is identified …