Natural gas exploration in the U.S. has increased dramatically over the last several years due to advances in hydraulic fracing. With new technology, natural gas extraction in areas such as the Marcellus formation in the eastern U.S. has become economically viable. The Seneca Resources Corporation uses subsurface data to locate the best possible locations for natural gas well pads in the Marcellus region of Pennsylvania. However, such a strategy may fail to account for environmental surface features, which may be subject to local, state, and federal regulations. This project aimed to produce a site suitability model and web-based application capable of allowing Seneca geologists to consider surface features and environmental and government regulations when choosing a well pad site. The suitability tool was developed using ArcGIS ModelBuilder and incorporates data on streams, wetlands, terrain, and threatened and endangered species. The model was published to the Seneca Resources GIS Portal and allows users to interactively view the suitability of well pad sites in the Marcellus region. This tool can be used to protect sensitive areas while supporting natural gas extraction in the region.

: Many local governments collect and maintain data that can be used to evaluate the development potential of vacant land. Establishing a GIS architecture capable of housing this data and performing a suitability analysis, however, can be costly. This project utilizes affordable open source GIS software to model the suitability of vacant parcels in Pima County, Arizona, and to develop an interactive online application. The open source system utilizes data from public sources and transforms and analyzes it using integrated geospatial methods and structured query language (SQL). The interactive online application communicates up-to-date suitability to a target audience of market researchers tasked with locating, assembling, and ranking parcels for potential acquisition and development. This project demonstrates that it is possible to use affordable open source GIS to evaluate the suitability of vacant land and to provide updated data for land and development research.

Wildfire presents a significant threat to life, property, and natural resources. This study explores how remote sensing applications can be employed to estimate the risk of wildfires in Northern California. The focus of this study will be on Shasta County as well as subsets of Northern Tehama County and Eastern Trinity County. The severity of wildfire is a function of elements within the fire shed such as vegetation cover, solar insolation, and vegetation moisture and biomass. Previous literature has made extensive use of various remote sensing applications to assess the threat of wildfire ignition and spread by identifying these fire shed elements that pose the greatest risk. This study will examine supervised and unsupervised classifications of vegetation species using a number of different spectra to determine which is most accurate. Using regression analysis, the most accurate of these classification systems is grouped with solar insulation as well as several NDVI raster’s as dependent variables to estimate wildfire size. Regression results indicate that the classification process can successfully identify areas of vegetation that increase the likelihood of large wildfire and potentially wildfire management agencies in identifying wildfire hazards.

When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods, employment and economic health, transportation and mobility and health and the environment.