The highly publicized 2013 Yarnell Hill Fire is the deadliest wildfire in Arizona history, killing 19 members of the Granite Mountain Hotshots firefighting crew. Wildland fires like Yarnell Hill have immediate effects on human life and property, but they can also increase the frequency and severity of flooding events due to loss of vegetation and hydrophobicity of soils and ash. This study seeks to model hydrological impacts due to land cover change following the Yarnell Hill Fire using the Automated Geospatial Watershed Assessment (AGWA) Tool. AGWA can enable hydrologic modeling using the Soil & Water Assessment Tool (SWAT) or the Kinematic Runoff and Erosion Model (KINEROS2) and can help land and water resource managers make quick decisions regarding flood mitigation strategies following a wildfire. In this study, AGWA is used to model the change in land cover due to the Yarnell Hill fire based on a burn intensity map created using the differenced Normalized Burn Ratio (dNBR) and based on pre- and post-fire Landsat 8 Operational Land Imager (OLI) imagery. Storm conditions included in the model represent a variety of storm recurrence intervals based on National Weather Service (NWS) data for the town of Yarnell. For all post-fire storm conditions modeled, flooding increases more rapidly and with greater volume as compared to pre-fire conditions.

Flooding events have caused many considerable losses to individuals and businesses all over the United States. Floods are the most frequent severe weather threat and the costliest natural disaster in the nation. Ninety percent of all natural disasters in the country involve flooding. In order to effectively mitigate and prevent flood disasters, flood risk and flood management needs to be implemented in areas that are vulnerable and susceptible to flooding. The area of study will be the Navajo Nation region in the state of Arizona. Analysis and evaluation of flooding events near Marble Canyon and the city of Winslow between the years 2010 and 2020 are the main objectives of this project. Flood susceptibility analysis in ArcGIS Pro can determine the risk of flooding near these areas and how it affects their geology and economy. Major flooding of these areas has caused severe damage to roads, homes, crops, businesses, schools, hospitals, and emergency services. The following factors are included in the flood analysis, slope, hydrology, rainfall, and land cover. Using the reclassify and weighted overlay tools in ArcGIS Pro for each of these factors, a flood risk map of each area shows the severity of flood risk. Each flood risk area has an allocated value from “very low,” “low,” “medium,” “high”, and “very high” flood risk. This shows what areas are more vulnerable than others in both regions of the Navajo Nation. These results can be used to highlight areas that require planning and action to avoid or mitigate damages and loss of lives.

The depression era Home Owners’ Loan Corporation (HOLC) bought troubled mortgages from banks and refinanced mortgages with borrowers directly. The HOLC also graded neighborhoods by risk of mortgage default using racist criteria in a process now commonly called redlining. Scholarly work indicates that redlining resulted in adverse socioeconomic conditions in the decades since the depression. This study examines the possible long-term effects of redlining in the Twin Cities, Minneapolis and St. Paul, Minnesota, using decennial census and American Community Survey data. Trends were identified using socioeconomic measures at the census tract level in four areas: population, housing and rent, employment, and income. Analysis including geographically weighted regression identified significant variation in measures between best and worst rated tracts. As a percentage of a tract’s population, non-White population grew while the White population declined. However, non-White population grew mostly in census tracts with the lowest HOLC grades. Home values in the lowest graded tracts increased over time yet lagged well behind better graded areas. Median rents in worst graded tracts doubled when compared to other tracts. The best graded area outpaced median income growth in all other tracts. Generally, the influence of HOLC grades was less in later census years. While regression analysis of median home value using the selected census data failed to provide a reliable predictive model, useful explanatory variables were identified for future study. Overall, the results show that multiple adverse socioeconomic conditions continue to exist in formerly redlined areas.

The purpose of this project is to analyze the relationship between tree canopy and violent crime in the city of Austin, Texas. Using only recent 2022 data on violent crime and the most up-to-date data on tree inventory in Austin, a Heat Map was generated to assess the density of the crime data source. The Aggregation of Points was made to assess the density of tree inventory. A Tree Priority data map was also used to build upon by joining the crime data to it and creating a Bivariate Choropleth map from the output. Median Household Income was analyzed against crime to determine whether low-income households are areas that might benefit the most from the planting of more trees. Furthermore, a Pareto (80/20) analysis was created, where violent crimes were aggregated based on the closest input comparison polygon feature. Police station locations, policing districts, and parkways were also used to provide background features when analyzing the results. Data came mostly from the Austin Texas Open Data Portal and from the US Census Bureau. Preliminary results show an inverse relationship between tree canopy and crime rates. Ergo, where crime rate was high, tree canopy in that area was at its lowest. Results also indicate that the low-income households are in high need of tree priority compared to higher-income households. The results described in this analysis can help identify areas that may require more extensive attention from law enforcement agencies and establish better community effort to plant more trees in Austin.

Over 250,000 free roaming domestic dogs are estimated to live on Navajo Nation land, raising animal welfare and public health concerns. Due to the high dog to human ratio in Navajo communities, dog bites are a common injury. Free roaming dogs can contract and spread rabies after interactions with infected wildlife, and less than 20% of dogs living on the Navajo Nation are vaccinated against the disease. Research on oral rabies vaccines emphasize how important efficient delivery methods are in adequately vaccinating enough dogs to reach herd immunity. Suitability modeling can further improve vaccination rates by mapping preferred habitat to locate unowned dogs. This research-based project aims to model suitable locations for oral rabies vaccine delivery using the geographic information systems software ArcGIS Pro. Suitable habitat in the study area is based on proximity to human development, preferred land cover, and water sources - as documented in existing free roaming dog research. Habitat is combined with Boolean overlays for slope and road buffers, to determine valid delivery sites. The results indicate 648 of 1663 square miles, or 38.96% of the total study area, is suited for both free roaming dog habitation and oral rabies vaccine delivery. Within this habitat, two main areas have the highest suitability scores; totaling 140 miles, or 8.41% of the study area. Conducting a local suitability analysis before implementing oral vaccine deliveries could improve efficiency by eliminating areas unsuitable for free roaming dogs. Determining suitable habitat may lead to the discovery of more dogs, especially when dogs may be unrestricted or completely feral.

In this project, I describe a method for automating historical land use/land cover change analysis for the Northern Arizona, greater Flagstaff area. The method investigates deforestation as a result of urban growth, the results of which are displayed in a timeseries. The area surrounding Flagstaff, Arizona is interspersed with areas of urban development as well as a diverse population of spruce, fir, and pine trees. In recent years, more forest and grassland areas have been removed to make room for more urban development, both within and outside the city limits. I obtained one surface reflectance raster image per year taken in Spring from 2022 back through 2000. These scenes were obtained from the United States Geological Survey Earth Explorer data collection and were captured by the Landsat 8 Operational Land Imager/Thermal Infrared Sensor and Landsat 5 Thematic Mapper satellites. After initial processing, scenes were classified using an unsupervised ISO Cluster classification technique. The land cover classifications were identified via manual interpretation techniques aided by high-resolution, Google Earth historical imagery. Analysis of these classifications provided land use and land cover data for understanding the recent extent of urbanization and deforestation in this region. The results of this study demonstrate approximately 3,946.6 acres of forest were lost to urban development, between 2000 and 2022, which equates to a 15.44% loss in forest acreage. In addition, ArcGIS Pro Model Builder models were developed to allow for a reproducible method of performing similar analyses in other study areas.

This paper examines the progress of the COVID-19 virus through the Southwestern United States and the factors that raised or lowered the infection rates with a particular focus on the response of state and county governments. In it, this project shows that preventative measures being implemented only partially changed the rate of infection in the study area and that seasonal trends impacted the spread of the disease as well. This paper will also show that there were two major seasonal spikes in COVID-19, a summer spike and a winter spike, and prove that the temperature fluctuations associated with the changing seasons triggered surges in COVID-19 infections.

Greenspace is an area of vegetated land (grass, trees, shrubs, etc.) within an urban context. Green spaces can be community gardens, parks, common land, playing fields, and green corridors like exercise paths, rivers, and canals. Green spaces play an important role in an urban ‘ecosystem’ by providing a place for physical activity, relaxation, social interaction, community events, and so on. In high-density urban areas, green spaces can provide a place relatively free from air and noise pollution. Green spaces with water features can play a critical role in cooling cities. Maricopa County added more new residents than any county in the nation from April 1, 2020, to July 1, 2021, according to the Census Bureau. With the growth of the population and many new developments, it is important to determine the best placement for new green spaces. This analysis uses GIS processes to perform a site suitability analysis that locates potential sites for new green spaces within Maricopa County. The first part of this analysis performs a Boolean Suitability Modeling which identifies areas best suitable for new green spaces based on different criteria. The criteria for making a new green space are more suitable areas further away from existing parks, unused/undeveloped land, higher populated areas, lower income areas, and areas closer to public transportation routes. The second part of the analysis performs a weighted suitability analysis. The outcome of this project will provide a roadmap for the county to determine areas best for new green spaces and to meet the community’s needs.

In the last 50 years, commercial vineyards in Texas have increased to more than five hundred. Wine production has tripled since 2012, making Texas the fifth largest wine producer in the United States. Like California’s Napa Valley, the Texas Hill Country is ripe for agritourism and wine cultivation bringing millions of visitors and billions of dollars to the state annually. Vineyards continue to increase, but most new owners lack agricultural experience. Due to its unique climate and lack of historical data, Texas growers and winemakers are still determining the best use of terrain while navigating harsh weather and regional hazards. Proper site selection is crucial. Spatial analysis of climate, soil and terrain characteristics was used to determine variables with the most impact on land suitability in the Fredericksburg viticulture region of the Texas Hill Country. Geospatial software was used to create a weighted overlay model of potential variables. Surface analysis found aspect, slope, solar radiation, flood frequency, drainage class, current land usage and available water storage to be statistically significant to this study. Potential areas were ranked on a scale of one to five, with one being permanently unsuitable and five being highly suitable for viticulture. Results found 594 acres or 27% to be highly suitable, 1,158 acres or 53% to be moderately suitable, and 430 acres or 20% not suitable for viticulture. Results of this study could help growers select prime areas for viticulture, but site-specific climate, environmental, and varietal specific factors should also be taken into consideration.

Crime throughout the Tucson city area reaches six figures every year. Over ten percent of these crimes are considered to be violent: murder, aggravated assault, rape, and robbery. It is a widely accepted belief that violent crime is a factor of vulnerability in a neighborhood and can be found in conjunction with certain socioeconomic factors. In 2020, a study conducted by the University of Arizona and the City of Tucson determined that five major socioeconomic factors determine the vulnerability of a neighborhood. These factors did not include crime, but the percentage of residents identifying as anything other than “non-Hispanic white alone”, percent of households who rent, rather than own, their homes, percent of residents aged 25 and over who lack a four-year bachelor’s degree or higher, percent of households with incomes below 80% of the Area Median Income (as determined by HUD), and the share of children that live in households below the official poverty line. This Master’s Project analyzes the five major socioeconomic factors along with violent crime statistics to determine whether vulnerable neighborhoods are also victims of violent crime. The analysis consists of City of Tucson crime reports between 2019 and 2021, spanning the time before and after the study was done to show that neighborhood vulnerability factors and violent crime are statistically significant to each other. Using spatial autocorrelation and regression analysis and ESRI’s ArcGIS Pro, violent crime can be associated with almost all factors of what is considered a vulnerable neighborhood. Analyses conducted include Kernel Density, Average Nearest Neighbor, Global Moran’s I, Geographically Weighted Regression, and Exploratory Regression. The results will be able to aid the City of Tucson in furthering its efforts to prevent violent crime throughout the city and aid the neighborhoods that need the most help.