The University of Arizona Geographic Information Systems Technology (UA GIST) integrates GIScience, cutting-edge GISystems, and geospatial technology, with management skills for use in government, corporate, non-profit, and academic settings.

This collection showcases the master's reports and projects from graduates of the Master's of Science in Geographic Information Systems Technology Program.

Submit Content

Graduating students are invited to submit their master's reports and projects each semester at the conclusion of their MS-GIST program.

Spring 2022 graduates can submit through May 31, 2022.

  • Log in to the repository using your NetID and password
  • Click the "Submissions" link in the left sidebar (under "My Account")
  • Start a new submission in the MS-GIST (Master's Reports) collection
  • You will receive an email with a persistent link to your submission when it is approved.

If you have questions about the submission process, contact us at repository@u.library.arizona.edu.


Contact UA GIST for more information about the Master's Reports in this collection, or about the UA GIST program.

Recent Submissions

  • Land Suitability Analysis of the Fredericksburg Viticulture Area in the Texas Hill Country

    Sánchez-Trigueros, Fernando; Teet, Stacy (The University of Arizona., 2022)
    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.
  • Using Earth Observations to Map the Spatial Distribution of Buffelgrass in the Sonoran Desert

    Lukinbeal, Chris; Olsson, Aaryn; Batres, Victor (The University of Arizona., 2022)
    The Sonoran Desert is recognized as an arid ecosystem with a year-round warm climate and biodiverse desert flora. The desert spans across the southwestern United States and northwestern Mexico. Much of the native flora, like the saguaro cactus (Carnegiea gigantea), are important members of the Sonoran Desert for native wildlife and human society. Currently, the ecosystem is being threatened by the rapid spread of an invasive grass species known as buffelgrass (Cenchrus ciliaris), as it is changing the desert landscape to a grassland and contributes to more flammable fuel of surging wildfires. This project, in partnership with the Tucson Sonoran Desert Museum’s “Save our Saguaros” initiative, utilized satellite imagery of Tumamoc Hill and Sentinel Peak from Google Earth to develop and assess an optimal workflow marking the spatial distribution of buffelgrass via manual mapping. This would aid in early detection and rapid response management not only within the study area but other areas of the Sonoran Desert. Geographic Information System (GIS) analysts worked with a predetermined buffelgrass identification key to manually plot growth sites of the species across the study site. Satellite Imagery from 2016-2020 was found to provide the best visual reference for historical buffelgrass growth and through remote mapping and ground truthing a significant accuracy level was achieved.
  • Crime in Tucson: Violence and Vulnerability

    Lukinbeal, Chris; Pells, Alexis (The University of Arizona., 2022)
    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.
  • Tunnel Fire's Effects on Northern Arizona 2022

    Sanchez, Fernando; Allen, David "Wil" (The University of Arizona., 2022)
    Flagstaff, Arizona has a diverse landscape and despite the regular monsoons and snowfall, Flagstaff is still subject to fires. The Tunnel Fire started on April 17, 2022 and burned almost 20,000 acres just outside of Flagstaff’s city limits. While the cause of the fire is still under investigation, authorities do not believe it was started by lightning. Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) will be used to analyze the health of the landscape before and after the fire occurred. In addition, hydrological modeling was performed to model theoretical watersheds of Flagstaff and its greater area. Then several of the theoretical watersheds are used to define the study area around the fire as watersheds can play a role in the spread or containment of fires. Then further hydrological modeling was conducted on the defined study area. Data for this project was obtained by the National Map, Sentinel 2A, Landsat 8, and Landsat 9. Upon completion of this project, the difference in the NBR or the dNBR showed a fire scar on the landscape and the NDVI where the fire took place had its values decreased significantly. Climate change is leading to more forest fires and managing the forests is of upmost importance in preventing and minimizing damage from future fires as climate change affects the wind patterns, rain patterns, temperatures, and the overall health of the forests.
  • Evaluating replanting priority using GIS: Proposed model for replanting California in the wake of wildfires

    Lukinbeal, Chris; Wade, Alexander (The University of Arizona., 2022)
    As the number and scale of wildfires in California continue to increase, so too has the amount of land in need of replanting. Severely burned areas run the risk of soil loss as unsupported soil formations are eroded by exposure to wind and water, damaging the environment and potentially endangering infrastructure. In order to reduce environmental degradation and potential secondary fire-related damage to infrastructure, a new geospatial model should be developed to help prioritize the most at risk areas for urgent replanting efforts. At the same time, due to the many stakeholders involved with these replanting efforts, such tools should be as easy and accessible for use. This Master’s Project aims to create an automated model that anybody with access to ArcGIS can use. Using publicly available data, the model produces a polygon file of the most at-risk areas in a given burn area on the basis of slope, erosion potential, burn severity, as well as recommended Forest Service species for replanting. Using the instructions provided with the model, the user will be able to locate the raw data necessary to generate a prioritized area file for their own fire incident and customize the model to suit their specific incident. While not a comprehensive tool, this model will provide a starting point for targeted post-fire replanting efforts. For anybody interested in the final model, downloads of the toolbox containing the model as well as a Python script version are available here: https://drive.google.com/drive/folders/1aTjOOsV-ILaLo0eGrZyRNqyWXNWyjkGU?usp=sharing
  • Land Cover Change across Barbados using Remote Sensing and GIS Technology

    Sanchez Trigueros, Fernando; Browne, Tia (The University of Arizona., 2022)
    This paper focuses on the use of GIS (Geographical Information Systems) technology to determine land cover change in Barbados between 2014 and 2021. The island has experienced drought and urban expansion over the years which has raised concern about the availability of arable land on the island. Data acquired from the U.S geological survey Earth Explorer portal for February 26th, 2014, and March 2nd, 2021, were used to compute the Normalized Difference Vegetation Index (NDVI) for both years. Supervised classification using Support Vector Machines was used to determine seven (7) identified classes and their changes over the eight (8) year period. Results from the NDVI showed a general decrease in healthy vegetation from 2014 to 2021. 43.22% of the island experienced vegetation loss with 56.52% having vegetation remaining unchanged. Interestingly, only 0.26% of vegetation experienced regrowth mainly in forested areas. The validation of the supervised classification method used yielded an overall medium level of agreement with between 64% and 67% accuracy. The greatest change in land cover was from bare soil/barren land to urban areas which accounted for 23.2% change. 10.4% of grassy areas in 2014 changed to urban areas in 2021 with less than 10% change from forest to urban and agriculture to urban.
  • Habitat Suitability Analysis for the Jaguar in the Amazon Biome of Brazil

    Lukinbeal, Chris; James, Riley (The University of Arizona., 2022)
    Jaguars are a keystone species of the Amazon Rainforest and they are suffering from habitat loss at an increasing rate. Brazil remains an integral portion of the jaguar’s habitat, specifically the Amazon Rainforest. This study performs suitability analysis on habitat within the Amazon biome to highlight suitable locations for potential future conservation units. Five variables were used for the analysis, high tree cover, wetlands, shrubs, human activity and terrain ruggedness. A weighted linear combination method was used to compare each variable and produce a map of suitable locations ranked from high suitable (1) to not suitable (4). A majority of the study area was considered high suitable at 80.02%,19.92% was medium suitable, .06% was low suitable and 0% was not suitable. The habitat suitability model was also compared to protected areas within the Amazon biome. When compared to protected areas within the biome, 73.61% of areas fell under high suitable, 26.36% were medium suitable and .04% were low suitable. The areas that fell outside of protected land were 86.52 % high suitable, 13.39% medium suitable and .09% low suitable. These results can lend conservationists, policy makers or other interested parties the groundwork on where to increase protection efforts and ensure the jaguar doesn’t suffer more habitat loss and fragmentation.
  • Forecasting Buffelgrass (Cenchrus ciliaris) Distributions In Southern Arizona Under Multiple Climate Change Scenarios

    Sanchez-Trigueros, Fernando; Hellmann, Patrick (The University of Arizona., 2022)
    Buffelgrass (Cenchrus ciliaris) is an invasive grass that can alter fire regimes, reduce local biodiversity, and convert complex arid ecosystems into buffelgrass dominated grasslands. As buffelgrass populations continue to grow, it will be important to be able to predict which areas are most susceptible to future buffelgrass invasion. This study attempts to provide some insight into this problem by creating a model to predict changes in the extent of potential buffelgrass habitat under different climate change scenarios between 2020 and 2100. Maximum entropy modelling was conducted using known occurrences of buffelgrass in the Santa Catalina mountains of Southern Arizona in combination with 19 bioclimatic variables from WorldClim to create a baseline model, which was then applied to future climatic conditions under the Canadian Earth Systems Model 5 (CanESM5) for three different climate change scenarios. The maximum entropy method produced an accurate model with an area under curve (AUC) value of 0.9913 and in validation trails it was able to accurately predict the presence of buffelgrass with 91.37% accuracy. When applied to future climatic conditions, the model showed a 280% increase in potential buffelgrass habitat under light and moderate climate change scenarios, and a 501% increase under a more severe scenario. Considering this potential for buffelgrass to spread, it may be essential for land managers to aggressively combat buffelgrass introductions to prevent it from being able to spread further and continue to damage ecosystems, as well as emphasize the importance of minimizing the impacts of climate change.
  • Mapping the Retreat of the Debris-Covered Tasman Glacier in the Aoraki-Mount Cook National Park, New Zealand

    Sánchez-Trigueros, Fernando; Garcia, Rose (The University of Arizona., 2022)
    As anthropogenic global climate change continues to accelerate, glaciers around the world are rapidly retreating. The Tasman Glacier offers a unique opportunity to demonstrate the challenge of mapping a debris covered glacier with a contemporary and rapid loss of ice at the terminus. Landsat 4, ETM+, and 8 OLI satellite Level-2 Reflectance imagery for years 1990, 2000, 2010 and 2022, are utilized for mapping the debris-covered glacier using a semi-automatic Support Vector Machine (SVM) classification. Normalized difference snow and ice index (NDSI) and normalized difference vegetation index (NDVI) are threshold and used as supplemental data for interpretation and optimization of machine learning training samples. To support delineation of the debris-covered glacier at the terminus location, slope data are derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (DEM). In addition to morphometric parameters, the DEM is used to calculate the glacier flow direction required to delineate the Tasman watershed. The 2013 Global Land Ice Measurements from Space (GLIMS) digital glacier outline is modified to delineate the Tasman Glacier System to derive the total area. Variability of the terminus retreat is quantified by area changes of the debris- covered glacier and proglacial lake. Post classification confusion matrices are computed to assess the quality and performance of the classified images. The overall level of agreement between the ground truth data and the predicted classes using the SVM classifier is strong, with an average Kappa statistic of 85 percent and average overall accuracy of 90 percent.
  • Measuring Ground Deformation Using Interferometry

    Summerlin, Tyler (The University of Arizona., 2022)
    The 2018 earthquake near Big Island, Hawaii caused landslides and ground deformation along the east coast. Ground deformation from seismic activity is of interest to scientists as it gives indications of volcanic activity below the Earth’s surface. Measuring this deformation can be challenging and typically requires Global Positioning System (GPS) monitors in place prior to an event to measure change, however, radar satellites provide a clear picture of wide scale movements. Interferometric Synthetic Aperture Radar (InSAR) is a collection method that compares Synthetic Aperture Radar (SAR) collections to measure vertical and horizontal ground displacement. This paper will outline the processes and methods used to process raw SAR data into an interferogram, a deformation map that precisely measures the ground shift after seismic events, glacial movements, or biomass change. Processing an interferogram started with reading raw radar collections from a SAR satellite such as Sentinel-1 and subsequently applying a series of conversions and transformations to create measurable data in the form of a displacement map. The calculated displacement indicates ground a sinking or downslope movement of -0.405 meters over the most active seismic area in Hawaii. The result from the interferogram and displacement map quantifies the effects of seismic activity and how InSAR can be used to accurately measure deformation for use in planning safe urban and infrastructure growth in areas of seismic activity.
  • Waterkeeper Alliance Coal-Ash Pilot Project: Development of Web GIS and Thermal Infrared Imagery Processing Tools for Preliminary Correlation Between Stream Temperatures and Proximity to Coal-Ash Waste

    Sánchez-Trigueros, Fernando; Ballantyne DeMaris, Raena (The University of Arizona., 2022)
    Coal-ash toxic waste is a “dirty energy” byproduct of coal-fired power that includes heavy metals and contaminants and is associated with climate change. Its disposal is poorly regulated in the United States; in some cases, coal-ash is discharged directly into rivers. Coal-ash contributes to elevated stream temperatures, adversely impacting living organisms and ecology. Waterkeeper Alliance, a non-profit organization that advocates for clean water, initiated a coal-ash project with three aims: creating web tools that aid waterkeepers in identifying coal-ash concerns; developing a tool that processes Landsat Surface Temperature data derived from thermal infrared imagery and prepares it for analysis; and exploring the correlation between water surface temperatures and proximity to coal-ash waste using Landsat data. Coal-ash data were transformed into web layers in ArcGIS Online and formed the basis of web maps and apps for waterkeeper use. Using ArcGIS Pro ModelBuilder, a model was built to process Landsat data in a Pilot Study Area (Missouri and Kentucky). In the model, high-quality water pixels are identified, converted to Celsius, and extracted as vector points with distance to coal-ash added as an attribute. The model concludes with a bivariate spatial correlation between water temperature and coal-ash proximity. In one iteration, ninety-four percent of data showed a geostatistically significant correlation between water surface temperature and coal-ash proximity. Twenty-four percent showed a negative linear correlation and thirty-seven percent showed a complex relationship. While the analysis affirms a non-random relationship between variables, the relationship appears more complex than two variables and Landsat imagery can explain.
  • Cartographic Survey of Golder Ranch Fire District

    Sanchez Trigueros, Fernando; Lawlor, Danny (The University of Arizona., 2022)
    The goal of this project was to complete a series of layouts for Golder Ranch Fire District (GRFD) to be incorporated into a report, Golder Ranch Fire District: Community Risk Assessment – Standards of Cover, created by the district as part of an ongoing accreditation process. Granted by the Center for Public Safety Excellence, the accreditation is centered around a thorough self-assessment that identifies strengths and weaknesses so that the fire district can better use its resources to serve the community. To this end, this capstone project provided a cartographic survey of GRFD, with maps and analysis displayed in layouts that featured a variety of assets and hazards. This was a collaborative project in which drafts were shared and revised until reaching satisfactory results. Ultimately, over thirty layouts were completed, covering everything from fire hydrant locations and water providers to wildfire risk and travel time analysis. As it would have been impractical for this paper to analyze all of the layouts as well as the methods involved in their creation, two layouts were selected for review: Senior Population and NWFD and TFD Stations That Can Reach GRFD within Eight Minutes. Looking at these layouts provided a window into the project’s methods and the cartographic techniques employed in a real-world application of the skills acquired as a result of completing the MS-GIST coursework.

    Sanchez-Trigueros, Fernando; Berg, Vladimir (The University of Arizona., 2022)
    The Southwest arid region of the United States is facing an unprecedented ‘megadrought’ which has resulted in a water crisis that threatens agriculture production and natural ecosystems. To observe and analyze the consequence of a decline in water availability, Sentinel-2 Images were compiled and analyzed based on NDVI values. These trends were analyzed in the Yuma subcounty in the state of Arizona, which is a center for agricultural production. A time-series was made using the powerful Google Earth Engine (GEE), a free-to-use cloud computing service, which can compile hundreds of images over time for analysis. The time series created plots all average NDVI values from Sentinel-2 images for the study area between January 2019 and June 2022. Additionally, four images were extracted from GEE and analyzed in ArcGIS Pro. Utilizing ArcGIS Pro’s built in raster analysis tools, one image for each year (2019-2022) were modified to display and assess the differences in NDVI values between the images. Based on the time-series, it is evident that NDVI values are trending downwards, indicating a decline in vegetation health for the Yuma subcounty. Observing the individual images, it is also clear that NDVI values are declining across the region, although more data needs to be collected on the ground to confirm this reduced vegetative productivity. Further study can be done annually using the highly detailed Sentinel-2 images to assess the impacts of drought and to analyze what changes can be made to agricultural systems in specific plots that may not be viable with less water availability.
  • New method to identify illegal uses of water by using remote sensing and neural network in Laguna de Aculeo, Chile

    Sanchez Trigueros, Fernando; Venegas-Quiñones, Héctor L. (The University of Arizona., 2022)
    The Aculeo lagoon basin has been declared an emergency drought place, limiting water usage strictly for domestic use. Chile's laws impose economic sanctions on individuals who use water resources to irrigate grass in these places. This project evaluates the healthy lawn condition in a specific dry season (period without rain events) to identify the areas that have potentially been using the water resource illegally by using multi-spectral and multitemporal free satellite data at the Aculeo lagoon basin. We derive different soil indices, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Moisture Index (NDMI), Moisture Stress Index (MSI), and Bare Soil Index (BSI) during October 2021 to April 2022. Also, we perform a cluster analysis to evaluate the statistical distribution of healthy vegetation cover. All the results are available in an ArcGIS interactive web map. This research proves some properties have probably used water to irrigate lawns because their health has maintained or increased over time. Thus, we estimate the lawn areas in the basin and their water consumption to illustrate how much water has been used illegally. In addition, the cluster analysis demonstrates a consistent pattern of healthy vegetation covers, concluding that these groupings are unusual compared to the entire basin. We present tools and protocols to be used in areas of water scarcity to identify locations that use the water resource illegally, helping governmental authorities to accomplish personal inspections and impose legal sanctions.
  • Modeling the Change in Distribution of an Endangered Lichen Species Under Projected Climate Conditions

    Sánchez-Trigueros, Fernando; Jones, Julia (The University of Arizona., 2022)
    Sulcaria spiralifera, or Dune Hair Lichen, is endemic to coastal dune forests along the Pacific coast in the continental United States. The species’ habitat is vulnerable to drought and temperature extremes. Modeling the possible impact of climate change can assist with conservation planning and bolster preservation of the entire ecosystem. This study investigates the impact of climate change on the distribution of a rare and endangered species by using the maximum entropy probability distribution principal to build a predictive species distribution model. The approach has demonstrated success in predicting the distribution of rare species that may include limited data and lack points of absence. The probable distribution of the species was modeled under current and historic climate conditions and used to train new models that would predict distribution under future climate conditions. Results of the project show a spatial change in habitat between 2021 and 2100 with suitable locations becoming more abundant. Positive changes in presence predict a shift inland while locations along the coast experience negative change. Despite an overall increase in suitable habitat, the predicted point of presence remains relatively stable with gradual increases around 2% every 20 years until a decrease of 4% between 2080 and 2100. Although the models show an increase in habitat suitability over time, it is unclear whether the Dune Hair Lichen could survive potential relocation as habitat shifts inland. The species distribution model under future climate conditions can help conservationists monitor and inventory the species to assess adaptation success.
  • Modeling Postfire Soil Erosion and Sediment Deposition on the Tonto National Monument with the Unit Stream Power Erosion and Deposition Model

    Sanchez, Fernando; Macias, Michael (The University of Arizona., 2022)
    A major consequence of wildfire events is the acceleration of soil erosion by surface runoff. During a rainfall event, soil may become detached, transported, and eventually deposited elsewhere on the landscape. One approach to predict whether and where this erosion process could occur requires determining six empirically established factors, namely, rainfall erosivity, soil erodibility, slope length, slope steepness, vegetation cover, and erosion management methods. This project analyzed these landscape factors on the Tonto National Monument, an archaeologically rich site containing 14th century cliff dwellings in central Arizona’s Tonto Basin. In the summer of 2019, over 80% of the monument burned, threatening its natural and cultural resources both from the fire itself and from the postfire erosion that followed. Chosen for its ability to predict both soil erosion and sediment deposition, the Unit-Stream-Power-Erosion-and-Deposition Model identified areas of the monument where the erosion process may have occurred and to what extent. This project used high resolution data to obtain each factor in raster format followed by further calculations based on changes in sediment transport capacity using a Geographic Information System (GIS) called ArcGIS Pro. The model predicted that 13.5% of the monument had high erosion, 27% moderate erosion, 15.5% low erosion, 8.7% stable, 3.2% low deposition, 6.2% moderate deposition, and 25.7% high deposition. Although this project’s methodology focused on the 2019 fire event, it offers resource managers on the monument an approach to monitor and mitigate potential future fire events, reducing costs and focusing efforts to areas of highest risk.

    Sánchez-Trigueros, Fernando; Garritson, David (The University of Arizona., 2022)
    Working in law enforcement is a challenging task, having the right tools available could mean success over failure. A proven method to identify and deter crime is mapping incidents from existing data to analyze and identify patterns. Small to medium size law enforcement agencies do not have the resources to utilize crime analysis mapping. Whether it is a matter of knowledge, time, staffing, or other factors; the benefits of mapping crime are unfortunately missing. By offering an understanding of the benefits of GIS, it will lead law enforcement agencies to use mapping in their crime analysis. With a clear understanding of where crime is being committed, policing trouble areas to reduce crime. Through creation of simple maps that depict criminal activity for a given area, it is possible to deter that crime. Learning how to utilize GIS tools that are currently available to prepare a visualization of crime in the form of a map, could lead to improved policing. Safer communities are possible with the proper training, a clear understanding of problem areas, and using mapping as a solution.

    Lukinbeal, Chris; Stanley, Gregory (The University of Arizona., 2022)
    Harmful Algal Blooms (HABs) are a natural phenomenon occurring 10-50 miles offshore, the size and concentration growing once they become more coastal. These blooms carry a neurotoxin that are not only harmful to marine life, but humans too, creating respiratory problems that could lead to death. Natural conditions such as the amount of sunlight, salinity, and temperature influence their survival and growth but there is also a human factor that accounts to their toxic state. Nutrient rich coastal runoff can attribute to the size and length of time of a red tide; this includes industrial and municipal waste discharge which contains nitrogen and phosphorus, key nutrients that the algae need to grow. This study focuses on an exploration of variables that may or may not affect the size and concentration of the HAB, Karenia brevis, which is responsible for red tides in the Gulf of Mexico, particularly in Florida. I used point data sets for the years 2015-2020 to create density maps that included salinity levels, chlorophyll concentrations, and three Toxic Release Inventory categories to see which affects HABs using Exploratory Regression. Additionally, a hot and cold spot analysis on these six datasets were tested to see if there was any high probability of occurrence around Florida. After running the regression tool, no passing models indicated any variables are related to HABs. However, the criteria VIF and Koenker (BP) of each model did pass as well as hot spots to occur in the Tampa Bay area and Cape Coral.
  • Seattle Crime

    Sanchez, Fernando; Bieler, Alec (The University of Arizona., 2022)
    The purpose of this Master Report is to spatially analyze violent crime rates in Seattle in 2020 to determine common demographic or locational relationships. I will be testing population subsets, number of households, mandatory affordable housing zones, unwanted land, and police station locations against crime using various types of analysis including spatial regression, heat maps, and bivariate maps. The data comes from Seattle City GIS including base map layers and 3,300 violent crimes. Population data came from the US Census Bureau. Preliminary results show a strong relationship between mandatory affordable housing and increased crime rates.
  • Housing & Race By Location Affordability in Washington State

    Lukinbeal, Chris; Stultz, Sierra (The University of Arizona., 2022)
    This Master’s Report focuses on how price-to-income ratio and race by location affordability affects housing in Washington State. Three types of analyses were used throughout this project. First, a price-to-income ratio using an affordability index was created to show areas of affordable to non-affordable housing. Price-to-income ratio calculates median home value divided by median household income resulting in an affordability ratio. The State of Washington has an affordability ratio of 5.6 and the ten highest ratios were in the following counties: San Juan, Whatcom, Chelan, Jefferson, Whitman, Skamania, King, Skagit, Kittitas, and Douglas. From county to block group level, King County tends to have the highest and most frequent affordability ratio in Washington. Cities and suburban areas tend to have a higher price-to-income ratio compared to the small-town rural areas. Second, race was added to a second affordability index. Race was compared to median home value through dot density and bivariate symbology to visually show race by location affordability. Third, race was compared to median home value and median household income through ordinary least squares linear regression to determine if there is a relationship which was shown. In terms of race by location affordability, majority of Washington State’s White population can afford a house compared to Hispanic/Latino, Black, Asian, American Indian or Alaskan Native, Hawaiian or Other Pacific Islander, and other races able to afford housing. The goal of this project is to bring further insight on where to focus efforts in providing equitable housing opportunities for racial disparities.

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