The exposure to extreme heat is both an environmental and a social issue. Youth populations are some of the most vulnerable to heat-related illness yet are often omitted in city plans and policies, further increasing their risk of exposure to the adverse impacts of extreme heat. A mixed-methodology approach using social mapping and plan evaluation was used to identify which areas within the greater Tucson area children are most vulnerable to extreme heat. The social mapping process identified a lack of services and areas where youth were particularly vulnerable to heat due to a lack of heat relief areas, like Flowing Wells and southeast Tucson. Evaluation of Tucson’s 2024 Heat Action Roadmap demonstrates the city’s clear mission to address heat resiliency equitably for vulnerable populations. Despite the inclusion of effective heat-specific strategies, very few were specific to youth populations.

While Arizona has abundant solar resources, policy decisions, regulatory changes, and the absence of state level incentive programs has limited the states solar adoption rate. This study examines if better solar education can change the way people in Pima County, Arizona purchase, manage, or make decisions about solar energy technologies. With the counties high solar irradiance of almost 300 days of sunshine (80%) a year, solar power has serious potential, but adoption remains limited due to knowledge gaps, economic barriers, and policy challenges. This longitudinal survey was conducted across two semesters of Fall 2024 and Spring 2025, and there were 68 participants in the first survey and 74 participants in the second survey. Results show that better solar education is supported by over half (56%) of the participants surveyed, because they believe better solar education improves solar purchasing/management decisions. The interviews confirm the survey results, with cost savings as the main consumer driver, distrust in some solar companies, and the affordable option of Power Purchasing Agreement’s for people who cannot afford the cost of solar installation. The study further finds that solar education plays an important role in boosting solar adoption, addressing misinformation, and improving policy. The public’s support for solar education, incentives/rebates, and energy conservation programs, says that Pima County could greatly expand its clean energy sector, providing jobs, and clean energy for the future generations of this region.

Food forests are layered ecosystems that improve biodiversity through permaculture techniques, food security, environmental health, and community empowerment. Many food forests have been created throughout the country and world but often are designed based only on what grows native in that region. However, to create a successful food forest for the city, it must meet the needs of the residents. Data was collected from residents of Goshen, Indiana, through surveys, systematic document review, and observational analyses to find the most valuable produce to be included in the food forest design. This data provided context for resident demographics and locations, food growth interest, and preferences for a community space. Case studies of other food forests, reports, and weather data emphasize the value that vegetation has on communities, the importance of local businesses, and how growing conditions can be utilized for successful yield. The study found that many fruits, vegetables, and herbs that Goshen residents prefer can grow in the Northern Indiana climatic region and the community space should prioritize water, seating, and open space. Through utilization of collected data from residents and information from case studies of food forests, a sustainable design for the site can be created. Localism, walkability, and health are priorities for Goshen residents and the implementation of a food forest and community space in the city can provide support for the diverse populations through free and accessible healthy produce.

As the population expands, interactions between humans and wildlife are increasing. In Tucson Arizona, bobcats, mountain lions, coyotes, and javelinas are common visitors, and while not all interactions between humans and animals are negative, negative interactions do happen. There are many ways to decrease wildlife-human interactions and wildlife crossing is one of them. Using a mixed method approach based on case study examination and secondary data analysis, this research aims to identify areas where wildlife crossings in Tucson should be placed. Using data from iNaturalist and the Pima County Open Geodatabase on wildlife sightings, roadkill locations, and tree equity scores, multiple maps were created in ArcGIS Pro to locate areas where wildlife crossings are needed in Tucson. The results showed significant sightings of bobcats, mountain lions, javelinas, and coyotes in Tucson, Arizona. A lot of them are in the upper half of Tucson, closer to current wildlife crossings as well as areas with high tree density. This is important because while there are quite a few wildlife sightings in Tucson, there are no wildlife crossings in Tucson. Some policies that can be added to decrease wildlife-human interaction include a ban on leaving pet food outside or adding a wildlife feeding ordinance that keeps humans from feeding wildlife. Further research can examine why wildlife are attracted to certain areas in Tucson and the potential of creating wildlife crossing in areas with high wildlife sightings and deaths.

This article presents a case study analyzing the opportunities provided by the Portland open data repository on vehicle speed to explore the relationship between vehicle speed and road safety. While the influence of vehicle speed on road safety has been well-documented on highways and freeways, where free flow conditions are generally uninterrupted by pedestrians or bus stops, this study shifts focus to urban core roads, which include arterials and collector roads. These types of roads account for 69% of road fatalities in the U.S. and are characterized by a higher density of diverse road users, making the interaction between vehicle speed and safety more complex. Using Geographically Weighted Poisson Regression (GWPR), the study examines the associations between vehicle speed and fatal road crashes at the block group level. The goal is to assess the significance of vehicle speed in predicting fatal crashes while identifying spatial variability across the city. This analysis aims to provide insights that could inform localized interventions, particularly in ethnically diverse areas that disproportionately bear the burden of road fatalities.

Wildfires pose a significant threat to natural resources, communities, infrastructure like homes on the Fort Apache Indian Reservation. This project developed a GIS- based wildfire risk assessment model utilizing available data and analytical tools in ArcGIS Pro. The analysis incorporated key environmental variables including fuel models from LANDFIRE, topographic features derived from USGS Earth Explorer, and proximity to communities. A weighted overlay approach was applied to classify areas into no risk. low, moderate, high and extreme wildfire risk zones. By adapting methodologies like kriging and weighted overlay, this study has ensured a replicable and objective assessment and framework. The final wildfire risk maps are able to support land managers in prioritizing mitigation efforts and resource allocation for planning and emergency response efforts.

Efficient and adaptive evacuation routing is essential for public safety during disasters such as wildfires, floods, and debris flows. Many traditional evacuation planning methods lack real-time adaptability and fail to account for road closures, congestion levels, and network constraints. This project develops an automated evacuation routing model using ArcGIS Network Analyst, integrating geospatial analysis techniques to generate optimized evacuation routes based on user-defined evacuation zones. A web-based application enables emergency managers to define evacuation zones by drawing a polygon, which triggers the routing model to compute optimal evacuation routes in real time. The model incorporates road closures, restricted access roads, and functional classifications to ensure that only available and suitable roads are used for evacuation. By analyzing residential parcel densities within the evacuation zone, the system assigns congestion penalties to road segments, dynamically influencing optimal route selection. The script automatically identifies exit points at the boundary of the evacuation zones where roads provide safe egress, ensuring logical and efficient evacuation paths. The model was tested using a road network dataset for Santa Barbara County to evaluate its effectiveness in real-world scenarios. This framework is scalable and adaptable, allowing emergency managers to tailor evacuation planning for various disaster scenarios and apply the model to different geographic regions and network datasets. By leveraging network analysis, GIS automation, and interactive web mapping, this project enhances disaster preparedness and response efforts, providing a flexible, real-time evacuation planning tool that supports data-driven decision-making and ensures safer and more efficient evacuations.

Debris flows are all too common in Southern California, especially in burned areas after a wildfire. This project predicts and informs of potential areas where a debris flow is likely to happen in the 10 counties of Southern California. Using digital elevation model data from the United States Geological Survey (USGS) and water catchment data from the United States Geological Survey’s Elevation Derivatives for National Applications (EDNA) database, the average slope was found in each water catchment, as debris flows follow the flow direction in the catchment. Using the National Interagency Fire Center’s fire perimeter data, recently burned areas were identified, as they are more susceptible to debris flows with the lack of vegetation holding the soil in place. Additionally, the National Weather Service’s National Digital Forecast Database (NDFD) was used to track predicted rainfall accumulation over 72 hours. This live data updates in 6-hour intervals, closely monitoring how much precipitation is expected. A model was created to analyze the data and make predictions on what is considered a no risk, low risk, medium risk, and high-risk area. An ArcGIS Dashboard was created to publicize the data, which provides information about what areas are prone to a debris flow, to help evacuate people and help utility crews with prevention and cleanup measures.

Wildfires have become increasingly more common in much of the West, posing a large threat to both human and natural resources. This study uses a variety of spatial statistical methods to investigate the patterns, trends, and causes of wildfires across the state of Idaho. Leveraging tools such as the Global Moran’s I and Hot Spot Analysis to identify significant clusters and emerging trends of fire patterns, Standard Deviational Ellipses were also employed to show the dispersion and orientation of wildfire occurrences. Regression analysis, including ordinary least squares (OLS) and geographically weighted regression (GWR), was used to examine how fire management practices influence the spatial patterns of wildfire incident sizes. The results indicate that human and natural-caused fires exhibit reverse patterns: human-caused fires tend to be smaller but more costly, whereas natural-caused fires tend to be larger but less costly. Unsurprisingly, human fires tend to be concentrated around higher population densities, while natural fires tend to occur in more remote areas. These findings highlight the importance of more localized and data-driven approaches to wildfire management and policy decision-making.

Subsurface water on Mars is key to understanding its geological evolution, climate history, and potential habitability. This study examines 1.5 billion radar signals from the Mars Advanced Radar for Subsurface and Ionospheric Sounding (MARSIS), a low-frequency radar sounder aboard the European Space Agency’s Mars Express orbiter, focusing on the strongest 5% of signals to detect subsurface structures. All 1.5 billion points were evaluated using clustering and statistical methods to detect large subsurface structures up to five kilometers deep, with more than 424 million points forming significant clusters. Using advanced signal processing (e.g., clutter suppression, noise filtering) and geospatial analyses (e.g., spatial clustering, autocorrelation, hotspot analysis, and cross-partition connectivity), the study identifies significant subsurface structures. The findings are validated by NASA’s Shallow Radar (SHARAD) data confirming known shallow ice deposits in mid-latitude regions like Utopia Planitia. Integrated into a GIS framework, these visualizations advance planetary science, astrobiology, astrogeology, and Mars exploration by enabling in-situ resource utilization (ISRU) and informing landing site selection. This study pioneers a GIS-based approach to globally map Martian subsurface water, integrating MARSIS and SHARAD data.