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.

Investigating aggravated assaults can be challenging for law enforcement officers. Crime analysts can make the process more efficient by uncovering valuable insights from data. Using only data from calls for service in 2024, this project analyzes crime patterns in Phoenix, Arizona. This study examines the distribution of aggravated assaults across the city of Phoenix using 2024 calls for service data to identify crime hotspots and peak times for these incidents. The study employed Excel and ArcGIS Pro for data preparation, filtering, and geocoding to isolate aggravated assaults. Methods for geospatial analysis, like Kernel Density Estimation and Hot Spot Analysis, were applied to locate areas with high incident concentrations. A temporal analysis, using the time and date fields in the data, was conducted to identify patterns related to when assaults occurred more frequently. These patterns were at specific times and days of the week. There was a significant concentration of aggravated assaults in certain areas in Phoenix. Aggravated assaults peaked on weekends, showing a clear temporal pattern. During high-risk periods, law enforcement should increase patrols in identified hotspots. In addition, the analysis offers recommendations for confronting the factors or issues that contribute to these crimes. This project demonstrates how public safety data can improve crime prevention strategies by leveraging spatial and temporal analysis. As a result, law enforcement can allocate resources more efficiently.

On September 7, 2024, Typhoon Yagi struck Northern Vietnam, severely affecting the coastal city of Hai Phong and neighboring Quang Ninh Province. The typhoon caused extensive flooding, significant infrastructure damage, and substantial economic and human losses. This research integrates remote sensing and GIS-based spatial analysis to evaluate the flood impacts and predict flood-prone areas. Sentinel-1 satellite data, processed using the Sentinel Application Platform (SNAP), was employed to extract flood-affected regions through pre- and post-crisis imagery differencing. Flood extent was identified by subtracting pre-crisis from post-crisis imagery, isolating inundated areas. Additionally, a predictive flood risk analysis was conducted using the weighted sum tool in ArcGIS Pro, incorporating seven key variables: DEM, terrain slope, drainage density, LULC derived from Sentinel-2 data, monthly rainfall data for September 2024 from PERSIANN-CCS, distance to roads, and distance to rivers. Each raster variable was reclassified into a standardized scale ranging from 1 (very low risk) to 5 (very high risk) to ensure comparability, followed by the assignment of weighted contributions to each variable based on its influence on flooding. The resulting analysis produced a flood risk map for the study area, highlighting the interplay of topography, hydrology, and infrastructure in flood dynamics. These findings offer critical insights for flood risk assessment, disaster response, and the development of mitigation strategies tailored to Hai Phong and Quang Ninh.

Effective climate change mitigation is complicated by economic factors, as renewable energy projects require significant upfront investment. Recently, the Bitcoin mining industry has begun investing in renewable energy, particularly projects powered by methane waste byproducts from other industries. These projects are promising for their low infrastructure needs and ability to reduce methane emissions that would otherwise be released into the atmosphere. Programs like the Endangered Species Act’s Cooperative Endangered Species Conservation Fund provide funding for conservation projects benefiting endangered species. These programs could support methane-fueled Bitcoin mining projects if a link between methane reduction and improved habitat for endangered species is established. This study used GIS to assess the impact of methane reduction on the breeding habitat of the California least tern, an endangered bird species. Using data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate modeling project, potential future habitat in 2050 was mapped under three projected climate scenarios developed by CMIP6 scientists. Alternative scenarios with 10%, 20%, and 50% reductions in methane emissions were then modeled using the Model for the Assessment of Greenhouse Gas Induced Climate Change (MAGICC) climate model for comparison with the unaltered versions. In two of the three scenarios, reducing methane emissions expanded the likely breeding habitat of the California least tern. However, in the third scenario, habitat suitability showed minimal change. This suggests that methane-fueled Bitcoin mining could be an effective climate change mitigation strategy, potentially improving habitat for endangered species, but further research is needed to confirm these findings.

The urban heat island effect poses a significant challenge for cities and urbanized areas, particularly those in warm climates. Vegetation, such as urban trees and parks, plays a crucial role in mitigating the effects of urban heat by helping to reduce land surface temperatures. This study explores the relationship between urban heat and vegetation in Albuquerque, New Mexico using remote sensing. To calculate land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and identify patterns of heat distribution and vegetation density across the city, the study used Google Earth Engine (GEE) to process Landsat 8 imagery, and ArcGIS Pro to integrate social vulnerability data and help determine what areas would be most impacted by new vegetation. Additionally, this study assessed the accessibility of this methodology for local governments, emphasizing its potential as a cost-effective approach to urban heat mitigation. By demonstrating the utility of GEE and freely available satellite data, this study provides a framework for municipalities to make informed decisions in combating urban heat and enhancing climate resilience. Results showed that there is a positive relationship between urban heat and areas of low vegetation, and GEE is a valuable tool to help government agencies tackle urban heat.