UC GIS Week

Chasing the Sun: Mapping Solar Geometry and Radiation with Sun Compass app

Sun Compass is a web-based geospatial application that visualizes solar position, radiation, and atmospheric geometry across space and time using live and historical NOAA datasets. Designed to make complex solar-radiation modeling more intuitive and accessible, the tool enables users to explore how sunlight interacts with geographic features, weather conditions, and temporal factors such as time of year or day. By integrating meteorological, astronomical, and topographical data, Sun Compass creates interactive carpet plots and SVG-based solar diagrams that dynamically represent changes in azimuth, elevation, and irradiance throughout the day.

Being developed as part of an independent research study at the University of California, Santa Barbara under the mentorship of Professor Diba Mirza, this project brings together computer science, GIS, and data visualization to promote environmental understanding through open-source technology. The presentation will cover the system’s architecture, including the data ingestion pipeline, computation of solar geometry, and real-time rendering process, as well as the design principles used to make scientific data approachable for both researchers and the general public.

Planned development of Sun Compass aim to incorporate machine-learning-based irradiance prediction models, terrain-based shadow analysis, and API endpoints for integration into renewable-energy and sustainability workflows. By bridging scientific rigor with intuitive visualization, Sun Compass serves as both a pedagogical tool and a prototype for how web technologies can democratize environmental data access, supporting climate research, urban planning, and renewable-energy analysis.

Community Knowledge Exchange for Future Air-Taxi Integration

MATLAB model that graphs prospective air-taxi routes onto a map of the Irvine area, and calculates important factors for consideration like distance, energy, and cost of flight. The map has an adjustable zoom, and the user input is in the form of clickable or typeable waypoints that the UAV will fly over in its path. The user can also adjust the altitude of the UAV at each waypoint to mimic take-off and landing, for more precise estimates, and there is an optional flight animation in the perspective of the UAV to get a better idea of the path and any obstacles that might be on it. This model is designed to be equally accessible to researchers judging the efficiency of a flight path and communities that want to become more informed on the functionality and limitations of air-taxi flight.

Making Autonomous Vehicle Crash Data More Accessible

Autonomous vehicle (AV) manufacturers testing in California are required by the Department of Motor Vehicles (DMV) to obtain either an Autonomous Vehicle Tester (AVT) or Autonomous Vehicle Driverless Tester (AVDT) permit to operate on public roads. As part of these testing programs, manufacturers must report their monthly and annual miles traveled, as well as any crashes resulting in property damage, injury, or death. UC Berkeley SafeTREC has analyzed these datasets to create two interactive dashboards that make AV testing data more accessible and provide insight into AV operations in California. All crashes reported through August 26, 2025, have been geocoded, and summary charts have been developed to visualize crash trends over time. In addition, SafeTREC has compiled statistics and visualizations summarizing monthly and annual mileage by manufacturer. Together, these analyses are presented in the AV Safety Dashboard, two user friendly, point and click tools that allow users of varying technical abilities to access the AV crash data and AV mileage data in a simple way. The dashboards aim to increase transparency of and public access to AV testing data so that users understand where, when, and how AVs are operating in California.