UC GIS Week

Cautions to GIS Professionals in Context of California Statutes Defining the Practice of Surveying

Navigating the boundaries between land surveyors, engineers, professional geologists, and geospatial professionals is crucial for maintaining the integrity of each field. Although land surveying and geospatial work complement each other, they differ significantly in scope and focus. In California, surveyors are licensed by the California Board for Professional Engineers, Land Surveyors, and Geologists and the activities exclusively reserved for Professional Land Surveyors are defined by the Professional Land Surveyors’ Act as codified in Section 8700 - 8805 of the California Business and Professions Code, specifically Section 8726 (a). GIS practitioners are at risk of being fined if their mapping work strays into the Surveyors’ domain. The California Geographic Information Association (CGIA) and the California Chapter of the Geospatial Professional Network (CalGPN) have launched an effort to inform GIS Professionals of this risk, to engage with the Surveying Community, and to advocate for clearer regulations. The speakers will focus on the recommendations document that was published in April 2025, and the current status of the work of the joint ad hoc committee led by CGIA and CalGPN.

Rural Atlas highlights differences in cancer incidence, proportion late stage, and survival using two different definitions of rurality

Background: Use of different measures of rurality has made it challenging to identify differences in cancer patterns and direct cancer control efforts toward rural regions. The UCSF Rural Atlas allows users to dynamically visualize differences in cancer epidemiology across the rural–urban dichotomy, specifically within the UCSF Helen Diller Family Comprehensive Cancer Center (HDFCCC) catchment area. Methods: Percent rural and Rural Urban Commuting Area (RUCA) codes were selected to explore cancer rates within the catchment area for the five most common cancer sites (female breast, prostate, lung, colorectal, and melanoma). Relevant area-level demographic and cancer risk factor data were sourced from the American Community Survey (ACS) 2015-2019 and CDC Places (2023 release). California Cancer Registry 2018-2022 data were used to generate incidence rates, percent late stage, and 5-year overall survival. Results: The Rural Atlas (https://cancerregistry.ucsf.edu/rural-atlas) allows users to select measure of rurality, sex, and cancer site and explore the corresponding maps, tables, and figures. Using the Rural Atlas, we observed some regional differences between rural and urban areas. Prostate cancer incidence is lower in rural areas compared to urban areas in the Central Coast using the RUCA definition but higher using the percent rural definition. Male and female melanoma incidence was higher in rural areas using the percent rural definition, but using the RUCA definitions, lower rates of melanoma in rural areas of the Central Coast were observed. Proportion of late-stage prostate cancer was higher in rural areas of the San Francisco Bay Area, especially when using the percent rural definition. Male lung cancer survival was higher in rural areas using the percent rural definition, but lower using the RUCA definition, especially in the Central Coast. Conclusion: The Rural Atlas is a flexible tool for highlighting disparities in cancer patterns by rurality across regions and informing cancer control efforts.

How to use Health Atlas: A Visualization Tool and Data Resource for Place-Based Drivers of Health

Where we live, including the physical and social environments that surround us, influences our health in both positive and negative ways. Examining factors that vary across time and space such as access to food and housing, air quality, and social support can help reveal the specific effects environments have on health and inform neighborhood-based interventions. We designed and developed Health Atlas (healthatlas.ucsf.edu) to help users explore place-based factors that might influence health. Health Atlas is a curated public repository of data aggregated at different geographic units (e.g., census tracts, zip codes, counties) and across a wide variety of domains (i.e., demographic; socioeconomic; built, social, and structural environments; environmental exposures; health behaviors; healthcare access and use; and health outcomes). Most data are available across all 50 states in the U.S., plus D.C. and Puerto Rico. Health Atlas provides user-friendly tools for mapping and visualization, and a data download feature to support use of the data by researchers, community organizations, government entities, and public health professionals. We propose a virtual workshop to teach potential users of Health Atlas how to use the tool for their work. We will include content on: - Health Atlas 200+ variables - Selecting variables - Visualizing data - Selecting custom areas - Downloading data - Tips and tricks on how to explore data

The UCSB rAtlas

The UCSB rAtlas is an Open Source project to mimic Data Carpentry's Introduction to Raster and Vector Data. Solutions to each episode of the Carpentry lesson are recreated using data sources local to the Santa Barbara campus. Started as a team-building exercise, the Atlas has provided the UCSB Library DREAM Lab solid experience using R with geospatial data--an increasingly common alternative to ArcGIS and QGIS. It has also solidified our ability to teach the Carpentry Lesson as it is written as well as take a leadership role with the evolution of the lesson.

The atlas is downloadable to run at: https://github.com/UCSBCarpentry/ucsb-ratlas

Mapping the Tren Maya’s Mark on Mérida

Mapping the Tren Maya’s Mark on Mérida investigates whether the Tren Maya railway is ushering in new sustainable development which provides economic and social benefits for the residents of Mérida, Mexico. The Mexican government has spearheaded the megaproject’s construction in a mere four years with the goal of boosting economic growth in an environmentally and socially conscious manner. The following investigation highlights where this vision, articulated in technocratically-constructed public documents and amplified in reporting, has been fulfilled in Mérida. Through geospatial, physical, and journalistic investigations and analysis, it is revealed the Tren Maya has brought a host of impacts upon the Yucatecan capital through channeling public investments into the Centro Histórico and periphery lands surrounding the metropolitan area. Although these investments present numerous economic, social, and environmental benefits in line with the stated Tren Maya goals, there are also a series of emerging negative consequences. These findings are also conveyed beyond this document using narrative storytelling via a publicly accessible ArcGIS Story Map with maps, imagery, and figures compiled into an accessible information hub.

Beyond Borders: Understanding Cross-Cultural Variations in Skin Tone Preferences

Prior research has documented that cultural context influences implicit skin tone biases. However, the sources of these variations remain unclear. This study tested hypotheses about cultural influences on skin tone biases. We hypothesized that cross-country variations in lighter skin tone preference would parallel those among U.S. communities from the same countries (H1). To explain these patterns, we examined whether socioeconomic disparities (H2), religious orthodoxy (H3), and cosmopolitanism (H4) predicted stronger implicit preferences for lighter skin tones. Data was utilized from Project Implicit (2008–2022) using anonymized responses from the Skin Tone IAT and self-reports. For Hypothesis 1, 80 countries (min. 100 respondents each) were included. For Hypotheses 2–4, analyses covered 43–71 countries for correlations and 28–69 for multilevel models. Implicit bias was measured through reaction times associating skin tones with positive or negative words and explicit bias through self-reported preferences. Socioeconomic disparities were measured by the GINI, Social Mobility, and Education Inequality Indices. Religious orthodoxy and cosmopolitanism were drawn from World Values Survey indicators on beliefs and immigration respectively. Bivariate correlations were used to examine predictor-bias associations, followed by multilevel modeling to account for nested data. Consistent with Hypothesis 1, higher implicit bias in a country corresponded with greater bias among U.S. communities from the same country, suggesting these biases may stem from cultural origins and extend beyond immediate surroundings. Bivariate correlations and multilevel models tested the effects of the three cultural factors on skin tone bias. Contrary to Hypothesis 2, greater socioeconomic disparities were linked to lower implicit bias, though this was not significant in multilevel models. For Hypothesis 3, religious orthodoxy showed no significant association with implicit bias. For Hypothesis 4, cosmopolitanism was significantly associated with lower implicit bias in correlations, though this was nonsignificant in multilevel models. Findings partially supported the hypotheses: some predictors showed meaningful bivariate associations to skin tone bias; however, the effects weakened after controlling individual-level factors. While cultural influences on implicit bias remain complex, this study highlights the value of cross-national perspectives on colorism and the need for further research into the social forces sustaining skin tone preferences.

Beyond Borders: Impacts of the Plastic Waste Trade in Indonesia

Global plastic waste generation has surged over the past two decades, while recycling rates have remained below 10 percent. As high-income countries increasingly export their plastic waste abroad, Southeast Asia has become a major destination, where much of this waste is openly burned or informally recycled. In 2018, China’s National Sword Policy abruptly banned plastic-waste imports, causing a large and sudden redirection of global waste flows toward countries such as Indonesia. This study investigates the effects of the policy shock on local environments and communities in Indonesia. The primary research question is to what extent did the inflow of imported plastic waste altered local air pollution levels? A difference-in-differences design is employed to compare changes in outcomes before and after the 2018 policy in regions near waste sites and ports, where imported waste is concentrated, against regions located farther away. The analysis incorporates satellite-derived PM2.5 pollution data, machine learning–identified waste-site locations from Global Plastic Watch, international trade data from the United Nations Commodity Trade Statistics Database (UN Comtrade), and nationally representative household surveys, including the Indonesian National Socioeconomic Survey (SUSENAS) and the Demographic and Health Survey (DHS). Preliminary findings show a marked rise in PM2.5 concentrations following the policy, with the largest increases near ports and waste-processing sites. These changes are consistent with the elevated open burning of imported waste. Collectively, these findings contribute new evidence regarding the influence of international waste redistribution on local pollution and livelihoods. This research advances understanding of the environmental and human consequences associated with the global plastic waste trade.

ElephantTracker: Spatiotemporal Analytics for Human–Elephant Conflict Mitigation

https://elephanttracker.com/

The ElephantTracker website is a research and conservation platform designed to help analyze and visualize elephant movement and human-elephant conflict patterns.It provides interactive GIS maps to display historical GPS tracking data, showing elephant movement paths, coordinates, and timelines. Researchers and conservationists can upload and manage datasets, making it easier to centralize and analyze elephant tracking information.The site also links elephant movements with records of human-elephant conflict incidents, such as crop damage or property loss. By doing so, it helps identify patterns and geographic hotspots where conflict is more likely to occur. These insights can support risk assessment, allowing conservation teams and local authorities to develop targeted strategies to reduce conflict and protect both communities and elephants.In short, ElephantTracker’s purpose is to serve as a decision-support tool for researchers, NGOs, and wildlife managers by providing clear, data-driven insights into elephant movement and its relationship with human activity. This helps guide conservation priorities, improve coexistence strategies, and ultimately support the long-term survival of elephant population.

Mapping Californian parking lots using machine learning to identify suitable for large-scale solar development

Parking lots represent a currently underutilized potential for solar in the built environment. Currently, there is no statewide map of parking lots in California, making it difficult to prioritize the best sites for solar installation. This project uses machine learning to identify and classify parking lots that may be suitable for solar development. First, we use parcel, roadway, railway, and other land use data to identify suitable parcels for large-scale solar. Next, we classified NAIP infrared imagery in these suitable parcels using machine learning in the ArcGis Pro Imagery classification system. We then post-processed to improve accuracy and calculate vegetation coverage in each parking lot. This map will not only be useful for solar development but can be used in further research in a wide variety of areas, including urban tree cover, land use and planning, and rainwater infiltration.

Uneven Validity of Place — Spatial Heterogeneity in POI Temporal Validity

Point-of-interest (POI) datasets are increasingly used in geographic research to study urban density, accessibility, and spatial clustering. Yet, these datasets often assume that each POI accurately reflects real-world conditions at the time of analysis. This presentation investigates the temporal validity of POIs—whether a place recorded in digital maps still exists and operates at the time of data use. Focusing on Amap POI data from Guangzhou, China (2014–2024), I evaluate temporal validity using both intrinsic and extrinsic approaches. Intrinsic measures assess the lag between the dataset’s retrieval date and the POI’s recorded update time, revealing spatial heterogeneity linked to land use, population, and POI diversity. Extrinsic validation compares POIs with historical street view imagery to identify outdated or demolished places. Results demonstrate uneven temporal accuracy across the urban landscape, emphasizing the need to quantify and model bias in volunteered geographic data. By assigning confidence scores based on update time and persistence, this work contributes to more reliable use of POI data in urban research and planning.

Identifying Suitable Burrowing Owl Habitat at Jack and Laura Dangermond Preserve

Burrowing owls (Athene cunicularia) are small raptors that nest in underground burrows abandoned by ground squirrels and badgers. In the last 100 years, the species has seen a sharp decline in population due to agricultural land conversion, large-scale wind and solar farm development, urbanization, the spread of non-native plants and trees, and overzealous ground squirrel control. Once common throughout California, it has been extirpated from nineteen counties and is close to extinction in ten more, including Santa Barbara, where breeding is fully extirpated and potentially only a few dozen birds visit to overwinter each year. One holdout of habitat is found at Cojo Terrace, part of the Jack and Laura Dangermond Preserve managed by The Nature Conservancy (TNC). In spring of 2025, UCSB’s Cheadle Center staff surveyed Cojo Terrace for suitable ground squirrel and badger burrows using ESRI Field Maps. Surveyors noted evidence of current owl habitation such as pellets and droppings, the presence of invasive plants, and proximity to tall perches such as telephone poles and non-native trees which advantage larger raptors that prey on burrowing owls. Staff tracked their movement throughout the thousand acre study area to assess completeness of the survey. 18 owl sightings and 218 suitable burrows were recorded, as well as an additional 75 substandard burrows which were typically found in overgrown grassland invaded with thick veldt grass. Distance analysis in ArcGIS Pro revealed owl preference for burrows at least 140 yards away from predator perches. The map of the surveyed burrows with a 150 yard safety buffer from perches will inform TNC’s efforts to restore and protect one of the best burrowing owl wintering habitats remaining in Santa Barbara County.

Geospatial location-allocation analysis reveals three distribution locations to maximize HIV prevention service reach for persons who inject drugs in Ciudad Juárez, Mexico

Background: Ciudad Juárez (Juárez), Mexico sits along a major binational, drug-trafficking route, with limited HIV prevention services for the >10,000 persons who inject drugs (PWID), of whom 11% are living with HIV and >80% are living with hepatitis C. Programa Compañeros, the region’s sole harm reduction organization, delivers safe injection equipment directly to PWID via mobile vans. Using location-allocation analysis (LAA), we aimed to identify three locations to maximize their mobile HIV prevention service delivery. Methods: From June to September 2023, we recruited PWID who injected in the previous month, ≥18 years old, living in Juárez and Spanish-speaking to complete an HIV environmental, cross-sectional survey with questions on (a) shared injection equipment (previous year) and (b) up to four locations where they last shared using Google Maps to capture latitude and longitude coordinates. Data were analyzed in ArcGIS Pro 3.1. We pre-specified three locations and a travel radius of 2.5 km (approx. 30- 40-minute walk) to maximize service reach. Results include descriptive statistics, a heat map indicating high-density sharing, and the LAA. Results: Of the 149 participants, 103 (69.1%) shared injection equipment within the last year and provided a total of 142 coordinates. Participants were mostly male (86.4%), with a median age of 45 years and <9th grade education (71.8%). All participants injected heroin, averaging 5.2 injections per day. Figure 1 indicates high-density equipment-sharing locations, with the majority of coordinates (82%) in Northern Juárez. LAA yielded two locations in North Juárez and one in South Juárez that maximize outreach to PWID communities with the greatest need for HIV prevention services. Conclusion: LAA was useful in suggesting three locations that maximize HIV prevention services to PWID within Juárez, Mexico, revealing one location in South Juárez that was previously unknown. Geospatial data analysis was useful in maximizing HIV prevention outreach services.

Geodata

Geodata is a Python library of geospatial data collection and "pre-analysis" tools. Geospatial and gridded datasets of physical variables are ubiquitous and increasingly high resolution. Long time-series gridded datasets can be generated as part of earth system models, and due to their geographic coverage they can have wider applications, including in engineering and social sciences. Geospatial (GIS) files can encode various physical, social, economic, and political data. However, working with these datasets often has significant startup costs due to their diverse sources, data formats, resolutions, and large file sizes. Geodata streamlines the collection and use of geospatial datasets through the creation of shared scripts for “analysis-ready” physical variables. Its purpose is to make it easier for researchers to identify, download, and work with new sources of geospatial data. Additionally, with a minimal amount of data consistency checks and metadata information, when one researcher goes through this exercise, everyone benefits. Geodata builds off the atlite library, which converts weather data (such as wind speeds, solar radiation, temperature and runoff) into power systems data (such as wind power, solar power, hydro power and heating demand time series). Geodata retains the power systems data functionality of atlite.

Scaling Up Drone-Based 3D Mapping for Campus Facility Management

This lightning talk continues our work on using drones to enhance campus facility management. Since last year’s presentation, the project has expanded from mapping a small neighborhood to developing a detailed 3D model of the entire UC San Diego La Jolla campus. Utilizing drone-based photogrammetry with Real-Time Kinematic (RTK) positioning, we achieved high spatial accuracy across extensive areas. Ground Control Points (GCPs) collected with Bad Elf and FieldMaps ensured precision and consistency within a unified coordinate system. Drone imagery and GCP data were processed through ArcGIS SiteScan for geotagging and 3D mesh generation, with outputs integrated into the campus GIS database. The resulting 3D campus model supports virtual fieldwork, site analysis, and data-driven decision-making, offering new tools for planning, management, and research. The project remains under active development to further improve performance, usability, and integration with other campus systems.

Coastal Global Fatal Landslides and Cliff Failures

Cliffs shape the world’s coastlines, providing areas of beauty, habitat, scientific discovery, and recreation. However, as erosional features, seacliffs can also pose fatal hazards. This paper presents and analyzes fatal landslides along the global coast, recorded in public databases and written media articles that were assembled into a new comprehensive database. In total, the coastal landslide fatality database contains reports showing that 294 people have been killed across 115 recorded fatal coastal landslide events from 1927 - 2024, with most fatalities from direct rockfall onto victims walking along beaches. Most fatalities occurred in temperate regions, with two seasonal peaks: one in wet and one in dry seasons. We suggest that this bimodal peak is from (a) elevated rainfall causing reduced cliff stability, leading to more failures in wet seasons and (b) increased summer tourism and local recreational beach activity in dry seasons, exposing more people to seacliff failure hazards. These observed peaks in coastal cliff-related fatalities during periods of elevated rainfall and during the peak beach tourism season can help inform beach hazard management decisions.

Land Use Regression Modeling to Assess Impacts of Warehouse Intensification on Neighborhood Exposure in Southern California’s Inland Empire

The Inland Empire, California (IE) faces rapid land use change from farmland to warehousing, and downwind neighborhoods may face changes in near-source pollution exposures of criteria pollutants. The rise of e-commerce caused Los Angeles to meet capacity for warehouse availability, leading to warehouse intensification and sprawl in the IE in the new millennium. This intensification further accelerated after the establishment of the 2013 Amazon mega-warehouse on former farmland. This study utilizes satellite column data (TROPOMI) and dispersion modeling (AERMOD) to assess criteria pollutant exposure in a neighborhood downwind of former farmland that has been transitioned into a warehouse cluster. Scenarios will assess periods when farmland dominated and when warehouse intensification persists. Area-specific transportation emission rates are developed to inform AERMOD, while agricultural emission rates are retrieved from other studies. A GAM-based land use regression that is informed by satellite data and AERMOD output as independent variables allows for hybrid modeling. This hybrid model allows modeled exposure assessment to consider multiple input variables such as land use, meteorology, terrain, and emission rates. Results intend to assess the impact of criteria pollutant exposure from upwind former farmland and a recently formed warehouse cluster during a period of dramatic land use change in a suburban IE neighborhood

Interactively Communicating the History and Present Legacy of Freeway Siting in California

Historically, disadvantaged communities have been disproportionately affected by highway planning and freeway siting. This project uses empirical research to not only understand but also quantify and describe in detail the historical impacts of freeways on communities of color in California. This translational project communicates the findings of a series of case studies on the history of freeway routing in communities of color in California in innovative ways, making the historical research more relevant to policymakers, more visible to community members, and more accessible to the public. Our research team has created a number of storymaps that weave together audiovisual and textual elements to tell the story of freeway siting, construction, and legacy in Pasadena, Stockton, Pacoima, Fresno, and Colton. The construction of freeways was a contributing mechanism to the perpetuation of racial inequality, weakening social institutions, disrupting local economies, and physically dividing neighborhoods. However, outcomes varied across locations. In Pasadena and Pacoima, decision-makers chose routes that displaced a greater share of households of color than proposed alternatives. Stockton underwent spatial restructuring, and xenophobia and racism placed Stockton’s Chinatown, Japantown, and Little Manila in the path of freeway construction and “slum clearance.” In South Colton, a freeway was ultimately not built through its community of color, though largely for reasons of construction costs. West Fresno did face consequences from freeway development but was also unique in its diversity of residents pre-freeway. Freeway development contributed to transforming West Fresno into an overwhelming community of color. Across these cases, freeways fragmented communities, displaced residents, and reinforced pre-existing racial divides. White, affluent interests often succeeded in pushing freeways to more powerless neighborhoods. These racialized impacts stemmed from systemic socioeconomic marginalization and exclusion of people of color in the planning process. Understanding the history of racism in freeway development can inform restorative justice.

Linguistic Landscapes through Mapping: Raising Awareness about Multilingual Communities

This presentation demonstrates how GIS mapping can be utilized as a powerful tool to explore language use in public spaces and raise awareness of multilingual communities. The maps showcased were developed as part of two projects aimed at highlighting linguistic diversity in the greater San Diego area. The first project, conducted as a class assignment, focused on documenting Spanish language use in signage. The second project was open to the broader San Diego community and expanded the scope to include all languages other than English. In these projects, participants contributed by uploading photographs of signage featuring non-English languages in public spaces, following the linguistic landscape methodology (Landry and Bourhis, 1997). Each sign was categorized by type (e.g., storefront, poster, billboard), language use (monolingual, bilingual, or multilingual), dominant language, origin (bottom-up or top-down, i.e., created by individuals or institutions), and purpose (e.g., cultural or informational). GIS technology was then used to map this data, visualizing the distribution of linguistic landscapes across the region. The resulting maps provided students with valuable insights into the frequency and geographic concentration of signage, the purposes of the signs, the prevalence of bilingual or multilingual language use, and the broader significance of these findings. By combining community participation and GIS technology, this project illustrates how mapping can raise awareness of linguistic diversity and its presence in public spaces, while also fostering a deeper understanding of bilingual and multilingual communities.

GeoConnect: Creating a research-informed UC-wide GIS resource

GeoConnect is a project to 1) assess what GIS users on our campuses seek with regards to geospatial resources and support, and 2) to collaboratively organize and maintain these resources into a centralized hub to support the UC community. The project arose out of conversations among members of the UC/Stanford Maps & Geospatial Data Common Knowledge Group and the University of California GIS Committee or UC GIS (composed of GIS professionals and users from across the UC system). Noting that we have 10 campuses maintaining separate resource guides containing similar information, we then asked “How can we collaborate on a UC-wide resource guide and share the labor?” This effort is challenging due to the nature of GIS software, data, and availability of collaborative resource solutions. GIS is a broad and rapidly evolving field in regards to software applications and their features. Similarly, there is an ever-shifting landscape of authoritative datasets from different levels of government, both nationally and internationally, as well as other authoritative agencies or organizations. In addition, the UC system currently lacks cross-institutional solutions that would enable a more effective approach to collaboratively provide GIS support on our campuses. The GeoConnect Working Group is a combination of librarians and campus GIS staff from across the UC system. We received LAUC funding to research the obstacles facing GIS users across our campuses, evaluate their information-seeking behavior, and create a centralized GIS resource hub. This presentation will highlight the findings from our cross-campus survey and focus groups with students, faculty, staff, and researchers from across the UC. It will also include a sneak-peek at the shared resource the GeoConnect team is developing.

Designing a Campus Map in Experience Builder

Under development since 2024, this presentation will cover the progress and enhancements made in developing UCSD's Campus Map in Experience Builder. The Experience Builder Campus Map will eventually replace the current Concept3D platform. The aim of the presentation is to showcase the Experience Builder layout, widgets, triggers and actions used to design a highly interactive, user-friendly campus map. A description of the button configuration, side-panels, views, search and directions will be provided. The presentation will also cover the creation of the campus vector tile basemap using Esri's Vector Tile Style Editor and sprites, which has greatly increased drawing times. The map will also feature real time transportation information, ingested via GTFS-RT APIs in GeoEvent Server.

Bridging Campus and Operations with Location Intelligence

Report It Now is an ArcGIS Online–based platform that empowers campus community members to report non-emergency issues—such as maintenance, safety, accessibility, and environmental concerns—directly on a map. By integrating spatial reporting with operational workflows, the system streamlines communication between users and departments including Facilities Management, Environmental Health & Safety, and Transportation. Since launch, more than 300 submissions have been recorded, categorized, and tracked through an interactive application that visualizes issue status and spatial distribution. Beyond improving reporting efficiency, Report It Now demonstrates how GIS supports data-driven operations by revealing spatial patterns. These insights help prioritize maintenance, allocate resources, and inform long-term planning. The project highlights how location intelligence can strengthen engagement, transparency, and operational effectiveness within a campus environment.

UC Damage Assessment Exercise

In October of 2025, the UC Damage Assessment Exercise was held by the UCGIS Facilities & Operations Workgroup and coordinated with Emergency Managers from across the UC. The month-long exercise put mobile GIS tools in the hands of campus operations staff to report hazards and/or to submit building damage assessments. The results were then visualized on ArcGIS Dashboards and Attachment Viewers, providing participants with direct and instant access to information. This presentation will show the exercise results and feedback from the participants.

High-Resolution Mapping: UAVs Meet GIS

The increasing frequency and severity of wildfires in California have a raising concerns for both human communities and ecosystems. But the fire dynamic and structures are lack of comprehensive estimation due to the technology limitations. Recent advances in Uncrewed Aerial Vehicle (UAV) coupled with high spatial resolution mapping and the Structure from Motion (SfM) Multiview Stereo algorithm, have made an indispensable tool for estimating wildfire fuel consumption across various fuel types. In this study, we implemented a one-year sequential approach for calculating fuel consumption and regrowth using UAV mapping before and after a prescribed canyon fire near Salinas, Northern California. Collaborating with the California Department of Forestry and Fire Protection, multi-source UAV mapping were conducted and processed the drone data into pre- and post-burn multispectral orthomosaic maps, Digital Surface Models (DSMs), fuel type classifications, and surface volume measurements. Using ground control points and tie points, we developed and trained linear regression models to address angular discrepancies and elevation displacements in the time series DSMs. These adjustments allowed for accurate comparisons of DSMs to assess vegetation changes over time around the fire event. DSMs, standardized geospatial technologies, GCPs, and ecological field data are used for precisely mapped fuel consumption and post-fire vegetation recovery at the individual vegetation level. Results show a positive correlation between fuel consumption height and fire temperature across various fuel types and a strong correlation between fuel consumption and recovery. This methodology demonstrates substantial potential for UAV-based cross-disciplinary analysis in fire ecology, meteorology, environmental science, and wildfire management.

Future Farm Now: A Scalable Geospatial Platform for Precision Agriculture

Future Farm Now (FFN) is a full-stack geospatial intelligence platform designed to empower agricultural research and decision-making across California and the Colorado River Basin. The project integrates remote sensing, soil science, and big data analytics to provide an exploratory interface for farmers, scientists, and policymakers. FFN combines satellite imagery from Landsat, Sentinel-2, and Planet Labs with comprehensive soil datasets to analyze and visualize key land and crop health indicators. At its core, FFN enables multi-dimensional exploration of farmland through three major capabilities: (1) Soil Property Analysis - generating spatially resolved statistics for properties such as bulk density, clay content, soil pH, organic matter, air entry pressure, water flow rate, pore distribution, porosity, sand content, silt content, water content (min & max) and water retention parameters across depth profiles; (2) Vegetation Monitoring - producing NDVI-based time series and spatial visualizations computed from processed spectral bands (B4, B5) to assess crop vigor over time; (3) Soil Sampling Optimization - identifying optimal sampling points (coordinates) based on data-driven variability in soil characteristics. The backend architecture leverages distributed spatial data processing using Apache Spark and BEAST to efficiently manage and query terabyte-scale raster and vector datasets. Custom indexing, data pipelines, and projection-aware transformations address the challenges of multi-resolution and multi-format geospatial data. The raw datasets are also stored and managed on UCR’s Horus server infrastructure, which also provides FTP access for researchers. FFN’s user-facing web application offers an intuitive, high-performance interface for real-time data exploration and visualization, translating complex geospatial analytics into actionable insights. By bridging advanced data management with user-centric design, Future Farm Now demonstrates how scalable, open geospatial systems can transform agricultural research and sustainability practices.

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.

Status of California Native American Tribal GIS Knowledge Build

This past July 31st and August 1st, 2025 – the 1st California Native American Tribal GIS Summit was held in Sacramento in the Cal/EPA Building. There were 22 presentations that included CA Tribal perspectives of their uses of GIS based technology, federal and state agencies collaboration in working with Tribes, Tribal non-profits utilizing GIS – all using GIS to help manage Natural Resources, protection of cultural landscapes, and environmental justice support. This summit was first of its kind in California. Both UC Davis and UC Berkeley also participated and provided their efforts in working with CA Native American Tribes with the usage of GIS based solutions and potential new water governance model. The concepts of Tribal GIS data sovereignty, rematriation, ground-truthing, land back, traditional ecological knowledge (TEK), decolonizing mapping, and cultural burning were discussed and explored. This summit provided to provide the need for the government to provide GIS based data for future natural resources changes, including CA Tribes in water and natural resources management policy making, and more GIS based training for CA Native American Tribes – both federally and non-federally recognized. The proposed presentation will provide a summary of this important and historic summit. The concepts of “Tribal GIS data sovereignty, rematriation, ground-truthing, land back, traditional ecological knowledge (TEK), decolonizing mapping, and cultural burning” will be discussed. Mapping solutions and concepts will also be illustrated. The presentation should take about 25 minutes. Planning for the 2026 California Native American Tribal GIS Summit is underway, and we encourage UC participation – presenter, exhibitor, or participant. www.catribalgis.org – location of the 2026 summit status.

(Re)Thinking the ‘Z Axis’: A Case for Relational Ontologies in Vertical Worlds

Current cartographic standards for representing vertical terrain are embedded in two-dimensional, top-down, colonial perspectives, holding the possibilities of the vertical world at bay. For example, topographic maps marginalize the vertical world through a type of compression. To signify steepness, we place contour lines closer together, providing the slope less space on the map. In other maps, the vertical is represented through artistic hatching or shadowing. A more modern tool, like LIDAR, scans the landscape from the top-down and builds verticality into a model where the user can manipulate the view of each crevice. Vertical or volumetric spaces sit in tension with the space often made visible by the Cartesian system. Vertical spaces are not often a space of conquest, a space of property, or even considered a ‘human space’ (Elden). Rather, what we find is a space where the geologies, ecologies, and biologies operate with increasing entanglement under the influence of the consequences of gravity (i.e., everything must hold everything else together on a vertical landscape lest it fall). With this, my project asks: how do we create a relational mapping system based in critical cartographic theory that allows for both a different representation and also, fundamentally, an epistemological shift in how we understand the world through the undertheorized ‘voluminous space’? And how might this system better represent the dynamics of a rapidly shifting world under climate change? My proposal is to make a presentation on these questions for the audience of US GIS Week and to engage them in a thought experiment around what it might take to create these relational representations and get feedback on these possibilities. 

Photosynthesis from Space: Solar-Induced Fluorescence Across Droughts and Tariffs

I present early exploratory results using monthly OCO-2 solar-induced chlorophyll fluorescence (SIF) over Iowa (2014–2025) to see whether broad shocks show up in crop activity. I compute statewide and county SIF means and standard deviations for morning (a) and evening (b), plot the long-term time series and seasonal cycle, and map the simple diurnal difference (a–b). I compare these patterns to (i) U.S. Drought Monitor intensity and (ii) key tariff periods, and summarize annual crop composition from the USDA Cropland Data Layer (corn/soy shares, indications of fallowing). The goal is descriptive: highlight years/months where SIF anomalies align with drought and note any coincident shifts in crop shares around tariff windows. I’ll close with a short roadmap for adding basic masks and simple correlations, with causal modeling left for future work.

UCSB Tree Restoration Data Collection and Monitoring

This lightning talk will introduce data collection methods used in the field at restoration sites managed by the Cheadle Center for Ecological Restoration at UCSB and demonstrate how the collected data is used to create publicly available web maps. These web maps display photo monitoring points at the Ellwood Marine Terminal restoration site, as well as tree monitoring data from the Ellwood Monarch Grove restoration site. The presentation will also include a discussion of future web map project ideas related to ecological restoration, such as displaying water quality and/or water elevation data collected from pressure transducers at the North Campus Open Space restoration site.

Exploring Water Use on Fallowed Fields

In response to a record drought along the Colorado River, the United States Bureau of Reclamation (USBR) has set aside funding to incentivize agricultural land fallowing to produce water savings and protect water elevations in Lake Mead and Lake Powell. Fallowing is a well-known and accepted method for producing agricultural water savings but studies have shown that fallowed fields consume some water which reduces the water savings impact of fallowing. Understanding the different factors that affect this fallowed field consumption can be key in minimizing their water use. This study evaluated traits that potentially correlate with consumptive use on fallowed fields using the Palo Verde Irrigation District Fallowing Program. Using evapotranspiration (ET) data from OpenET and crop shapefiles I show that fallowed field consumptive use is generally positively and statistically significantly correlated with the ET of its surrounding fields, smaller field size, and field narrowness, while being negatively and statistically significantly correlated with time fallowed. Future and on-going fallowing programs may be able to be made more efficient by adopting regulations to prioritize field traits that minimize fallowed water consumption. This could include ensuring larger and squarer fallowed fields, increasing the allowable time that a field can be fallowed, and locating fallowed fields away from high water use crops.

Agrivoltaics Suitability: Multi Criteria Decision Making in a Repurposing Context

California’s groundwater crisis is reshaping the state’s agricultural landscape. Implementation of the Sustainable Groundwater Management Act (SGMA) requires regional agencies to drastically reduce groundwater use—potentially fallowing nearly 900,000 acres of farmland in the San Joaquin Valley. As regions plan for a more water-limited future, innovative land use strategies are needed to sustain rural economies, preserve food production, and meet groundwater goals. Agrivoltaics—the co-location of solar photovoltaic systems and agriculture—offers a potential water savings for agriculture through increased soil moisture retention and reduced evapotranspiration. A new concept to California, this project develops a spatial Multi-Criteria Decision Making (MCDM) framework to identify areas in California most suitable for agrivoltaic deployment. Using GIS-based suitability modeling and the Analytic Hierarchy Process (AHP), I synthesize environmental, infrastructural, and social criteria. This study aims to conduct analyses relevant to decision makers at the basin-level by creating easy to use geospatial tools.

Spatial and Cultural Storytelling: Mapping Asian American Narratives through GIS, Oral History, and Virtual Reality

This year-long project unites the Oral History and Virtual Reality cohorts of the Asian American Youth Leaders (AAYL) Program in Irvine, California to document and spatialize the experiences of Asian American professionals and UCI students across Orange County. Each high school and college student researcher selects and interviews professionals in career fields they aspire to enter, creating intergenerational connections rooted in shared goals and community identity. Through these dialogues, students capture personal stories that reflect pathways of growth, resilience, and belonging. Geographic Information Systems (GIS) serve as the connective framework, mapping each oral history to its geographic context—homes, campuses, workplaces, and community landmarks—revealing spatial patterns of mentorship, aspiration, and cultural presence. The Virtual Reality cohort then transforms these mapped narratives into immersive environments, allowing audiences to virtually “walk through” stories and experience how geography shapes identity and opportunity. Together, GIS, oral history, and VR bridge data, place, and lived experience to create a multidimensional archive of Asian American life in Orange County—one that celebrates youth perspectives, fosters cross-generational understanding, and reimagines how technology can amplify community storytelling.