UC GIS Week

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.