The Center for Geospatial Analytics is a research and teaching center within the College of Natural Resources that focuses on data-driven spatial modeling and visualization for sustainable solutions to environmental and societal challenges.
As the technological and intellectual hub for the Chancellor’s Faculty Excellence Program in Geospatial Analytics, the center’s cross-college collaborations and industry and community partnerships are advancing geospatial frontiers in a variety of fields.
To get a better grasp on geospatial data and its impact on today’s society, we asked John B. Vogler, research scientist at the Center for Geospatial Analytics, a couple of questions.
What does geospatial data mean to you?
When I was working with geographic information systems (GIS) as a geography undergraduate, the popular analogy was that geospatial data are like layers of an onion. That analogy is useful for understanding how geospatial data are organized into “layers” of georeferenced digital information, but it completely misses the mark in terms of the purpose of geospatial data.
I like to think of geospatial data as clues for helping us solve some of society’s most pressing and complex problems. The data are like puzzle pieces that, individually, don’t provide much insight into an overall picture or solution. But, like a jigsaw puzzle nearing completion, something amazing happens when these data are analyzed or visualized together: important spatial patterns, relationships and interactions emerge that can lead to better decision making and new scientific discoveries about the world around us.
What are the top uses of geospatial data?
In the broadest sense, geospatial data are used to map and visualize places, things and events — from the beautiful and information-rich satellite images of the Earth’s atmosphere, land and water to more practical live maps of the locations of buses, stops and routes of the Wolfline Transit System (like Transloc).
Things get really interesting, though, when you consider how geospatial data are at the core of the computer models we develop to understand spatial phenomena over time. For example, at the Center for Geospatial Analytics we use geospatial data to understand land change processes and to build models that can simulate alternative futures of urbanization and landscape fragmentation and help us evaluate tradeoffs in ecosystem services (for example, clean air, clean water, biodiversity) given different scenarios.
We also use geospatial data to understand biological invasions and to predict the spread of disease. Recent collaborative research by center director, Ross Meentemeyer, myself, Monica Dorning, and our UC Berkeley colleagues used over 6,500 georeferenced leaf tissue samples collected over six years by more than 1,600 citizen scientists to develop predictive maps of the spread of sudden oak death (SOD) disease in and around the San Francisco Bay region of California. We tested submitted tissue samples for the presence of the pathogen that causes SOD.
We then combined those outcome data with other geospatial data representing suitability factors for the disease to identify and map areas of high risk of infection. The disease risk maps inform local communities and stakeholders on where to prioritize management efforts, such as the application of preventative treatments to valuable coast live oaks. Our published article, “Citizen science helps predict risk of emerging infectious disease,” was recently cited by President Obama’s senior adviser on science and technology policy, John Holdren, as real-world citizen science that is advancing disease research (read more here).
The center is also bridging the digital and physical worlds with tangible geospatial analytics. These novel decision making tools and approaches move stakeholders away from keyboards and computer monitors and gather them around geographically realistic physical landscape models for a very tactile collaborative experience. The center’s Tangible Landscape environment projects digital geospatial data onto 3D physical models of a study system and allows stakeholders to literally get their hands on the data.
For example, when testing different scenarios, users can make changes to the physical model during group discussion and get real-time feedback as a behind-the-scenes computer model analyzes the changes or runs simulations and then re-projects new and often animated geospatial data back onto the physical model. In this way, the focus shifts from building computer models to building consensus by improving communication and decision making among stakeholders.
Some of the many applications of tangible geospatial analytics include spatial diffusion of wildfire, simulating the spread of emerging infectious disease, and visualizing coastal storm surge. At the heart of all of these examples is geospatial data. And to circle back to the first question, in our research, having good geospatial data means everything.
For more information on the Center for Geospatial Analytics or earning a degree in Geospatial Information Science and Technology, check out their website.