2019 Spatial Data Science Symposium

Spatial Data Science Symposium “Setting the Spatial Data Science Agenda” December 9–11, 2019 Upham Hotel (https://www.uphamhotel.com/) Santa Barbara, California Motivation Space and time matter not only for the obvious reason that everything happens somewhere and at some time, but because knowing where and when things happen is critical to understanding why and how they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of spatial data. It generalizes and unifies research from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, and fosters the application of methods developed in these fields to outside disciplines ranging from the social to the physical sciences. In doing so, research on spatial data science must address a variety of new challenges that relate to the diversity of the utilized data and the underlying conceptual models from various domains, the opportunistic reuse of existing data, the scalability of its methods, the support of users not familiar with the language and methods of traditional geographic information systems, the reproducibility of its results that are often generated by complex chains of methods, the uncertainty arising from the use of its methods and data, the visualization of complex spatiotemporal processes and data about them, and, finally, the data collection, analysis, and visualization playing out in near real-time. Spatial data science does not only utilize advanced techniques from fields such as machine learning or big data storage and retrieval, but it also contributes back to them. Recent work, for instance, has shown that spatially-explicit machine learning methods substantially outperform more general data when applied to spatial data even though this spatial component may seem of secondary importance at first glance. The Center for Spatial Studies at the University of California, Santa Barbara is hosting a symposium entitled “Setting the Spatial Data Science Agenda.” The meeting will bring together academic and industry representatives from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, with interest in setting an interdisciplinary research agenda to advance spatial data science methods and practice, both from scientific and engineering viewpoints. We also invite experts from related fields and those that are producers or users of spatial data in the social and physical sciences. Goals Instead of being restricted by a historically grown partition into small and overlapping communities that deal with spatial data in one way or the other, the overarching goal of this symposium is to put spatial data science at the forefront of a unified field that explores the current research and application landscape to define an agenda for spatial data science...

Call for Applications for the 2019 Spatial Data Science Symposium

Jul 24, 2019 • Categories: Featured | Highlight | News | Specialist Meetings

Spatial Data Science Symposium “Setting the Spatial Data Science Agenda” December 9–11, 2019   Upham Hotel (https://www.uphamhotel.com/) Santa Barbara, California Motivation Space and time matter not only for the obvious reason that everything happens somewhere and at some time, but because knowing where and when things happen is critical to understanding why and how they happened or will happen. Spatial data science is concerned with the representation, modeling, and simulation of spatial processes, as well as with the publication, retrieval, reuse, integration, and analysis of spatial data. It generalizes and unifies research from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, and fosters the application of methods developed in these fields to outside disciplines ranging from the social to the physical sciences. In doing so, research on spatial data science must  address a variety of new challenges that relate to the diversity of the utilized data and the underlying conceptual models from various domains, the opportunistic reuse of existing data, the scalability of its methods, the support of users not familiar with the language and methods of traditional geographic information systems, the reproducibility of its results that are often generated by complex chains of methods, the uncertainty arising from the use of its methods and data, the visualization of complex spatiotemporal processes and data about them, and, finally, the data collection, analysis, and visualization playing out in near real-time. Spatial data science does not only utilize advanced techniques from fields such as machine learning or big data storage and retrieval, but it also contributes back to them. Recent work, for instance, has shown that spatially-explicit machine learning methods substantially outperform more general data when applied to spatial data even though this spatial component may seem of secondary importance at first glance. Co-sponsored by Esri, the Center for Spatial Studies at the University of California, Santa Barbara is hosting a symposium entitled “Setting the Spatial Data Science Agenda.” The meeting will bring together academic and industry representatives from fields such as geographic information science, geoinformatics, geo/spatial statistics, remote sensing, and transportation studies, with interest in setting an interdisciplinary research agenda to advance spatial data science methods and practice, both from scientific and engineering viewpoints. We also invite experts from related fields and those that are producers or users of spatial data in the social and physical sciences. Goals Instead of being restricted by a historically grown partition into small and overlapping communities that deal with spatial data in one way or the other, the overarching goal of this symposium is to put spatial data science at the forefront of a unified field that explores the current research and application landscape to define an agenda...

spatial@ucsb.local2019: Posters

spatial@ucsb.local2019 main page The Future of Island Oaks The Future of Island Oaks Laura Wolf, Sofie McComb, Claire Powers, Jazmine Uy, Alyssa Winchell Bren School of Environmental Management, University of California, Santa Barbara Description: Island oak (Quercus tomentella) is a rare oak species endemic to six islands in the California Island Archipelago (CAIA). Over a century of farming and grazing on the islands degraded core habitat and reduced island oak seedling recruitment. The species was listed as endangered by the IUCN in 2016. Most historical threats have been removed, though island oak regeneration is still restricted and there is concern that impending climate change poses an additional threat that may ultimately lead to extinction. Spatially-constrained, if the island oak’s range shifts or further deteriorates, alternative options are limited. We used MaxEnt, a species distribution model, to identify island oak’s bioclimatic niche on Santa Cruz, Santa Rosa, and Santa Catalina Islands and then predicted where that niche might exist through the end of the century under four climate change scenarios. Model outputs supported three main findings: (1) Island oak’s predicted bioclimatic niche was largely driven by soil moisture availability; (2) Santa Rosa Island had the most predicted suitable habitat under each climate change scenario, while predicted suitable habitat on Santa Cruz and Santa Catalina Islands was minimal; and (3) the bioclimatic habitat occupied by island oak varies substantially between the three islands studied. Improvements in life history information, legacy grazing patterns, and more finely downscaled climate data would substantially increase model validity. Research should focus on identifying mechanisms driving the variation in habitat occupied on each island, while restoration should prioritize habitat augmentation and seedling recruitment, to increase island oak’s resiliency to climate change.   Urbanization and its Effects on the Surrounding Environment                       Urbanization and its Effects on the Surrounding Environment: Case Study of Beijing and Lanzhou, China Guiyu Li, Yingyi He, Jiaxuan Lyu, Hoayu Shi Department of Geography, University of California, Santa Barbara Description: In the past decades, China has experienced massive economic growth and urban development. Changes in urban land cover, vegetation healthiness, and temperature distribution are crucial factors to understand the urbanization effects on the surrounding environment. Beijing and Lanzhou, two distinctive cities in terms of size and geographical location, are selected as our study objects. Using Landsat 5 and 8 images from 1993 to 2017 for the two cities, we train our algorithms to classify land cover types, including urban, vegetation, soil, and water. Normalized Difference Vegetation Index (NDVI) is calculated to measure vegetation health. Temperatures are derived using the radiance of the thermal band. Land cover classes are used for NDVI and temperature analysis. Based...

Call for Visitors

Dec 5, 2018 • Categories: Featured | Highlight | News | Research | Visitors

Welcoming New Applications! The UCSB Center for Spatial Studies invites interested scholars, instructors, postdoctoral researchers, and interns to apply to become a visitor with the Spatial Center. Details are available through this...

Hosted Conferences

Conferences hosted by the UCSB Center for Spatial Studies. International Conference on the Internet of Things The 8th International Conference on the Internet of Things (IoT 2018) hosted by the Center for Spatial Studies at the University of California, Santa Barbara was held October 15–18, 2018 in Santa Barbara, California. Read...

Thank you to Spatial Lightning Talk 2018 presenters

Feb 13, 2018 • Categories: Event | Featured | Highlight | Lightning Talks | News | Photos

The UCSB Center for Spatial Studies hosted another great round of Spatial Lightning Talks this year, with a whole new batch of spatially-relevant topics. Eight speakers took the challenge to “enlighten us, but make it quick!” Many new and returning faces in the crowd had the opportunity to hear a lively group of speakers and make new connections across campus. This year, there was a 2-minute question and answer period after each 3-minute presentation, allowing the audience to participate more in the program. Two speakers, Paul Wilson (formerly of GE / MapFrame) and James Caesar (UCSB Campus Emergency Manager) spoke about the recent Thomas Fire and mudslides. Paul called for map-minded people in the local community to band together and improve the state of emergency mapping; James shared some of his real, on-the-ground experience responding to the recent fire and mudslide events. Geography Professor Keith Clarke taught us about a local piece of history just around the bend at Honda Point. Thomas Crimmel continued the geographers and history theme with his (very!) abridged history of the digital desktop. Joshua Kuntzman, graduate student at the Gervitz School of Education, pushed us to think more about what it takes to facilitate true interdisciplinary work through the UCSB Crossroads program. Jeremy Douglass, Assistant Professor of English, gave us a run-through of his project Panelcode. Graduate student Lily Cheng made everyone stop and think about their well-developed ‘paw preferences’, and aviation consultant William Yim gave everyone a new perspective on focus in photography. This event was organized by Crystal Bae, bidding adieu to Kitty Currier who has done a great job organizing in years past. If you are interested in presenting a 3-minute lightning talk at next year’s event, get in touch with Crystal anytime. Videos of this year’s talks will be posted on the Spatial Center website once they become...