GEOG Seminar 11/2: Taylor Oshan, "Towards a Decentralized Geospatial Web"

Join us for our weekly seminar on Thursday, Oct. 26 from 3:45-5 p.m. at River Road and on Zoom. GEOG Assistant Professor Taylor Oshan will explore the increasing importance of geospatial data due to its prevalence in the global data landscape. 

Seminar description: A large portion of the world’s data contains some type of geospatial information and with larger and larger volumes of data becoming available and the proliferation of location-based services, alternative solutions for handling these data are more important than ever. As a result, this talk will introduce the notion of the decentralized web, including the role of blockchain and peer-to-peer systems, as well as review elements of contemporary geospatial web technology.

Ongoing work will be highlighted that explores how the decentralized web can support the access and storage of geospatial data and how the geospatial web can increase discoverability on the decentralized web. While the primary focus of this work will be on the example of remotely-sensed satellite imagery, an array of additional applications, use cases, and projects will also be discussed that strive to make geospatial information more accessible, transparent, and interoperable. This suggests the emergence of a decentralized geospatial web and some potential opportunities and challenges of such a paradigm are considered in the context of geospatial science.

About the Speaker: Dr. Taylor M. Oshan is broadly interested in characterizing spatial patterns and processes through the use of quantitative geographic methods, which typically falls under the banners of spatial analysis and spatial statistics, geographic information science, and the emerging discipline of spatial data science. His work typically involves modeling human processes within urban environments and therefore also intersects with the disciplines of computational social science, and urban informatics. Overall, his research has targeted the development of multivariate spatial statistics and how they can be used to capture how relationships change by spatial and temporal contexts. This includes issues of theory, interpretation, scalability, and integration of traditional geographic models with novel "big" datasets, as well as applications in public health, crime, urban mobility, and transportation systems.

Zoom Info: For Zoom details, please visit our Department Calendar on Google or reach out to ushaship@umd.edu.