Date: Tuesday, April 11th, 2:00 - 2:45 p.m.
Location: Engineering and Physical Sciences Library, Math (Kirwan) Building
Gaining access to inexpensive, high-resolution, up-to-date, three-dimensional road network data is a top priority beyond research, as such data would fuel applications in industry, governments, and the broader public alike. Road network data are openly available via user-generated content such as OpenStreetMap (OSM) but lack the resolution required for many tasks, e.g., emergency management. More importantly, however, few publicly available data offer information on elevation and slope. For most parts of the world, up-to-date digital elevation products with a resolution of less than 10 meters are a distant dream and, if available, those datasets have to be matched to the road network through an error-prone process. In this talk I discuss the current state of OpenStreetMap and introduce a radically different approach to deriving road network elevation data from massive amounts of in-situ observations extracted from user-contributed data from an online social fitness tracking application.
Grant McKenzie is an assistant professor in the Department of Geographical Sciences, affiliate of the Center for Geospatial Information Science, member of the Human Computer Interaction Lab. Dr. McKenzie’s research interests lie in spatio-temporal data analysis, geovisualization, place-based analytics and the intersection of information technologies and society. Currently, he is exploring computational, data-driven models of human behavior, taking a multi-dimensional approach to investigating the relationship between place & space and the activities people carry out at those places. The foundation of this research involves working with large geosocial, user-contributed and authoritative datasets, exploiting and visualizing spatial, temporal and thematic signatures within the data. These signatures are employ through unique methods and statistical models for the development of effective interactive (desktop and mobile) geovisualization, place-based prediction models and knowledge discovery applications.
The presentation is available on the UMD Libraries Twitter account.