About the Lab

The Place Time Analysis Lab or PTAL (pronounced "petal"), is a geoinformatics research group founded by Professor Grant McKenzie. As the name suggests, PTAL focuses on the roles of place and time in defining and better understanding the world around us. Specifically, this lab pulls apart the concept of place and takes a data-driven approach to understanding the dimensions that contribute to how individuals and groups understand the concept.
Topics of Interest include:
•  Dimensions of place / Semantic Signatures
•  Geo-privacy
•  Geospatial knowledge representation
•  Credibility of place information
•  Spatio-temporal data mining
Interested in joining the group as a student researcher? Send us an email along with your CV and research interests or stop by and say hi.


Dr. Grant McKenzie

Grant McKenzie is an assistant professor in the Department of Geographical Sciences at the University of Maryland, College Park. Grant is also an affiliate of the Center for Geographic Information Science and leads the Place Time Analysis Lab. He holds a PhD in Geography from the University of California, Santa Barbara (2015), a Master of Applied Science degree from the University of Melbourne (2008) and a Bachelors in Geography from the University of British Columbia (2002). 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.