Grant McKenzie
Title:
Associate Professor
Research areas:
Spatial Data Science
Geographic Information Science
Biography:
Grant McKenzie is an associate professor of spatial data science in the Department of Geography at McGill University. At McGill, Grant leads the Platial Analysis Lab, an interdisciplinary research group that works at the intersection of information science and behavioral geography. Much of Grant's work examines how human activity patterns vary within and between local regions and global communities. This has driven his applied interests in financial accessibility, geoprivacy, and micro-mobility services as well as the broader role that geographic information science plays at the intersection of information technologies and society. Grant is a founding member of the Seattle-based start-up consultancy Spatial Development International (now Locana) and has worked as a data scientist and software developer for a range of NGOs and leading technology companies.
Current research:
- Location privacy and trust
- Assessing the urban impacts of shared micromobility services
- Temporal dynamics in urban environments
Degree(s):
- PhD in Geography, UC Santa Barbara (2015)
- M. App. Sci., University of Melbourne (2008)
- BA Geography, University of British Columbia (2002)
Selected publications:
- Qiang, D., McKenzie, G. Navigating the Post-Pandemic Urban Landscape: Disparities in Transportation Recovery & Regional Insights from New York City. Computers, Environment and Urban Systems. Elsevier.
- Verma, P., McKenzie, G. Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage. Environment and Planning B: Urban Analytics and City Science (Special issue on Spatial Inequalities and Cities). Sage
- McKenzie, G., Zhang, H. Platial k-anonymity: Improving location anonymity through temporal popularity signatures. Proceedings of the 12th International Conference on Geographic Information Science. Schloss Dagstuhl-Leibniz-Zentrum für Informatik. (September 13-15; Leeds, UK)
- McKenzie, G., Romm, D., Zhang, H., Brunila, M. (2022) PrivyTo: A privacy preserving location sharing platform. Transactions in GIS. 26(4). pp. 1703-1717. Wiley Press.
Group:
Faculty