Grant McKenzie
Associate Professor

Grant McKenzie is an associate professor in the Department of Geography at McGill University in Montréal, Canada where he leads the Platial Analysis Lab, an interdisciplinary research group that works at the intersection of data science and behavioural geography. Much of Dr. McKenzie’s work examines how human activities vary within and between local neighbourhoods and global communities. This has driven his applied interests in financial accessibility, geoprivacy, and micro-mobility services as well as the broader role that spatial data science plays at the intersection of information technologies and society. Dr. McKenzie is a founding member of the Seattle-based start-up consultancy Spatial Development International and has worked as a data scientist and software developer for a range of NGOs and leading technology companies.

Priyanka Verma
Doctoral Candidate

Priyanka Verma is a PhD student in the Department of Geography at McGill University. Her research leverages GIS and machine learning techniques to study socioeconomic inequities in existing shared micro-mobility service networks. She received her M.Sc. in Geographical Information Science from Clark University where she developed automated workflows to expand access to map comparison metrics. Prior to this, she worked as a Data Analyst (GIS) at Foot Locker, Inc. supporting market planning, real estate, and supply chain efforts. She also holds a B.A. in Geography from City University of New York-Hunter College.

Dan Qiang
Doctoral Candidate

Dan Qiang is a PhD student in the Department of Geography at McGill University. She completed her master’s degree in architecture from the College of Architecture and Urban Planning, Tongji University in 2022 and her bachelor’s degree in architecture from Hunan University in 2019. Before joining the Platial Analysis Lab, Dan was a member of the Computational Urban Design Lab led by Dr Yu Ye. Given her interdisciplinary background, she is passionate about improving the built environment and generating urban vitality. Her research spans urban morphology, human mobility and GIScience. Her professional goal is to bridge the gap between Urban Planning and Spatial Data Science.

Daniel Romm
Doctoral Student

Daniel Romm is a Doctoral student in Geography at McGill University, where he is researching free-floating micromobility systems using quantitative methods. Daniel has previously studied these mobility systems using qualitative methods for several years at the University of Toronto, where he completed his Bachelor’s degree in Human Geography in 2020. Daniel also has an interest in theories of “place”, as well as geoprivacy.

Mackenzie Johnston
Master's Student

Mackenzie Johnston is a Master of Science student in Geography at McGill University. She completed her Bachelor of Science in Mathematics & Sustainability at Dalhousie University, where she gained an interest in data driven geography. Her research will focus on the link between urban mobility, climate change, and socio-economic equity through data science and geospatial analysis. Mackenzie is particularly interested in how new transportation technologies impact urban communities, with a focus on developing strategies for sustainable urban mobility.

Research Assistants

Alumni

Collaborating Labs

Space-Time for Knowledge Organization Lab
University of Vienna

GeoKEA Lab
University of Canterbury, Christchurch, New Zealand

Geospatial Artificial Intelligence Lab
University of Buffalo, SUNY

Geospatial Data Science Lab
University of Wisconsin-Madison

Zhang, Hongyu; McKenzie, Grant

Geoprivacy knowledge, attitudes, and behaviours in contemporary China Journal Article

In: Geographical Review, 2024.

Abstract | Links | BibTeX

Qiang, Dan; McKenzie, Grant

Navigating the Post-Pandemic Urban Landscape: Disparities in Transportation Recovery & Regional Insights from New York City Journal Article

In: Computers, Environment and Urban Systems, vol. 110, pp. 102111, 2024.

Abstract | Links | BibTeX

Verma, Priyanka; McKenzie, Grant

Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage Journal Article

In: Environment and Planning B: Urban Analytics and City Science, 2024.

Abstract | Links | BibTeX

@article{Verma2024,

title = {Regional Comparison of Socio-Demographic Variation in Urban E-scooter Usage},

author = {Priyanka Verma and Grant McKenzie},

url = {https://grantmckenzie.com/academics/Verma_2024.pdf},

year  = {2024},

date = {2024-03-26},

urldate = {2024-03-09},

journal = {Environment and Planning B: Urban Analytics and City Science},

abstract = {In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Brunila, Mikael; LaViolette, Jack; CH-Wang, Sky; Verma, Priyanka; Féré, Clara; McKenzie, Grant

Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City Best Paper Proceedings Article

In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP2023), 2023.

Abstract | Links | BibTeX

Members

Grant McKenzie

Priyanka Verma

Dan Qiang

Daniel Romm

Mackenzie Johnston

Research Assistants

Talia Cohen-Fette

Anoushka Oke

Alumni

Mikael Brunila

Mikael Brunila

Clara Féré

Clara Féré

Hongyu Zhang

Hongyu Zhang

Siyuan Chen

Siyuan Chen

Nicholas Foisy

Nicholas Foisy

Kevin Nam

Kevin Nam

Maya Shyevitch

Maya Shyevitch

Elodie Ithier

Elodie Ithier

Jonah Levitt

Jonah Levitt

Emily Chen

Emily Chen

Myeong Lee

Myeong Lee

Collaborating Labs

Friendly Cities Lab

Space-Time for Knowledge Organization Lab

GeoKEA Lab

Geospatial Artificial Intelligence Lab

Geospatial Data Science Lab