Join the Lab! PhD and Master’s degree opportunities

November 3, 2020

The Platial Analysis Lab is currently accepting applications for funded graduate research from motivated students at the PhD and Master’s levels. Potential research topics are listed below.

Topics of interest include (but are not limited to):
• Analysis of micromobility services and human activity patterns
• Social and computational models for preserving location privacy

The lab will provide stipends to cover living expenses in Montreal, but students are expected to apply for additional funding. More information on the McGill graduate student program is available at:
• https://www.mcgill.ca/geography/graduate
• https://www.mcgill.ca/gps/

Requirements
• Bachelor’s (or Master’s) degree in Geography, Computer Science, Information science, or related field.
• Preference will be given to students with programming and spatial statistics experience.

If any of these (or related) topics are of interest to you, please check out this page and send your CV and Cover Letter (including a general research topic idea) to Grant McKenzie at grant.mckenzie@mcgill.ca before Dec. 15, 2020

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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