Papers – Page 2 – Platial Analysis Lab

PrivyTo: A privacy preserving location sharing platform

March 18, 2022

In our recent forthcoming publication, we introduce PRIVYTO: A location sharing platform designed with user privacy and security in mind…

Rehumanize geoprivacy: from disclosure control to human perception

February 16, 2022

Congratulations to lab member Hongyu Zhang for this new paper as lead author published in GeoJournal…

Regional variation in place visit behavior during a global pandemic

December 24, 2021

In a recent paper, Prof. McKenzie and colleague investigate how visits to places across the United States have changed over the course of the COVID-19 pandemic…

Two papers in upcoming symposia

November 30, 2021

Papers published by lab members were recently accepted into two upcoming symposia this December…

Two new workshop papers

October 15, 2021

Two peer-reviewed papers from lab members were recently accepted to leading workshops…

IJGIS GeoAI Editorial Webinar

February 2, 2021

Professor McKenzie will join coauthors to present their editorial GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond…

Countries vary in place-based activity response to COVID-19 policies

May 18, 2020

We recently published a paper comparing countries based on the relationship between place-based activity patterns and government enacted policy…

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

McKenzie, Grant; Zhang, Hongyu

Platial k-anonymity: Improving location anonymity through temporal popularity signatures Best Paper Proceedings Article

In: Proceedings of the 12th International Conference on Geographic Information Science, Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik, 2023, ISSN: 1868-8969.

Abstract | Links | BibTeX

@inproceedings{McKenzie2023b,

title = {Platial k-anonymity: Improving location anonymity through temporal popularity signatures},

author = {Grant McKenzie and Hongyu Zhang },

url = {https://drops.dagstuhl.de/opus/volltexte/2023/18904/},

issn = {1868-8969},

year  = {2023},

date = {2023-08-31},

urldate = {2023-08-31},

booktitle = {Proceedings of the 12th International Conference on Geographic Information Science},

volume = {277},

publisher = {Schloss Dagstuhl -- Leibniz-Zentrum fur Informatik},

series = {Leibniz International Proceedings in Informatics (LIPIcs)},

abstract = {While it is increasingly necessary in today's digital society, sharing personal location information comes at a cost.  Sharing one's precise place of interest, e.g., Compass Coffee, enables a range of location-based services, but substantially reduces the individual's privacy.  Methods have been developed to obfuscate and anonymize location data while still maintaining a degree of utility.  One such approach, spatial k-anonymity, aims to ensure an individual's level of anonymity by reporting their location as a set of k potential locations rather than their actual location alone.  Larger values of k increase spatial anonymity while decreasing the utility of the location information.  Typical examples of spatial k-anonymized datasets present elements as simple geographic points with no attributes or contextual information.  In this work, we demonstrate that the addition of publicly available contextual data can significantly reduce the anonymity of a k-anonymized dataset.  Through the analysis of place type temporal visitation patterns, hours of operation, and popularity values, one's anonymity can decreased by more than 50 percent.  We propose a platial k-anonymity approach that leverages a combination of temporal popularity signatures and report the amount that k must increase in order to maintain a certain level of anonymity. Finally, a method for reporting platial k-anonymous regions is presented and the implications of our methods are discussed.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}