CAG 2021: Call for papers

March 12, 2021

The 71st annual meeting and conference of the Canadian Association of Geographers (CAG) will take place virtually this year June 7 – 11. The conference will be hosted by the University of Northern British Columbia and take place entirely online. PhD student Hongyu Zhang is co-organizing three sessions at the conference on the topic of geospatial data science. If your work fits into this broad topic, please consider submitting to one of the sessions. Deadline to submit papers/abstracts is March 26th.

See the call for papers. More details on the sessions below:

1. Lightning Talks (3-5 minute presentations [question period at the end])

We are inviting research presentations for this lightning talks session on a variety of geospatial data science topics. Each lightning talk will be a maximum of five minutes long. Lightning talks are an excellent way to share progress and preliminary results of research involving geospatial data and methods. We strongly encourage work-in-progress submissions from researchers at all levels. This is an excellent opportunity to get feedback on emerging research ideas.

2. Paper Presentations (fifteen-minute presentations [10+5])

We are soliciting research paper presentations on topics that involve geographic information science and data science applied or theoretical problems or methodologies. We encourage submissions involving research at the intersection of data science and geographical information science. We aim to showcase how geospatial data and computational methods are being used across the range of applications encountered in geographical research.

3. Panel Discussion (5 minute intro from each panelist + questions)

Alongside this special session, we will host a discussion roundtable where we intend to identify common themes, trends, and skillsets encountered by geographers and domain specialists working with geospatial data in different applications within geography. We invite CAG participants to discuss what it means to do geospatial data science based on their own experiences. We are actively seeking a diverse cast of panel members who range in age, career experience, ethnic background and field of study, and as such, we encourage early-career researchers/upper year graduate students, post docs, or assistant professors to participate. The goal is to have an inter-disciplinary and inter-personal discussion surrounding the benefits and challenges of geospatial data science in their own research.

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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}

}