Scooter-share – Platial Analysis Lab

Daniel Romm interviewed on CBC Radio Noon

August 24, 2022

Lab member Daniel Romm was recently interviewed on CBC’s Radio Noon regarding e-scooters…

TRB webinar on spatiotemporal patterns of micromobility

October 6, 2021

On Oct 8th, Prof. McKenzie gave a presentation to the Transportation Research Board AMS 50 committee…

Master’s degree opportunities for 2022

September 10, 2021

The Platial Analysis Lab has funding to support two graduate students, at the Master’s level, with interest in the broad topics of either Geo-Privacy or Urban Mobility…

Chapter in new book on the urban sharing economy

August 16, 2021

Professor Grant McKenzie and colleague contributed Chapter 14 to the recently published book A Modern Guide to the Urban Sharing Economy…

New paper measuring regional similarity through mobility signatures

July 5, 2021

A novel approach to regional similarity based on human mobility as proxied through micro-mobility trips…

Animating micro-mobility trajectories

October 12, 2020

We are currently building a platform to visualize animated urban trajectories… A demo version showing 24 hours of e-scooter data in Berlin is available here…

2020 MSSI Innovation Fund Recipient

March 18, 2020

The lab is happy to be awarded a grant from the MSSI Innovation Fund to further explore the environmental impact of micromobility services across Canada.

E-scooter Interview on CBC Radio Noon

February 25, 2020

Professor McKenzie discusses the City of Montreal’s recent e-scooter ban on CBC Radio Noon Quebec with Shawn Apel…

Leading Workshop and Lecture at Brown University

November 19, 2019

Professor McKenzie will be giving a lecture and leading a visualization workshop at Brown University on November 21 and 22, respectively…

Recent News

New NSERC funding for homomorphic encryption research

May 30, 2023

Equitable, accessible, and sustainable mobility workshops

April 24, 2023

Congratulations to honours student Maya Shyevitch

April 13, 2023

Platial Lab at AAG 2023

April 4, 2023

The 4th Spatial Data Science Symposium

March 23, 2023

Recent Publications

McKenzie, Grant; Battersby, Sarah; Setlur, Vidya

MixMap: A user-driven approach to place-based semantic similarity Journal Article

In: Cartography and Geographic Information Science, pp. 1-16, 2023.

Abstract | Links | BibTeX

@article{McKenzie2023,

title = {MixMap: A user-driven approach to place-based semantic similarity},

author = {Grant McKenzie and Sarah Battersby and Vidya Setlur},

doi = {10.1080/15230406.2023.2176930},

year  = {2023},

date = {2023-03-02},

urldate = {2023-03-02},

journal = {Cartography and Geographic Information Science},

pages = {1-16},

publisher = {Taylor & Francis},

abstract = {What other locations are like my neighborhood? How? Why? The heart of many spatial analyses is in finding similarities or dissimilarities between locations. Discovering patterns and interpreting similarity is a complicated process that is based on both the spatial characteristics and the semantics or meaning that we assign to place. Human conceptualization of similarity in locations is multi-faceted and cannot be captured with a simple assessment of single numeric attributes like population density or median income; however, these quantifiable attributes are the basis for an initial pass of sense-making. MixMap facilitates the incorporation of similarity measures and spatial analytics to provide an information reduction (or semantic generalization) that brings the user closer to actionable insights. Through a preliminary evaluation of MixMap, we found that the tool supports the geospatial inquiry of determining similarity between regions, where participants can manipulate individual weights of the various attributes describing these locations. Based on feedback and observations from the study, we discuss potential implications and considerations for exploring the role of context and additional place-specific parameters for computing similarity, as well as understanding the nuances of semantics for place similarity in geospatial analysis tools.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

What other locations are like my neighborhood? How? Why? The heart of many spatial analyses is in finding similarities or dissimilarities between locations. Discovering patterns and interpreting similarity is a complicated process that is based on both the spatial characteristics and the semantics or meaning that we assign to place. Human conceptualization of similarity in locations is multi-faceted and cannot be captured with a simple assessment of single numeric attributes like population density or median income; however, these quantifiable attributes are the basis for an initial pass of sense-making. MixMap facilitates the incorporation of similarity measures and spatial analytics to provide an information reduction (or semantic generalization) that brings the user closer to actionable insights. Through a preliminary evaluation of MixMap, we found that the tool supports the geospatial inquiry of determining similarity between regions, where participants can manipulate individual weights of the various attributes describing these locations. Based on feedback and observations from the study, we discuss potential implications and considerations for exploring the role of context and additional place-specific parameters for computing similarity, as well as understanding the nuances of semantics for place similarity in geospatial analysis tools.

doi:10.1080/15230406.2023.2176930

Romm, Daniel; Verma, Priyanka; Karpinski, Elizabeth; Sanders, Tracy; McKenzie, Grant

Differences in First-Mile and Last-Mile Behaviour in Candidate Multi-Modal Boston Bike-share Micromobility Trips Journal Article

In: Journal of Transport Geography, vol. 102, iss. June, 2022.

Links | BibTeX

@article{Romm2022,

title = {Differences in First-Mile and Last-Mile Behaviour in Candidate Multi-Modal Boston Bike-share Micromobility Trips},

author = {Daniel Romm and Priyanka Verma and Elizabeth Karpinski and Tracy Sanders and Grant McKenzie},

url = {https://doi.org/10.1016/j.jtrangeo.2022.103370},

year  = {2022},

date = {2022-06-07},

urldate = {2022-06-07},

journal = {Journal of Transport Geography},

volume = {102},

issue = {June},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

https://doi.org/10.1016/j.jtrangeo.2022.103370

McKenzie, Grant; Romm, Daniel; Zhang, Hongyu; Brunila, Mikael

PrivyTo: A privacy preserving location sharing platform Journal Article

In: Transactions in GIS, 2022.

Abstract | Links | BibTeX

@article{McKenzie2022,

title = {PrivyTo: A privacy preserving location sharing platform},

author = {Grant McKenzie and Daniel Romm and Hongyu Zhang and Mikael Brunila},

url = {https://grantmckenzie.com/academics/McKenzie2022_PrivyTo.pdf

https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12924},

doi = {10.1111/tgis.12924},

year  = {2022},

date = {2022-05-12},

urldate = {2022-03-11},

journal = {Transactions in GIS},

publisher = {Wiley Press},

abstract = {Concern over the privacy of one's personal location is at an all-time high, yet the desire to share our lives with friends, family, and the public persists. Current methods and applications for sharing location content with the range of people in our lives are sorely lacking.  Application users are often limited to sharing a single spatial resolution with all individuals, regardless of relation, and with little control over how this content is shared.  Processes for sharing typically involve allowing a for-profit company access to your location before it can be transmitted to the intended recipient.  In this work we propose a set of design goals and a design pattern for sharing personal location information that are realized through a prototype mobile web application.  Our approach is built on the novel idea of obfuscated and encrypted location views, and promotes a uniquely open method for sharing.  The intention of this paper is to demonstrate that location sharing need not require one to expose private location information to third parties, and that methods exist to put an individual back in control of their content.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Concern over the privacy of one's personal location is at an all-time high, yet the desire to share our lives with friends, family, and the public persists. Current methods and applications for sharing location content with the range of people in our lives are sorely lacking. Application users are often limited to sharing a single spatial resolution with all individuals, regardless of relation, and with little control over how this content is shared. Processes for sharing typically involve allowing a for-profit company access to your location before it can be transmitted to the intended recipient. In this work we propose a set of design goals and a design pattern for sharing personal location information that are realized through a prototype mobile web application. Our approach is built on the novel idea of obfuscated and encrypted location views, and promotes a uniquely open method for sharing. The intention of this paper is to demonstrate that location sharing need not require one to expose private location information to third parties, and that methods exist to put an individual back in control of their content.

https://grantmckenzie.com/academics/McKenzie2022_PrivyTo.pdf
https://onlinelibrary.wiley.com/doi/full/10.1111/tgis.12924
doi:10.1111/tgis.12924

Zhang, Hongyu; McKenzie, Grant

Rehumanize geoprivacy: From disclosure control to human perception Journal Article

In: GeoJournal, 2022.

Abstract | Links | BibTeX

@article{Zhang2022,

title = {Rehumanize geoprivacy: From disclosure control to human perception},

author = {Hongyu Zhang and Grant McKenzie},

doi = {https://doi.org/10.1007/s10708-022-10598-4},

year  = {2022},

date = {2022-02-16},

urldate = {2022-01-31},

journal = {GeoJournal},

publisher = {Springer},

abstract = {Traditional boundaries between people are vanishing due to the rise of Internet of Things technology. Our smart devices keep us connected to the world, but also monitor our daily lives through an unprecedented amount data collection. As a result, defining privacy has become more complicated. Individuals want to leverage new technology (e.g., making friends through sharing private experiences) and also avoid unwanted consequences (e.g., targeted advertising).  In the age of ubiquitous digital content, geoprivacy is unique because concerns in this area are constantly changing and context-dependent. Multiple factors influence people’s location disclosure decisions, including time, culture, demographics, spatial granularity, and trust. Existing research primarily focuses on the computational efforts of protecting  geoprivacy, while the variation of geoprivacy perceptions has yet to receive adequate attention in the data science literature. In this work, we explore geoprivacy from a cognate-based perspective and tackle our changing perception of the concept from multiple angles. Our objectives are to rehumanize this field from contextual, cultural, and economic dimensions and highlight the uniqueness of geodata under the broad topic of privacy. It is essential that we understand the spatial variations of geoprivacy perceptions in the era of big data. Masking geographic coordinates can no longer fully anonymize spatial data, and targeted geoprivacy protection needs to be further investigated to improve user experience.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

Traditional boundaries between people are vanishing due to the rise of Internet of Things technology. Our smart devices keep us connected to the world, but also monitor our daily lives through an unprecedented amount data collection. As a result, defining privacy has become more complicated. Individuals want to leverage new technology (e.g., making friends through sharing private experiences) and also avoid unwanted consequences (e.g., targeted advertising). In the age of ubiquitous digital content, geoprivacy is unique because concerns in this area are constantly changing and context-dependent. Multiple factors influence people’s location disclosure decisions, including time, culture, demographics, spatial granularity, and trust. Existing research primarily focuses on the computational efforts of protecting geoprivacy, while the variation of geoprivacy perceptions has yet to receive adequate attention in the data science literature. In this work, we explore geoprivacy from a cognate-based perspective and tackle our changing perception of the concept from multiple angles. Our objectives are to rehumanize this field from contextual, cultural, and economic dimensions and highlight the uniqueness of geodata under the broad topic of privacy. It is essential that we understand the spatial variations of geoprivacy perceptions in the era of big data. Masking geographic coordinates can no longer fully anonymize spatial data, and targeted geoprivacy protection needs to be further investigated to improve user experience.

doi:https://doi.org/10.1007/s10708-022-10598-4

PRESS & PRESENTATIONS

Photographs


	
    
		
    
        
            Lab BBQ - Celebrating the end of Spring semester.
            	                    
        
    

    
        
            Lab member Maya Shyevitch presented her honours th
            	                    
        
    

    
        
            We attended the @esricanada GIS in education and r
            	                    
        
    

    
        
            A good showing from the lab at the Spatial Knowled
            	                    
        
    

    
        
            Kicking off the first @mcgillu -hosted talk of the
            	                    
        
    

    
        
            Axe throwing with the lab (and friends). Thanks fo
            	                    
        
    

    
        
            The lab is co-organizing the 8th conference on Spa
            	                    
        
    

    
        
            New visualisation paper titled "Differences in fir
            	                    
        
    

    
        
            We are happy to announce that the lab was just awa
            	                    
        
    

    
        
            Today we are soft launching #privyto, a personal l
            	                    
        
    
    

	

			
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Twitter Feed

The lab recently received new #NSERC funding for homomorphic encryption research. We will be posting a call for gr… https://t.co/1PW1TzBlBn6 days

Finally, a photo of the lab members together! Celebrating the end of another great academic year at @mcgillu https://t.co/nueGInkidG4 weeks

The Thematic Sessions, Keynotes, and Interviewees have been announced for this year's Spatial Data Science Symposiu… https://t.co/JW64TcWoXp1 month

RT @grantdmckenzie: Special issue CfP: "Environmentally and Socially Sustainable Mobility" in the journal Computers, Environment and Urban…1 month

RT @mikaelbrunila: super excited for the #LMSocial23 talk by @laurenfklein tomorrow: "How Words Lead to Justice: Modeling Language Change i…1 month