Platial Analysis Lab

New lab member – Hongyu Zhang

September 3, 2019

As the new academic year gets under way, we would like to welcome a new doctoral student member to the Platial Analysis Lab team…

Keynote at the ACM SIGSPATIAL LENS workshop

August 30, 2019

Professor McKenzie will give a keynote at the 3rd ACM SIGSPATIAL Workshop on Analytics for Local Events and News in Chicago on Nov 5th…

Lab receives 5 yrs of funding for platial science research

July 24, 2019

We are happy to announce that the Platial Analysis Lab (with Professor Grant McKenzie as PI) has been awarded 5 years of funding from the Natural Sciences and Engineering Research Council of Canada…

Presentation to the MTL Machine Learning Group

April 18, 2019

Last week Grant McKenzie presented a talk entitled “A machine learning approach to identifying urban neighborhood names” at the Montreal Machine Learning Group meetup in downtown Montreal. Check out the video below.

Urban mobility in the sharing economy: A spatiotemporal comparison of shared mobility services

McKenzie, Grant

Computers, Environment and Urban Systems, 2019. Journal Article

Abstract | Links | BibTeX

@article{McKenzieCEUS2019,
title = {Urban mobility in the sharing economy: A spatiotemporal comparison of shared mobility services},
author = {Grant McKenzie },
url = {https://www.grantmckenzie.com/academics/McKenzie_CEUS2019.pdf},
year = {2019},
date = {2019-09-12},
journal = {Computers, Environment and Urban Systems},
abstract = {The influx of micro-mobility services, such as dockless scooter-share and e-bikes, in many cities are contributing to a substantial change in urban transportation with adoption rates reminiscent of other shared-mobility services, such as ride-hailing, years prior. Touted as a solution to the last mile problem, a multitude of micro-mobility companies have situated themselves in urban centers promising low cost alternative transportation options for short, urban travel. The rapid arrival of these companies, however, has left little time for city officials, transportation planners, and citizens to assess the demand for these services and compare them to existing transportation options. In this work, we investigate two key aspects of these micro-mobility services. First, we identify the spatial and temporal differences between these mobility companies and highlight the nuanced differences in usage patterns. Second, we compare these new services to an existing mode of transportation, namely automobile-based ride-hailing, with regards to differences in travel time within a city. The results of these analyses indicate that while many micro-mobility companies are spatiotemporally similar, there are notable differences in where and when these services are used. Similarly, we find that automobile travel is not always the fastest means of transportation within an urban setting. During periods of heavy traffic congestion, e.g., rush hour, micro-mobility services offer a faster means of travel within the city. The findings presented in this work offer evidence on which to inform urban planning and transportation policy with respect to shared mobility services, free floating vehicles, and alternative urban transportation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C.

McKenzie, Grant

Journal of Transport Geography, 78 , pp. 19-28, 2019. Journal Article

Abstract | Links | BibTeX

A user-generated data based approach to enhancing location prediction of financial services in sub-Saharan Africa

McKenzie, Grant; Slind, Todd R

Applied Geography, 2019. Journal Article

Abstract | Links | BibTeX

@article{71,
title = {A user-generated data based approach to enhancing location prediction of financial services in sub-Saharan Africa},
author = {Grant McKenzie and Todd R Slind},
url = {https://www.sciencedirect.com/science/article/pii/S0143622818302078},
doi = {10.1016/j.apgeog.2019.02.005},
year = {2019},
date = {2019-01-01},
journal = {Applied Geography},
abstract = {The recent increase in user-generated content and social media adoption in developing countries offers an unprecedented opportunity to better understand the accessibility and spatial distribution of financial services in sub-Saharan Africa. Financial inclusion has been identified as a priority by multiple agencies in the region and on-the-ground efforts are currently underway to identify previously unknown financial access points in numerous developing African countries. Existing techniques for estimating the location of these access points rely on spatial analysis of often outdated or unsuitable publicly available datasets such as population density, road networks, etc. as well as expensive and time consuming surveys of locals in the region. In this work we propose an approach to augment existing spatial data analysis techniques through the inclusion of user-generated geo-content and geo-social media data. Though a comparison of standard regression models and machine learning techniques, this work proposes the use of alternative data sources to build prediction models for identifying financial access locations in countries where current estimation models are insufficient. Through a better understanding of geospatial distribution patterns this work aims at reducing data acquisition costs and providing decision makers with critical data more quickly and efficiently. Finally, we present a mobile application build on the outcomes of this analysis that is currently being used to better inform on-the-ground data collection efforts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}