Platial Analysis Lab

Morgan Guttman presents summer internship project

January 23, 2020

Morgan Guttman, a research assistant in the lab (and Anthropology major) recently presented the work he completed as part of his summer internship award…

McGill CSCDS Data Science Expo

January 13, 2020

Professor Grant McKenzie will be presenting at McGill’s Center for Social and Cultural Data Science (CSCDS) Data Science Expo on January 21 at McGill’s new residence hall…

Editorial for Geoprivacy Special Issue

December 26, 2019

Our special issue on the topic of geospatial privacy and security was recently published in JOSIS. We wrote a guest editorial in support of the special issue…

The Environmental Impact of Micro-mobility Services

December 16, 2019

Given the recent discussions surrounding the environmental impact of new e-scooters, I thought I would do a quick comparison of emissions…

Spatial Data Science Symposium

December 12, 2019

The 2019 Spatial Data Science Symposium, “Setting the Spatial Data Science Agenda,” came to a close today in Santa Barbara, California…

Platial Lab at SIGSpatial 2019

November 7, 2019

…while the main conference always has a range of interesting talks one of the more disturbing trends I noticed this year was a move from “identifying fake content” to “generating fake content”…

New Editorial on GeoAI

October 21, 2019

Professor McKenzie and colleagues wrote an editorial on GeoAI for a special issue of the International Journal of Geographical Information Sciences. Click through to read a pre-print…

McKenzie, Grant

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

Computers, Environment and Urban Systems, 2019.

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}
}

McKenzie, Grant

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

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

Abstract | Links | BibTeX

McKenzie, Grant; Slind, Todd R

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

Applied Geography, 2019.

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}
}