Platial Analysis Lab

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.

Platial at the Annual AAG Meeting in D.C.

April 10, 2019

Another American Association of Geographers (AAG) meeting has come to a close, this time in Washington, D.C. By all accounts the meeting was a success and it is always great to catch up with spatial scientists in their natural habitat.

Montreal Building Footprints

March 8, 2019

Microsoft just recently published their Building Footprints dataset for Canada. A quick analysis of the coverage for the Island of Montreal shows that there are considerable differences…

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 Existing techniques for estimating the location of these access points rely on spatial analysis of often outdated or unsuitable publicly available datasets such as population density, road networks, etc.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}
}