Hi, I’m Christopher!
I am an Environmental Data Scientist at Ryerson University in the Urban Forest Research & Ecological Disturbance (UFRED) Group. At Ryerson, I am a 4th year PhD Candidate in the Environmental Applied Science and Management Program (EnSciMan). My research focuses on understanding the relationship of our digital representations (data) for the natural environment. Using Machine and Deep Learning methods, I explore data relationships and how we can better understand and interpret the environment, by digging into the black box of machine learning.
Interests
- Geostatistics
- Scale
- Urban Ecology
- Urban Forestry
- Soil Science
- Spatial Analysis
- Remote Sensing
- Data Science
Education
PhD in Environmental Applied Science and Management, 2017 - today
Ryerson University
MSc. in Geography (Soil Sciences), 2012 - 2015
Simon Fraser University
Masters of Spatial Analysis (MSA), 2011 - 2012
Ryerson University
BA in Geographical Analysis, 2007 - 2011
Ryerson University
Skills
R
Advanced
QGIS & ESRI ArcGIS
Expert
LaTeX
Beginner
SQL
Intermediate
Python
Intermediate
Experience
Graduate Writing Consultant
Ryerson University - Student Life and Learning Support
Sessional - EUS 402, CODG 212, GEO 141, GEO 110
Ryerson University
Featured Publications
A multimethod approach for county-scale geospatial analysis of emerging infectious diseases. A cross-sectional case study of COVID-19 incidence in Germany
As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.
Contact
- 44 Gerrard St E, Toronto, M5B 1G3
- GitHub