University of Guelph
Identifying areas of greatest heat vulnerability and lowest tree canopy cover in Montreal, QC using an index model and MCE in GIS GEOG*4480 Applied Geomatics
Maggie Samson Mary Tress Itzy Kamil
1
Introduction
Heat-related illness resulting from extreme heat events is a significant health risk for
urban residents, and it is disproportionately distributed among urban populations. Among the
most vulnerable to sustained high urban temperatures are infants and those over 65, people with
low income or limited education, racial minorities, and those with limited access to cooling
infrastructure (e.g. air conditioning, pools) (Reid et al., 2009; Ho, Knudby and Huang, 2015).
These populations are more likely to be hospitalized during extreme heat events.
Projections show more severe and more frequent extreme heat events in the next twenty
years in North America (Martin et al., 2012). Concurrent increases in heat-related illness and
death require municipalities to mitigate extreme heat for residents. Increasing residential tree-
cover serves this need and mitigates neighbourhood heat (Reid et al., 2009; Pham et al., 2017).
Municipalities can make the most of limited tree-planting budgets by prioritizing planting in the
most heat-vulnerable areas, and improving life in those neighbourhoods.
Objectives
This research aimed to develop and apply a GIS-based multi-criteria evaluation model
to identify high priority areas for increased tree cover to mitigate heat vulnerability in Montreal,
Quebec. Figure 1 shows the study area. Four objectives were outlined in pursuit of this goal.
1. Identify factors related to heat vulnerability.
2. Develop a GIS model to identify areas of greatest susceptibility to heat-related illness.
3. Apply the model to the City of Montreal; identify census tracts with the least tree-
cover and greatest heat-vulnerability.
4. Evaluate the strengths, weaknesses, and accuracy of the model.
2
Methodology
a summary for each research objective (1-4)
1. A literature review revealed 9 heat-vulnerability factors. Table 1 relates each factor to its
associated metric.
Table 1. Heat-vulnerability factors, their associated study metrics, and supporting
literature
Vulnerability Factor Metric Literature
1
Age
Percent tract residents
age 4 or below
+ age 65 or above
Auger, et al., 2015; Hansen et
al., 2011; Ho, Knudby and
Huang, 2015; Smoyer et
al., 2002; Stewart et al., 2017
2 Income Percent residents with Low-
income Measure
Ho, Knudby and Huang, 2015;
O’Neill, Zanobetti and Schwartz,
2005; Reid, 2009.
3 Education Percent residents with highest
level of education high school
diploma or lower
Harlan et al., 2006; Ho, Knudby
and Huang, 2015; O’Neill et
al., 2005; Reid, 2009;
Figure 1: Study area for heat vulnerability analysis. The map shows the Island of Montreal with 2006 census tract delineations as defined by Statistics Canada. By Maggie Samson (ArcMap 10.5.1)
3
4 Ethnicity Percent tract residents identifying
as non-white
Jesdale et al., 2013; O’Neill et
al., 2005; Reid et al., 2009
5 Social Isolation Percent population living alone Ho, Knudby and Huang, 2015;
Reid et al., 2009; Klinenberg,
2003; Reid et al., 2005; Stewart,
2017
6 Height of
Housing
Structure
Percent population in apartment
buildings of five stories or more
Buscail et al., 2012; Chan et
al., 2007; Smoyer-Tomic et
al., 2002; Xu et al., 2013
7 Age of Housing
Structure
Percent population in buildings
built prior to 1970
Buscail et al., 2012; Ho et
al., 2015; Smoyer-Tomic et
al., 2002; Xu et al., 2013
8 Proximity to
Cooling
Infrastructure
Tract-average distance from
household to cooling facility
Fraser, Chester and Eisenman,
2017; Stewart, 2017; Ville de
Montréal, 2018
9 Canopy Cover Tract area covered by tree canopy Akabari and Taha, 1992;
Leuzinger et. al, 2010; Rahman
et. al, 2017; Shashua-Bara and
Hoffmanab, 2000
2. A GIS model overlaid the 9 heat-vulnerability variables.
The model overlays three types of vulnerability data and compares vulnerability between
census tracts. Figure 2 outlines the model, which runs in three segments to process census data
(variables 1-7), cooling center locational data (8) and LiDAR-derived tree-canopy data (9). An
example of a map created by the model is seen for the variable age in Figure 3, similar maps
were produced for each variable
Table 1. Continued.
4
Figure 2: Functional model for determining priority heat-vulnerable areas.
5
3. The GIS model identified heat-vulnerable tracts in Montreal, QC.
Census data for variables 1-7 were available at the census tract level. Tract vulnerability
was derived from percent of population experiencing each vulnerability variable; for each
variable, the 25% most vulnerable tracts were selected and identified as “priority.”
A Euclidean distance analysis yielded a “distance to nearest cooling center” raster; this
was aggregated to the census tract. The 25% of tracts with greatest average distance to the
nearest cooling center were identified as “priority” tracts.
Classification of the City of Montreal LiDAR point cloud (Figure 4) yielded a municipal
tree-canopy shapefile (Figure 5). Building and ground points were classified and removed;
vegetation points greater than 1.5m tall were output into a 1m digital elevation model (DEM).
Converting the mosaicked DEM to vector yielded a canopy-cover shapefile. Canopy-cover was
aggregated to the census tract, and the 25% of tracts with least tree-cover were identified as
“priority”.
Figure 3: Map of Census Tracts Identified as High Priority for the Age Variable. By Maggie Samson (ArcMap 10.5.1)
6
a)
b)
Figure 4: a) Unclassified LiDAR point cloud showing intensity values b) Classified LiDAR point cloud with high vegetation in orange, buildings in yellow, and ground points in blue
Figure 5: Tree canopy cover for the Island of Montreal derived from LiDAR data. By Itzy Kamil (ArcMap 10.5.1)
a)
b)
7
For each variable (1-9), “priority” tracts were given a single-variable vulnerability index
value of 1. Equation 1 combines vulnerability index values and assigns all Montreal tracts a final
vulnerability score. From the vulnerability scores a bivariate choropleth map was created
showing trends in tree coverage as well as social vulnerability (Figure 6).
Equation 1 assigns a final vulnerability score to each Montreal census tract. Lvlvariable represents the index value (1 or
0) for variables 1-9. Variable weighting imitates Ho, et al. (2015) and Aminipouri et al. (2017).
Hot Spot Getis-Ord Gi*(Figure 7) analysis identified neighbourhoods where multiple
census tracts showed high heat-vulnerability, based on fixed distance band and Euclidean
Figure 6: Bivariate heat map of the Island of Montreal. Tracts are ranked on a spectrum of both Canopy Cover and Socioeconomic Vulnerability. Tracts with High Vulnerability and Poor Cover (highest priority) are designated dark green while tracts with Low Vulnerability and Good Canopy coverage are denoted in light green. By Maggie
Samson (ArcMap 10.5.1)
8
distance parameters. The groups of census tracts identified as vulnerable at the 99% confidence
level were isolated and compared to the bivariate tract vulnerability map to find the worst tracts
in the area. The Hot Spot tool identified areas with significantly higher vulnerability than
surrounding tracts. Identifying these tracts is the final result of the model, and indicates where
increased tree cover is needed most.
4. Ground-Truthing Evaluated Accuracy of Canopy-Cover Output
As per He et al. (2013), census tracts with socioeconomic characteristics of both high-
and low-vulnerability were selected. Using satellite imagery and Google Earth Street View, a
visual comparison of tree-cover in each area was compared to the model’s tree-canopy polygon
output. Figure 8 shows an example of the comparisons between the Google street view images
and the canopy cover shape file produced by the model.
Figure 7: Heat map of census tracts in Montreal that represent areas of highest and lowest heat vulnerability. The three areas that are highly vulnerable with 99% confidence are Ville-Marie (centre-right), Montreal-
Nord/Saint Leonard (top), and LaSalle (bottom). By Maggie Samson (ArcMap 10.5.1)
9
Results
Using the bivariate choropleth and Getis-Ord Gi* Hotspot maps produced by the model
three neighbourhoods in Montreal were identified where vulnerability is greatest and municipal
tree-planting would most improve heat mitigation. These three neighbourhoods are the Ville-
Marie borough, Montreal Nord/Saint Leonard, and LaSalle. Ten census tracts, shown in Figure 9,
were identified within these boroughs as highest priority tracts for increased tree-cover. This
model supports prioritizing tree-cover increases in the ten highest-vulnerability tracts.
Figure 8: Visual comparison between tree canopy cover in high and low vulnerability areas in Montreal. a) Satellite image of Montreal-Nord neighborhood. b) Satellite image of Westmount neighborhood. c) Tree canopy
cover polygon layer overlaying satellite imagery of the Montreal-Nord neighborhood. d) Tree canopy cover polygon overlaying satellite imagery of Westmount neighborhood. The images above are 1 square kilometer
tiles
10
This model has many strengths as a tool for municipal planning. Namely, the model is
accurate for the City of Montreal. Ground-truthing confirmed that the model accurately predicted
areas of high vulnerability based on socioeconomic and tree-cover parameters. Additionally, the
main visual and statistical outputs are comprehensible to a variety of audiences and may be
presented to both planning professionals and citizens involved in municipal decision-making.
Improvements to the model may include aggregating data to a finer scale to more accurately
predict areas of highest vulnerability.
Figure 9: Map of the ten most heat vulnerable census tracts in Montreal. The model has determined that these tracts would benefit the most from increased tree planting to mitigate heat vulnerability. By Maggie Samson
(ArcMap 10.5.1)
11
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Appendix I
Table 2. Data requirements for heat vulnerability analysis
Source Layer Name Scale Year Description of
data
Statistics Canada Census
CTs_Montreal Census tract 2006 Montreal census
tract boundaries
Under_4 Census tract 2006 Percent of census
tract population
age 4 or younger
Over_65 Census tract 2006 Percent of census
tract population
age 65 or older
Low_income Census tract 2006 Percent of census
tract population
with after-tax low
income measures
Racial_makeup Census tract 2006 Percent of census
tract population
identifying as
non-white or
racial minority
Social_isolation Census tract 2006 Percent of census
tract population
living alone
1970_res Census tract 2006 Percent
households living
in buildings built
prior to 1970
High_rise Census tract 2006 Percent
households living
in apartment
buildings of 5
stories or taller
City of Montreal
Sylvain Carrière (Points d’eau)
Loc_cooling Montreal
Metropolitan
Area
2013 Locations of
publicly
accessible water-
based cooling
areas (splash pads,
outdoor pools)
LiDAR Island of
Montreal
2015 LiDAR files
(LAS) for the
Island of
Montreal.
15
Montreal Department of
Culture
Loc_cooling_2 Montreal
Metropolitan
Area
2013 Municipal cultural
places of
Montreal,
includes libraries,
museums, theaters
and cultural
centers, and other
municipal places
that could be
visited in a
heatwave. Data
includes longitude
and latitudes for
each location
Infrastructure, Roads and
Transportation Service –
Geomatics Division
CARTO-VEG-
TREE.shp
Montreal
Metropolitan
Area
2017 This data includes
a shape file of
canopy cover as
well as individual
tree shapefiles for
the city of
Montreal. It is
represented in
surfaces, even for
isolated trees.
CARTO-VEG-
BOISE.shp
United States Geologic Survey Imagery Island of
Montreal
2017 Multispectral
imagery for use in
the creation of an
NDVI layer to aid
in the
classification of
vegetation from
the LiDAR point
cloud.
Roberto Rocha (Montreal
Gazette)
Montreal.shp Municipalities
of Montreal
2011 Shapefile of
Montreal and each
municipality
Table 2. Continued.