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.