Unveiling the Insights of Vancouver's Street Trees

| Exploratory Data Analysis

Kyounggu Yeo | Data Analyst

1. Introduction

Trees are an essential part of our environment and play a crucial role in maintaining a healthy and sustainable city. They are not just an aesthetically pleasing addition to our concrete jungle, but also provide a range of benefits that make our lives better.

Street trees provide the city with a wide range of benefits: a friendly environment and a clean atmosphere for humans and animals. The city should compose a habitable environment for the trees. Providing the conditions to grow healthy, long-lived trees is the best approach to minimize the conflicts between trees and the surrounding urban infrastructure. (Ely, Martin, 2009)

In this analysis, we will focus on street trees in the City of Vancouver and examine their distribution by type, location, and other characteristics. By doing so, we hope to gain insight into the level of tree diversity in different areas of the city and the implications for urban planning and development.

Data Source and Overview

The data for this analysis is obtained from the City of Vancouver's Open Data Portal. The dataset contains information about street trees in the City of Vancouver including the location, species, diameter, and characteristics of the trees. Please follow the link below to access the dataset. City of Vancouver Open Data Portal > Streets and Transportation > Street trees.

UBC Data Science faculty has done of wrangling and cleaning for the origianl dataset, and provided with a modified version of the dataset. The subset of the original data may or may not be a representative sample of the original data set.

The street_trees dataset is a table composed of 21 columns, Unnamed: 0, std_street, on_street, species_name, neighbourhood_name, date_planted, diameter, street_side_name, genus_name, assigned, civic_number, plant_area, curb, tree_id, height_range_id, on_street_block, cultivar_name, root_barrier, latitude, longitude stored in a .csv file.

Dataset schema

The schema of the dataset is represented by the following columns:

• Unnamed: 0 : N/A.
• std_street : Street name of the site at which the tree is associated with.
• on_street : The name of the street at which the tree is physically located on.
• species_name : Species name.
• neighbourhood_name : City's defined local area in which the tree is located. For more information, see theLocal Area Boundary Datapage.
• date_planted : The date of planting in YYYYMMDD format. Data for this field may not be available for all trees.
• diameter : DBH in inches. (DBH stands for diameter of tree at breast height)
• street_side_name : The street side which the tree is physically located on. (Even, Odd or Median (Med))
• genus_name : Genus name.
• assigned : Indicates whether the address is made up to associate the tree with a nearby lot. (Y=Yes or N=No)
• civic_number : Street address of the site at which the tree is associated with.
• plant_area : B = behind sidewalk, G = in tree grate, N = no sidewalk, C = cutout, a number indicates boulevard width in feet.
• curb : Curb presence. (Y = Yes, N = No)
• tree_id : Numerical ID.
• height_range_id : 0-10 for every 10 feet (e.g., 0 = 0-10 ft, 1 = 10-20 ft, 2 = 20-30 ft, and 10 = 100+ ft)
• on_street_block : The street block at which the tree is physically located on.
• cultivar_name : Cultivar name.
• root_barrier : Root barrier installed. (Y = Yes, N = No)
• latitude : Geographic coordinate that specifies the north–south position of a point on the Earth's surface.
• longitude : Geographic coordinate that specifies the east–west position of a point on the Earth's surface, or the surface of a celestial body.

Data visualization methods and techniques were utilized in this analysis to analyze the information within the street_trees dataset.

2. Data Cleaning

The first step in our analysis is to gather data on street trees in Vancouver. Fortunately, the City of Vancouver has made this information publicly available through its Open Data Portal. We collected data on street trees across the city, including their location, species, diameter, and other characteristics.

# Import all the required libraries needed for EDA
import pandas as pd
import numpy as np
import altair as alt

#!conda install -c conda-forge folium=0.5.0 --yes
import folium

# Import the street trees dataset
street_trees = pd.read_csv("https://raw.githubusercontent.com/UBC-MDS/data_viz_wrangled/main/data/Trees_data_sets/small_unique_vancouver.csv").dropna()
street_trees.head()

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
5	17458	BUTE ST	BUTE ST	PERSICA	West End	2012-04-05	3.0	EVEN	PARROTIA	N	...	C	Y	233622	VANESSA PERSIAN IRONWOOD	1	1100	VANESSA	N	49.281906	-123.133076
9	28279	MATAPAN CRESCENT	MATAPAN CRESCENT	ZUMI	Renfrew-Collingwood	2008-03-13	3.0	ODD	MALUS	N	...	12	Y	102612	REDBUD CRABAPPLE	1	3200	CALOCARPA	Y	49.257272	-123.030023
10	1684	KINGSWAY	KINGSWAY	SYLVATICA	Kensington-Cedar Cottage	2009-11-06	3.0	MED	FAGUS	N	...	8	Y	228772	DAWYCK'S BEECH	1	1500	DAWYCKII	N	49.248839	-123.073073

5 rows × 21 columns

It's important to clean the data. This includes checking for missing values, correcting errors, and removing irrelevant columns.

# Information of the data frame in the dataset
street_trees.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1653 entries, 1 to 4997
Data columns (total 21 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Unnamed: 0          1653 non-null   int64  
 1   std_street          1653 non-null   object 
 2   on_street           1653 non-null   object 
 3   species_name        1653 non-null   object 
 4   neighbourhood_name  1653 non-null   object 
 5   date_planted        1653 non-null   object 
 6   diameter            1653 non-null   float64
 7   street_side_name    1653 non-null   object 
 8   genus_name          1653 non-null   object 
 9   assigned            1653 non-null   object 
 10  civic_number        1653 non-null   int64  
 11  plant_area          1653 non-null   object 
 12  curb                1653 non-null   object 
 13  tree_id             1653 non-null   int64  
 14  common_name         1653 non-null   object 
 15  height_range_id     1653 non-null   int64  
 16  on_street_block     1653 non-null   int64  
 17  cultivar_name       1653 non-null   object 
 18  root_barrier        1653 non-null   object 
 19  latitude            1653 non-null   float64
 20  longitude           1653 non-null   float64
dtypes: float64(3), int64(5), object(13)
memory usage: 284.1+ KB

3. Descriptive Statistics

The second step in the analysis is to examine the basic statistics of the dataset, such as the count, mean, and median. This will give us an idea about the overall distribution of the trees in the city. For example, we can calculate the number of trees, the average diameter of the trees, and the range of most highest trees.

# The description of the data
street_trees.describe()

	Unnamed: 0	diameter	civic_number	tree_id	height_range_id	on_street_block	latitude	longitude
count	1653.000000	1653.000000	1653.000000	1653.000000	1653.000000	1653.000000	1653.000000	1653.000000
mean	14689.987296	6.004864	3106.491833	174940.944949	1.736237	3146.400484	49.245937	-123.101320
std	8778.890359	3.880239	2155.172608	59039.081620	0.887429	2183.920864	0.021302	0.048578
min	2.000000	1.000000	3.000000	616.000000	0.000000	0.000000	49.203531	-123.220360
25%	6907.000000	3.000000	1365.000000	150798.000000	1.000000	1400.000000	49.228664	-123.137163
50%	14579.000000	4.500000	2728.000000	184692.000000	2.000000	2700.000000	49.245210	-123.099031
75%	22253.000000	8.000000	4472.000000	222708.000000	2.000000	4500.000000	49.262586	-123.059325
max	29979.000000	31.000000	8989.000000	262098.000000	6.000000	8600.000000	49.292581	-123.023514

# The shape of the data frame
street_trees.shape

(1653, 21)

4. Visualization

Next, we can create visualizations to explore the data further. We can use bar charts to visualize the distribution of tree species, scatter plots to examine the relationship between tree diameter and height, and heat maps to see the concentration of trees in different areas of the city.

City of Vancouver

Vancouver, British Columbia is known for its lush greenery and abundant parks. However, one of the city's most significant green assets is often overlooked: its street trees. Street trees, also known as boulevard trees, are those that are planted on the public right-of-way between the sidewalk and the curb. These trees play a vital role in providing shade, reducing urban heat islands, and enhancing the overall aesthetic of the city.

Now, we'll dive into the street_trees dataset and explore the varieties of trees that line Vancouver's streets.

# Aggregate data and create a bar chart
top_species = (
    alt.Chart(street_trees)
    .transform_aggregate(
        count='count()',
        groupby=['species_name']
    )
    .transform_window(
        rank='rank(count)',
        sort=[alt.SortField('count', order='descending')]
    )
    .transform_filter(
        alt.datum.rank <= 10
    )
    .mark_bar()
    .encode(
        y=alt.Y('species_name:N', sort='-x'),
        x='count:Q',
        color=alt.Color('species_name:N', scale=alt.Scale(scheme='category10'), sort=['PLATANOIDES','RUBRUM','CERASIFERA','FREEMANI X','BETULUS','SYLVATICA','X YEDOENSIS','CALLERYANA','SERRULATA','AMERICANA']),
    )
    .properties(title="Top 10 tree species in the City of Vancouver")
)

# Show chart
top_species

The top ten tree species in Vancouver are: PLATANOIDES,RUBRUM,CERASIFERA,FREEMANI X,BETULUS,SYLVATICA,X YEDOENSIS,CALLERYANA,SERRULATA,AMERICANA. These tree species have been selected based on their abundance and prevalence in the city.

species_names = ['PLATANOIDES', 'RUBRUM', 'CERASIFERA', 'FREEMANI X', 'BETULUS', 'SYLVATICA', 'X YEDOENSIS', 'CALLERYANA', 'SERRULATA', 'AMERICANA']
top10_tree = street_trees.loc[street_trees['species_name'].isin(species_names)]

top10_tree.head()

	Unnamed: 0	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	...	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
1	12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	...	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
3	8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	...	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
10	1684	KINGSWAY	KINGSWAY	SYLVATICA	Kensington-Cedar Cottage	2009-11-06	3.0	MED	FAGUS	N	...	8	Y	228772	DAWYCK'S BEECH	1	1500	DAWYCKII	N	49.248839	-123.073073
15	5416	GOTHARD ST	CLARENDON ST	PLATANOIDES	Renfrew-Collingwood	1994-11-08	3.0	EVEN	ACER	Y	...	8	Y	156303	GLOBEHEAD NORWAY MAPLE	2	4700	GLOBOSUM	N	49.241778	-123.054438
19	17945	W 12TH AV	W 12TH AV	SERRULATA	Kitsilano	2008-03-13	9.0	ODD	PRUNUS	N	...	20	Y	106587	SHIROTAE(MT FUJI) CHERRY	1	2600	SHIROTAE	N	49.261319	-123.164948

5 rows × 21 columns

5. Correlation Analysis

With this data, we can explore the relationship between tree diameter and height.

To study the relationship between tree diameter and height, we can make scatter plots for each of these species. Scatter plots are a useful tool to visualize the relationship between two variables. In this case, the diameter of the tree is the independent variable, and the height of the tree is the dependent variable.

# Set up chart dimensions and mark
chart_width = 700
chart_height = 500
point_size = 40
chart_title = "The characteristics of the top 10 tree species"
chart_mark = alt.MarkDef(type='circle', size=point_size)

# Set up chart encoding
chart_encoding = alt.Chart(top10_tree, width=chart_width, height=chart_height).mark_circle(size=point_size).encode(
    alt.X('diameter', title='Diameter (inches)'),
    alt.Y('height_range_id', title='Height range (*10 feet)'),
    alt.Color('species_name', title="Species", scale=alt.Scale(scheme='category10'), sort=['PLATANOIDES','RUBRUM','CERASIFERA','FREEMANI X','BETULUS','SYLVATICA','X YEDOENSIS','CALLERYANA','SERRULATA','AMERICANA']),
    tooltip=['species_name']
).properties(title=chart_title)

# Set up dropdown menu for selecting tree species
types = sorted(top10_tree['species_name'].unique())
dropdown = alt.binding_select(name='Species', options=types)
select_types = alt.selection_single(fields=['species_name'], bind=dropdown)

# Update chart encoding to include opacity condition based on dropdown selection
chart_encoding_with_selection = chart_encoding.add_selection(select_types).encode(
    opacity=alt.condition(select_types, alt.value(0.7), alt.value(0.05))
)

# Render the chart
tree_point = chart_encoding_with_selection
tree_point

By examining the scatter plots, we can look for patterns or trends in the data.

Studying the relationship between tree diameter and height is important for urban forestry management. Knowing the relationship between these two variables can help urban foresters determine the appropriate pruning and maintenance schedules for different tree species.

Nature-Friendly Environment

Afterward, our interest lies in observing the tree distribution across every neighborhood in Vancouver.

# Create chart
trees_count_neighbourhood = alt.Chart(street_trees).mark_bar().encode(
    x=alt.X('neighbourhood_name', sort='-y', title='Neighbourhoods'),
    y=alt.Y('count()', title='Number of trees')
).properties(
    title="The number of trees for each neighborhood",
    width=700,
    height=200
)

# Add text labels to bars
trees_count_neighbourhood_text = trees_count_neighbourhood.mark_text(
    align='center', dy=-7
).encode(
    text=alt.Text('count(neighbourhood_name)')
)

# Display chart with text labels
trees_count_neighbourhood + trees_count_neighbourhood_text

It has been determined that Renfrew-Collingwood area has the highest number of trees in the city.

Renfrew-Collingwood is situated along the eastern boundary of the city, adjacent to Burnaby. Although primarily residential, the area offers convenient access to nature, including the beautiful Renfrew Ravine Park featuring a natural creek within the Still Creek watershed. The residents of Renfrew-Collingwood also have easy access to a wide range of services and amenities, particularly along the Collingwood stretch of Kingsway. (Renfrew-Collingwood | City of Vancouver).

6. Spatial Analysis

As the dataset includes location information for the trees, we can perform a spatial analysis to see the distribution of the trees across the city. This can also help identify areas that are lacking in tree coverage and where new plantings may be needed.

One of the simplest ways to visualize geospatial data is by creating a point map. The idea is to mark a point on the map for each area that corresponds to a street tree.

# Set the map location and GeoJSON file
vancouver_location = [49.246292, -123.116226]
geo_json = "https://raw.githubusercontent.com/blackmad/neighborhoods/master/vancouver.geojson"

# Create the map object
trees_map = folium.Map(
    location=vancouver_location,
    zoom_start=12,
    max_zoom=14,
    min_zoom=12,
    tiles="Stamenterrain"
)

# Create the choropleth layer for neighborhood tree counts
data_choropleth = street_trees['neighbourhood_name'].value_counts().reset_index().rename(columns={"index": "neighbourhood", "neighbourhood_name": "count"})
trees_map.choropleth(
    geo_data=geo_json,
    name="choropleth",
    data=data_choropleth,
    columns=['neighbourhood', 'count'],
    key_on="feature.properties.name",
    fill_color="YlGn",
    fill_opacity=0.5,
    line_opacity=0.4,
    legend_name="Number of Trees (Count)"
)

# Create markers for each tree
for _, row in street_trees.iterrows():
    icon_color = None
    
    # Set the icon color based on the tree species
    if row["species_name"] == "PLATANOIDES":
        icon_color = "#17becf"
    elif row["species_name"] == "RUBRUM":
        icon_color = "#ffbb78"
    elif row["species_name"] == "CERASIFERA":
        icon_color = "#c49c94"
    elif row["species_name"] == "FREEMANI   X":
        icon_color = "#9edad5"
    elif row["species_name"] in ("BETULUS", "SYLVATICA"):
        icon_color = "#d62728"
    elif row["species_name"] == "X YEDOENSIS":
        icon_color = "#bcbd22"
    elif row["species_name"] in ("CALLERYANA", "SERRULATA"):
        icon_color = "#ff9896"
    elif row["species_name"] == "AMERICANA":
        icon_color = "#ff7f0e"

    # Add a circle marker for the tree
    folium.CircleMarker(
        location=[row["latitude"], row["longitude"]],
        radius=1,
        popup=row['species_name'] + ' on ' + row['on_street'] + ' in ' + row['neighbourhood_name'],
        color=icon_color
    ).add_to(trees_map)

# Display the map
trees_map

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7. Conclusion

In conclusion, the top ten tree species in Vancouver are identified as PLATANOIDES, RUBRUM, CERASIFERA, FREEMANI X, BETULUS, SYLVATICA, X YEDOENSIS, CALLERYANA, SERRULATA, and AMERICANA, based on their abundance in the city. No significant correlation has been found between tree diameter and height. The Renfrew-Collingwood area has the highest number of trees.

The street_trees dataset provides valuable insights into the diversity of tree species in Vancouver and highlights the importance of maintaining and expanding the city's urban forest. Maintaining the health of Vancouver's street trees is crucial in mitigating the impacts of climate change and improving the quality of life for its residents. Continuous monitoring and maintenance will ensure that the benefits of street trees are enjoyed by future generations.

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"By far the greated and most admirable form of wisdom is that needed to plan and beautify cities and human communities."

_ Socrates

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8. References

• Ely, Martin (2009). Planning for Trees In Urban Environments. Link
• The University of British Columbia, Open Case Studies: How Street Trees Can Save Our Cities. Link
• Imaga 1: City of Vancouver. Link
• Renfrew-Collingwood | City of Vancouver. Link
• Imaga 2: City of Vancouver. Link