Street cherry trees in Vancouver | An exploratory data analysis

Table of contents (TOC)

• 1 Introduction
• 2 Data overview
• 3 Data wrangling
• 4 Exploratory data analysis
  • 4.1 Number of street cherry trees in Vancouver
  • 4.2 Most common and uncommon street cherry trees
  • 4.3 Street cherry trees in Vancouver's neighbourhoods
  • 4.4 Using maps to visualize street cherry tree locations
    • 4.4.1 Street cherry tree clusters in Vancouver
    • 4.4.2 Locations of uncommon street cherry trees
• 5 Conclusions

1 Introduction

There are thousands of cherry trees in Vancouver. The first cherry trees were donated by the mayors of Kobe and Yokohama in the early 1930s. They were meant to be planted at the Japanese cenotaph in Stanley Park, which honours Japanese Canadians who served in World War I.

This exploratory data analysis (EDA) focuses on street trees and aims at answering the following questions:

• How many street cherry trees are there in Vancouver?
  

• What are the most common and uncommon street cherry trees?
  

• Where are they located?

The EDA uses the street tree dataset. The City of Vancouver Open Data Portal maintains said dataset, which presents information on public trees. The inventory does not include park or private trees.

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2 Data overview

A copy of the dataset was downloaded on 8 April 2022 as a csv file containing the following columns:

• TREE_ID: numerical identification of tree
• CIVIC_NUMBER: street address of the site at which the tree is associated with
• STD_STREET: street name of the site at which the tree is associated with
• GENUS_NAME
• SPECIES_NAME
• CULTIVAR_NAME
• COMMON_NAME
• ASSIGNED: indicates whether the address is made up to associate the tree with a nearby lot
• ROOT_BARRIER
• PLANT_AREA: B = behind sidewalk, G = in tree grate, N = no sidewalk, C = cutout, a number indicates boulevard width in feet
• ON_STREET_BLOCK: the street block at which the tree is physically located on
• ON_STREET: the name of the street at which the tree is physically located on
• NEIGHBOURHOOD_NAME: city's defined local area in which the tree is located
• STREET_SIDE_NAME: the street side which the tree is physically located on (even, odd or median)
• HEIGHT_RANGE_ID: 0-10 for every 10 feet (e.g., 0 = 0-10 ft, 1 = 10-20 ft, 2 = 20-30 ft, and10 = 100+ ft)
• DIAMETER: diameter of tree at breast height in inches
• CURB: curb presence
• DATE_PLANTED: the date of planting in YYYYMMDD format
• GEOM: coordinates of the tree

# Import libraries

import pandas as pd
import numpy as np
import plotly.express as px
import plotly.io as pio
import folium
from folium.plugins import MarkerCluster

pio.renderers.default = 'plotly_mimetype+notebook'

# Load data downloaded from the City of Vancouver Open Data Portal into the trees DataFrame

trees = pd.read_csv('./street-trees.csv', sep=';')

# Summary of the trees DataFrame

trees.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 151420 entries, 0 to 151419
Data columns (total 19 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   TREE_ID             151420 non-null  int64  
 1   CIVIC_NUMBER        151420 non-null  int64  
 2   STD_STREET          151420 non-null  object 
 3   GENUS_NAME          151420 non-null  object 
 4   SPECIES_NAME        151420 non-null  object 
 5   CULTIVAR_NAME       80474 non-null   object 
 6   COMMON_NAME         151420 non-null  object 
 7   ASSIGNED            151420 non-null  object 
 8   ROOT_BARRIER        151420 non-null  object 
 9   PLANT_AREA          149896 non-null  object 
 10  ON_STREET_BLOCK     151420 non-null  int64  
 11  ON_STREET           151420 non-null  object 
 12  NEIGHBOURHOOD_NAME  151420 non-null  object 
 13  STREET_SIDE_NAME    151420 non-null  object 
 14  HEIGHT_RANGE_ID     151420 non-null  int64  
 15  DIAMETER            151420 non-null  float64
 16  CURB                151420 non-null  object 
 17  DATE_PLANTED        68486 non-null   object 
 18  Geom                129891 non-null  object 
dtypes: float64(1), int64(4), object(14)
memory usage: 21.9+ MB

The trees DataFrame has 151,420 rows and 19 columns. Geom is not null for 129,891 rows (85.8%), which means coordinates are not available for all trees.

# Number of unique trees

trees['TREE_ID'].unique().shape[0]

151420

There are no duplicated tress in the DataFrame, i.e., the dataset contains information on 151,420 street trees.

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3 Data wrangling

# Remove columns that will not be used in the analysis

columns_to_be_removed = ['CIVIC_NUMBER', 'STD_STREET', 'GENUS_NAME', 'SPECIES_NAME', 'CULTIVAR_NAME',
                         'ASSIGNED', 'ROOT_BARRIER', 'PLANT_AREA', 'ON_STREET_BLOCK', 'ON_STREET', 
                         'STREET_SIDE_NAME', 'HEIGHT_RANGE_ID', 'DIAMETER', 'CURB', 'DATE_PLANTED']

trees = trees.drop(columns_to_be_removed, axis = 1)

trees.head(5)

	TREE_ID	COMMON_NAME	NEIGHBOURHOOD_NAME	Geom
0	22	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.076828, 49.269726], "typ...
1	24	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.07665, 49.269724], "type...
2	26	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.076336, 49.269722], "typ...
3	31	PYRAMIDAL EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.075913, 49.269586], "typ...
4	35	PYRAMIDAL EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.075064, 49.269579], "typ...

# Rename columns

trees.rename(columns = {'TREE_ID': 'tree ID', 'COMMON_NAME': 'common name', 
                        'NEIGHBOURHOOD_NAME': 'neighbourhood', 
                        'Geom': 'geom'}, inplace = True)
trees

	tree ID	common name	neighbourhood	geom
0	22	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.076828, 49.269726], "typ...
1	24	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.07665, 49.269724], "type...
2	26	EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.076336, 49.269722], "typ...
3	31	PYRAMIDAL EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.075913, 49.269586], "typ...
4	35	PYRAMIDAL EUROPEAN HORNBEAM	GRANDVIEW-WOODLAND	{"coordinates": [-123.075064, 49.269579], "typ...
...	...	...	...	...
151415	275572	PISSARD PLUM	KENSINGTON-CEDAR COTTAGE	{"coordinates": [-123.085267, 49.240811], "typ...
151416	275575	TATARIAN MAPLE	KENSINGTON-CEDAR COTTAGE	{"coordinates": [-123.085052, 49.240464], "typ...
151417	275577	TATARIAN MAPLE	KENSINGTON-CEDAR COTTAGE	{"coordinates": [-123.084977, 49.240653], "typ...
151418	275579	GINKGO OR MAIDENHAIR TREE	KENSINGTON-CEDAR COTTAGE	{"coordinates": [-123.084558, 49.240757], "typ...
151419	275581	WEEPING WILLOW	KENSINGTON-CEDAR COTTAGE	{"coordinates": [-123.084299, 49.240768], "typ...

151420 rows × 4 columns

# Capitalize only the first letter of each word under common name and neighbourhood columns

columns_to_be_modified = ['common name', 'neighbourhood']

for column in columns_to_be_modified:
    trees[column] = trees[column].str.title()

trees.head(5)

	tree ID	common name	neighbourhood	geom
0	22	European Hornbeam	Grandview-Woodland	{"coordinates": [-123.076828, 49.269726], "typ...
1	24	European Hornbeam	Grandview-Woodland	{"coordinates": [-123.07665, 49.269724], "type...
2	26	European Hornbeam	Grandview-Woodland	{"coordinates": [-123.076336, 49.269722], "typ...
3	31	Pyramidal European Hornbeam	Grandview-Woodland	{"coordinates": [-123.075913, 49.269586], "typ...
4	35	Pyramidal European Hornbeam	Grandview-Woodland	{"coordinates": [-123.075064, 49.269579], "typ...

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4 Exploratory data analysis

# Create a DataFrame containing only trees whose common names contain the string cherry

cherry_trees = trees[trees['common name'].str.contains('cherry', case = False)]

cherry_trees.shape[0]

18200

This approach may leave out cherry trees whose common names do not contain the string cherry.

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4.1 Number of street cherry trees in Vancouver

# Cherry tree unique names

cherry_trees['common name'].unique()

array(['Kwanzan Flowering Cherry', 'Shirotae(Mt Fuji) Cherry',
       'Sargent Flowering Cherry', 'Rancho Sargent Cherry',
       'Shirofugen Cherry', 'Ukon Japanese Cherry',
       'Akebono Flowering Cherry', 'Mazzard Cherry', 'Wild Cherry',
       'Japanese Flowering Cherry', 'Seiboldi Cherry',
       'Schubert Chokecherry', 'Accolade Cherry', 'Whitcomb Cherry',
       'Pink Perfection Cherry', 'Amanogawa Japanese Cherry',
       'Paperbark Cherry', 'Cherry, Plum Or Peach Species',
       'Miyako Cherry', 'Weeping Japanese Cherry',
       'Columnar Sargent Cherry', 'Yoshino Cherry', 'Black Cherry',
       'European Birdcherry', 'Autumn Higan Cherry', 'Higan Cherry',
       'Hillier Spire Cherry', 'Weeping Higan Cherry',
       'Commom Chokecherry', 'Pin Cherry', 'Bailey Select Chokecherry',
       'Snow Goose Cherry', 'Cornelian Cherry', 'Manchurian Cherry',
       'Cheals Weeping Cherry', 'Great White Cherry', 'Sweetheart Cherry',
       'Mikuruma-Gaeshi Cherry', 'Shogetsu Japanese Cherry',
       'Bitter Cherry'], dtype=object)

Cherry, Plum Or Peach Species may not refer to cherry trees. Therefore, all the rows whose common name column is Cherry, Plum Or Peach Species will be removed.

cherry_trees = cherry_trees[~cherry_trees['common name'].str.contains('Cherry, Plum Or Peach Species')]

cherry_trees.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 17982 entries, 13 to 151311
Data columns (total 4 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   tree ID        17982 non-null  int64 
 1   common name    17982 non-null  object
 2   neighbourhood  17982 non-null  object
 3   geom           15992 non-null  object
dtypes: int64(1), object(3)
memory usage: 702.4+ KB

There are 17,982 street cherry trees in the dataset, i.e., these trees account for approximately 11.9% of the records. Coordinates are available for about 88.9% of the cherry trees.

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4.2 Most common and uncommon street cherry trees

# Types of cherry trees

cherry_trees['common name'].nunique()

39

There are 39 types of cherry trees in Vancouver.

# Cherry trees per type

type_count = cherry_trees['common name'].value_counts(ascending = False).rename_axis('name'
                                                                           ).reset_index(name = 'count')

type_percentage = cherry_trees['common name'].value_counts(ascending = False, normalize = True).mul(
    100).round(2).rename_axis('name').reset_index(name = 'trees (%)')

types_of_cherry_trees = type_count.merge(type_percentage, on = 'name', how = 'inner')

types_of_cherry_trees

	name	count	trees (%)
0	Kwanzan Flowering Cherry	10375	57.70
1	Akebono Flowering Cherry	2418	13.45
2	Ukon Japanese Cherry	770	4.28
3	Japanese Flowering Cherry	634	3.53
4	Mazzard Cherry	568	3.16
5	Pink Perfection Cherry	514	2.86
6	Rancho Sargent Cherry	500	2.78
7	Shirofugen Cherry	390	2.17
8	Shirotae(Mt Fuji) Cherry	224	1.25
9	Schubert Chokecherry	213	1.18
10	Sargent Flowering Cherry	173	0.96
11	Seiboldi Cherry	160	0.89
12	Higan Cherry	105	0.58
13	Yoshino Cherry	100	0.56
14	Autumn Higan Cherry	82	0.46
15	Wild Cherry	74	0.41
16	Whitcomb Cherry	66	0.37
17	Amanogawa Japanese Cherry	65	0.36
18	Bailey Select Chokecherry	65	0.36
19	Commom Chokecherry	64	0.36
20	Weeping Japanese Cherry	60	0.33
21	Accolade Cherry	55	0.31
22	Pin Cherry	41	0.23
23	European Birdcherry	39	0.22
24	Hillier Spire Cherry	37	0.21
25	Black Cherry	37	0.21
26	Snow Goose Cherry	33	0.18
27	Columnar Sargent Cherry	28	0.16
28	Cornelian Cherry	26	0.14
29	Paperbark Cherry	22	0.12
30	Manchurian Cherry	15	0.08
31	Great White Cherry	10	0.06
32	Cheals Weeping Cherry	5	0.03
33	Sweetheart Cherry	5	0.03
34	Weeping Higan Cherry	3	0.02
35	Mikuruma-Gaeshi Cherry	2	0.01
36	Miyako Cherry	2	0.01
37	Bitter Cherry	1	0.01
38	Shogetsu Japanese Cherry	1	0.01

# Street cherry trees in Vancouver

fig = px.bar(types_of_cherry_trees.loc[::-1], x = 'trees (%)', y = 'name', 
             title = """Kwanzan Flowering and Akebono Flowering cherries account for over 70% of the street cherry trees
             <br><sup>Street cherry trees in Vancouver (%)<sup>""", 
             orientation = 'h', height = 900)
fig.update_traces(marker_color = '#1f9e89')
fig.update_layout({
  'plot_bgcolor': '#ffffff',
  'paper_bgcolor': '#ffffff', 
  
})
fig.update_xaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()

Kwanzan Flowering, Akebono Flowering, Ukon Japanese, Japanese Flowering, Mazzard and Pink Perfection cherries are the most common street cherry trees in Vancouver. Kwanzan Flowering and Akebono Flowering cherries account for approximately 57.7% and 13.5% of the cherry trees, respectively.

Weeping Higan, Miyako, Mikuruma-Gaeshi, Bitter and Shogetsu Japanese cherries are the most uncommon cherry trees.

Back to TOC

4.3 Street cherry trees in Vancouver's neighbourhoods

# Street cherry trees per neighbourhood

neighbourhood_count = cherry_trees['neighbourhood'].value_counts(ascending = False).rename_axis('neighbourhood'
                                                                           ).reset_index(name = 'trees')

neighbourhood_percentage = cherry_trees['neighbourhood'].value_counts(ascending = False, normalize = True).mul(
    100).round(2).rename_axis('neighbourhood').reset_index(name = 'percentage')

cherry_trees_per_neighbourhood = neighbourhood_count.merge(neighbourhood_percentage, 
                                                           on = 'neighbourhood', how = 'inner')

cherry_trees_per_neighbourhood

	neighbourhood	trees	percentage
0	Renfrew-Collingwood	1489	8.28
1	Dunbar-Southlands	1453	8.08
2	Kensington-Cedar Cottage	1300	7.23
3	Sunset	1146	6.37
4	Marpole	1093	6.08
5	Mount Pleasant	1090	6.06
6	Victoria-Fraserview	1014	5.64
7	Riley Park	1009	5.61
8	Grandview-Woodland	923	5.13
9	Kerrisdale	905	5.03
10	Oakridge	829	4.61
11	Hastings-Sunrise	829	4.61
12	Kitsilano	741	4.12
13	Arbutus-Ridge	593	3.30
14	Shaughnessy	586	3.26
15	Killarney	584	3.25
16	West Point Grey	561	3.12
17	Fairview	495	2.75
18	South Cambie	472	2.62
19	West End	449	2.50
20	Strathcona	251	1.40
21	Downtown	170	0.95

# Street cherry trees in Vancouver's neighbourhoods

fig = px.bar(cherry_trees_per_neighbourhood.loc[::-1], x = 'trees', y = 'neighbourhood',  
      title = """There are 1,400+ street cherry trees in two neighbourhoods: Renfrew-Collingwood and Dunbar-Southlands
      <br><sup>Street cherry trees per neighbourhood<sup>""", 
      orientation = 'h', height = 750)
fig.update_traces(marker_color = '#1f9e89')
fig.update_layout({
  'plot_bgcolor': '#ffffff',
  'paper_bgcolor': '#ffffff', 
  
})
fig.update_xaxes(showgrid = True, gridwidth = 1, gridcolor = '#e0e0e0')

fig.show()

There are at least one thousand street cherry trees in Renfrew-Collingwood, Dunbar-Southlands, Kensington-Cedar Cottage, Sunset, Marpole, Mount Pleasant, Victoria-Fraserview and Riley Park neighbourhoods.

The most sparsely-foliated neighbourhoods with regard to street cherry trees are Fairview, South Cambie, West End, Strathcona and Downtown.

name_neighbourhood_crosstab = pd.crosstab(cherry_trees['common name'], 
                                          cherry_trees['neighbourhood'], normalize = 'columns')

# Distribution of cherry tree types across Vancouver's neighbourhoods

fig = px.imshow(name_neighbourhood_crosstab * 100, 
      labels = dict(color = 'trees in neighbourhood (%)'),
      x = name_neighbourhood_crosstab.columns, 
      y = name_neighbourhood_crosstab.index,
      title = """The Kwanzan Flowering cherry is the prevalent street cherry tree in all neighbourhoods
      <br><sup>Distribution of different cherry tree types across Vancouver's neighbourhoods<sup>""",
      height = 1000,
      aspect = 'auto',
      color_continuous_scale = 'viridis')

fig.show()

The Akebono Flowering cherry is the second most common street cherry tree in all neighbourhoods with the exception of Downtown, Fairview, Grandview-Woodland, Killarney, Mount Pleasant, Oakridge, Strathcona and West End. In these neighbourhoods, the second most prevalent street cherry trees are:

• Downtown, Fairview, Grandview-Woodland: Rancho Sargent cherry
• Killarney: Mazzard cherry
• Mount Pleasant, Strathcona and West End: Ukon Japanese cherry
• Oakridge: Pink Perfection cherry

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4.4 Using maps to visualize street cherry tree locations

Cherry tree coordinates are available for approximately 88.9% of the records. These coordinates can be used to build maps that provide different vantage points to explore the dataset.

Maps will be generated using Folium, a Python library for geospatial data visualization.

# Create DataFrame containing only rows for which coordinates are available

cherry_trees_with_coordinates = cherry_trees.dropna(subset = ['geom']).copy(deep = True)

# Extract coordinates from the geom column and store them into lat and lon columns

cherry_trees_with_coordinates[['lon', 'lat']] = cherry_trees_with_coordinates['geom'].apply(
    lambda st: st[st.find('[') + 1:st.find(']')]).str.split(',', expand = True)

cherry_trees_with_coordinates

	tree ID	common name	neighbourhood	geom	lon	lat
13	59	Kwanzan Flowering Cherry	Grandview-Woodland	{"coordinates": [-123.071482, 49.270015], "typ...	-123.071482	49.270015
14	60	Kwanzan Flowering Cherry	Grandview-Woodland	{"coordinates": [-123.071486, 49.269908], "typ...	-123.071486	49.269908
32	134	Kwanzan Flowering Cherry	Grandview-Woodland	{"coordinates": [-123.06389, 49.269711], "type...	-123.06389	49.269711
39	187	Shirotae(Mt Fuji) Cherry	Grandview-Woodland	{"coordinates": [-123.05987, 49.269887], "type...	-123.05987	49.269887
40	188	Shirotae(Mt Fuji) Cherry	Grandview-Woodland	{"coordinates": [-123.059869, 49.269773], "typ...	-123.059869	49.269773
...	...	...	...	...	...	...
151307	274866	Kwanzan Flowering Cherry	Dunbar-Southlands	{"coordinates": [-123.175003, 49.245205], "typ...	-123.175003	49.245205
151308	274867	Japanese Flowering Cherry	Dunbar-Southlands	{"coordinates": [-123.175103, 49.245188], "typ...	-123.175103	49.245188
151309	274875	Japanese Flowering Cherry	Dunbar-Southlands	{"coordinates": [-123.175086, 49.245012], "typ...	-123.175086	49.245012
151310	274877	Japanese Flowering Cherry	Dunbar-Southlands	{"coordinates": [-123.175221, 49.244922], "typ...	-123.175221	49.244922
151311	274885	Kwanzan Flowering Cherry	Kerrisdale	{"coordinates": [-123.16681, 49.23383], "type"...	-123.16681	49.23383

15992 rows × 6 columns

# Convert lon and lat columns of the cherry_trees_with_coordinates DataFrame to numeric 

columns = ['lon', 'lat']

cherry_trees_with_coordinates[columns] = cherry_trees_with_coordinates[columns].apply(
    pd.to_numeric, errors = 'raise', axis = 1)

cherry_trees_with_coordinates.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 15992 entries, 13 to 151311
Data columns (total 6 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   tree ID        15992 non-null  int64  
 1   common name    15992 non-null  object 
 2   neighbourhood  15992 non-null  object 
 3   geom           15992 non-null  object 
 4   lon            15992 non-null  float64
 5   lat            15992 non-null  float64
dtypes: float64(2), int64(1), object(3)
memory usage: 874.6+ KB

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4.4.1 Street cherry tree clusters in Vancouver

Displaying one marker for each of the 15,992 cherry trees would clutter the Vancouver map and potentially hinder data exploration.

Clustered point maps combine markers that are close to each other into clusters. The number on a cluster shows how many markers it has. As the user zooms in, the number of cluster decreases and individual markers begin to appear.

# Create list of coordinates that will be used in the clustered point map

cluster_data = [[row['lat'], row['lon']] for index, row in cherry_trees_with_coordinates.iterrows()]

# Create clustered point map using cherry tree coordinates

cherry_tree_map = folium.Map([49.24, -123.11], 
                                 tiles = 'OpenStreetMap', zoom_start = 12, control_scale = True)
MarkerCluster(cluster_data).add_to(cherry_tree_map)

cherry_tree_map

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4.4.2 Locations of uncommon street cherry trees

# Uncommon cherry trees (5 trees or fewer)

uncommon = cherry_trees_with_coordinates['common name'].value_counts().loc[lambda x : x <= 5].reset_index()

uncommon.columns = ['name', 'count']

uncommon

	name	count
0	Sweetheart Cherry	5
1	Mikuruma-Gaeshi Cherry	2
2	Weeping Higan Cherry	2
3	Miyako Cherry	2
4	Shogetsu Japanese Cherry	1

As there are only 12 trees, it is possible to represent them as markers on a map without compromising the user experience. Hovering over each marker causes a tooltip contaning the name of the tree to appear.

# Create a subset of the cherry_trees_with_coordinates DataFrame containing only the uncommon cherry trees

uncommon_cherry_trees_list = uncommon['name'].tolist()

uncommon_cherry_trees = cherry_trees_with_coordinates.loc[cherry_trees_with_coordinates['common name'].isin(
    uncommon_cherry_trees_list)]

uncommon_cherry_trees

	tree ID	common name	neighbourhood	geom	lon	lat
2683	29153	Miyako Cherry	Kensington-Cedar Cottage	{"coordinates": [-123.077354, 49.244628], "typ...	-123.077354	49.244628
6589	36276	Weeping Higan Cherry	Shaughnessy	{"coordinates": [-123.130806, 49.236875], "typ...	-123.130806	49.236875
59220	245324	Sweetheart Cherry	Grandview-Woodland	{"coordinates": [-123.064839, 49.276675], "typ...	-123.064839	49.276675
59252	245452	Sweetheart Cherry	Grandview-Woodland	{"coordinates": [-123.064836, 49.276578], "typ...	-123.064836	49.276578
72963	58832	Mikuruma-Gaeshi Cherry	West End	{"coordinates": [-123.135502, 49.288665], "typ...	-123.135502	49.288665
78604	28078	Sweetheart Cherry	Grandview-Woodland	{"coordinates": [-123.064953, 49.276578], "typ...	-123.064953	49.276578
80759	31142	Sweetheart Cherry	Grandview-Woodland	{"coordinates": [-123.064571, 49.276575], "typ...	-123.064571	49.276575
118000	58820	Mikuruma-Gaeshi Cherry	West End	{"coordinates": [-123.134916, 49.288292], "typ...	-123.134916	49.288292
122295	114383	Miyako Cherry	Arbutus-Ridge	{"coordinates": [-123.165891, 49.236758], "typ...	-123.165891	49.236758
129138	142824	Shogetsu Japanese Cherry	Dunbar-Southlands	{"coordinates": [-123.19164, 49.25012], "type"...	-123.191640	49.250120
129237	143613	Weeping Higan Cherry	Oakridge	{"coordinates": [-123.133285, 49.224239], "typ...	-123.133285	49.224239
146337	245451	Sweetheart Cherry	Grandview-Woodland	{"coordinates": [-123.064732, 49.276672], "typ...	-123.064732	49.276672

# Create map showing locations of the uncommon cherry trees

uncommon_cherry_tree_map = folium.Map([49.24, -123.11], tiles = 'OpenStreetMap', 
                                      zoom_start = 12, control_scale = True)

for i in range(0, len(uncommon_cherry_trees)):
   folium.Marker(
      location = [uncommon_cherry_trees.iloc[i]['lat'], uncommon_cherry_trees.iloc[i]['lon']],
      icon =  folium.Icon(color = 'green', icon = 'tree', prefix = 'fa'),
      tooltip = uncommon_cherry_trees.iloc[i]['common name'],
   ).add_to(uncommon_cherry_tree_map)

uncommon_cherry_tree_map

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5 Conclusions

• There are 17,982 street cherry trees in Vancouver. These trees correspond to 11.9% of the street tree dataset.
  
• Kwanzan Flowering cherries make up more than 57% of the city's cherry trees. Kwanzan Flowering and Akebono Flowering cherries account for over 70% of the street cherry trees.
  
• Sweetheart, Weeping Higan, Mikuruma-Gaeshi, Miyako and Shogetsu Japanese cherries are the most uncommon cherry trees.
  
• The Kwanzan Flowering cherry is the prevalent cherry tree in all neighbourhoods.
  
• Renfrew-Collingwood, Dunbar-Southlands, Kensington-Cedar Cottage, Sunset and Marpole are the neighbourhoods with the highest amounts of street cherry trees.

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