Victoria data projectIs there really a 76 m cherry tree in Victoria? A project on the city’s tree file
The City of Victoria publishes its parks department tree inventory as open data: species, trunk diameter and height for each tree it maintains. Our students write rules that catch impossible or doubtful values before they average anything. The counts are ours.
Our checks on the City of Victoria Tree Species file, downloaded 22 October 2026 (34,965 rows). Height in metres and trunk diameter in centimetres, per the dataset description; the thresholds are ours| Check (our rule) | Rows flagged | Why it deserves a second look |
|---|
| Height exactly 0 | 68 | Probably not measured, recorded as 0 |
| Height below 0 | 24 | A height cannot be negative; the lowest is -4 m |
| Trunk diameter 0 | 21 | A standing tree has a trunk |
| Name contains “cherry” and height over 20 m | 5 | Japanese kwanzan cherries in the file have a median height of 6 m |
| Tallest row in the file | 1 | A redwood at 112 m; the other nine redwoods have a median of 17 m and the next tallest is 30 m |
This script runs every check and prints the five commonest species and the four tallest rows. It needs Python 3 and the downloaded CSV, nothing else:import csv
from collections import Counter
species = Counter()
flags = Counter()
tallest = []
with open("tree-species.csv", encoding="utf-8-sig") as f:
for row in csv.DictReader(f):
name = row["CommonName"]
height = float(row["Height"])
dbh = float(row["DiameterAtBreastHeight"])
species[name] += 1
if height < 0:
flags["negative height"] += 1
if height == 0:
flags["height exactly 0"] += 1
if "cherry" in name.lower() and height > 20:
flags["cherry over 20 m"] += 1
if dbh == 0:
flags["trunk diameter 0"] += 1
tallest.append((height, name, dbh))
print("rows:", sum(species.values()), "names:", len(species))
for name, n in species.most_common(5):
print(name, n)
for problem, n in flags.most_common():
print("FLAG", problem, n)
for height, name, dbh in sorted(tallest, reverse=True)[:4]:
print(height, "m", name, "trunk", dbh, "cm")
1. Read the units
The city’s dataset description says trunk diameter at breast height is in centimetres and canopy height and width are in metres. Without that sentence, nobody could say whether 76 is suspicious.
2. Compare like with like
A range check catches negatives. A per-species check catches more: the 76 m and 56 m kwanzan cherries sit beside 771 kwanzan cherry rows with a median of 6 m, and above every Douglas fir in the file, whose tallest is 36 m.
3. Know the rule’s limits
Matching the word “cherry” also catches cherry plum, a different tree, and misses a cherry spelled another way. Students see their own rule catch the wrong things, and fix it.
Flag, do not delete
The right response to a 112 m redwood is a note, not a silent delete. It may be a typing slip, perhaps a misplaced digit, or the tree may be recorded in the wrong field; the file cannot tell us which. A student whose rules raise 118 flags on 100 different rows, who explains each rule and leaves the decision visible has done honest data work.
Most of the file is ordinary and lovely to explore. Garry oak is the commonest tree, with 5,200 rows, 3,974 of them park trees, followed by cherry plum (1,814) and Douglas fir (1,300). A good follow-up maps the Garry oaks by neighbourhood with the Area column. Contains information licensed under the Open Government Licence - City of Victoria (licence, dataset page). The City has not endorsed us or this page.
