Whitby data projectHow big is a typical Whitby park? An efficient program on the Town’s parks file
The Town of Whitby publishes a points file of its parks, with a class for each (LOCAL, PARKETTE, DISTRICT and so on) and an area in hectares. Oshawa publishes open data too, but its licence document would not load when we tried on 22 October 2026, so this project uses Whitby data only. Every figure below is our own calculation from the Whitby download of that day.
Whitby parks by CLASS, our calculation from the Town of Whitby Parks file (area column AREA1_HECT, which the metadata defines as area of park in hectares; one repeated ID counted once)| Class in the file | Parks | Mean area (ha) | Median area (ha) |
|---|
| LOCAL | 55 | 1.78 | 1.65 |
| PARKETTE | 33 | 0.39 | 0.39 |
| DISTRICT | 21 | 5.65 | 4.70 |
| WATERFRONT (TOWN) | 11 | 6.20 | 3.52 |
| TOWN PARK | 3 | 10.95 | 2.14 |
| OFFLEASH | 2 | 1.19 | 1.19 |
| FACILITY | 1 | 9.28 | 9.28 |
| LEASED TOWN LANDS | 1 | 14.39 | 14.39 |
The download has 128 rows but 127 parks: one ID appears twice with identical details, and another starts with a stray space, so the program strips spaces before comparing. 48 parks are under one hectare. The efficient part is the shape of the loop. A beginner might loop through the whole file once for each of the eight classes, eight passes. This program makes one pass, dropping each park into a list for its class as it goes, which is the Grade 6 idea of fewer wasted steps. A 12-year-old in our class writes:import csv, statistics
sizes = {}
seen = set()
with open("whitby-parks.csv", encoding="utf-8-sig") as f:
for park in csv.DictReader(f):
pid = park["FACILITYID"].strip()
if pid in seen:
continue
seen.add(pid)
sizes.setdefault(park["CLASS"], []).append(float(park["AREA1_HECT"]))
for kind, ha in sizes.items():
print(kind, len(ha), round(statistics.mean(ha), 2), round(statistics.median(ha), 2))
A set for repeats
Checking whether an ID is in a set takes about the same time however many IDs it holds. Checking a long list means walking along it. For 128 rows either works; for a million, only one does.
One mean that misleads
TOWN PARK has three parks: about 0.06, 2.14 and 30.66 hectares. Their mean is 10.95, larger than two of the three. The median, 2.14, describes the middle park far better.
Typical is a choice
For PARKETTE the mean and median are both 0.39, because the sizes are even. Children learn to check both, and to say which one they used and why.
The file and its description disagree
The Town’s description lists five classes as included and says FACILITY is not included. The file holds a FACILITY row anyway, plus OFFLEASH and LEASED TOWN LANDS rows, and spells waterfront as WATERFRONT (TOWN). The dataset page says it was updated in December 2025, while the attached metadata says it was last updated in October 2024. Neither is wrong on purpose; they were written at different times. We teach children to report what the file actually contains and to note the mismatch.
There is a friendlier question in the AMENITIES column. Splitting it on commas, our count found Playspace listed at 90 of the 127 parks and Splashpad at 18, while 25 parks list no amenity at all. Park names are in the file, but several honour people, so in class we work with IDs and classes only. Contains information licensed under the Open Government Licence - The Corporation of the Town of Whitby. The Town does not endorse this page or our classes.
