data-freshness

name	data-freshness
description	Patterns for checking when Montréal open data was last updated, detecting stale data, and understanding update schedules across different datasets. / Stratégies pour vérifier la fraîcheur des données, détecter les données périmées et comprendre les calendriers de mise à jour.
triggers	["fresh, stale, updated, last update, current, outdated, when","frais, périmé, mise à jour, dernière, actuel, quand"]

Data Freshness / Fraîcheur des données

Why This Matters / Pourquoi c'est important

Not all datasets update at the same rate. Some are real-time (BIXI station status, every minute). Others are annual snapshots. An agent must know the difference to set user expectations correctly.

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Update Frequency by Dataset / Fréquence de mise à jour

Real-Time (minutes)

Dataset	Update Rate	Source
BIXI station status	~1 min	GBFS feed
STM bus positions	Multiple/min	GTFS-RT (API key required)
Air quality (RSQA)	~50 min lag	DataStore

Daily to Weekly

Dataset	Update Rate	Source
311 requests	Daily	DataStore
Snow removal status	During operations	Planif-Neige SOAP
Skating rink conditions	During season	JSON feed
Tree inventory	Weekly (DHP history)	DataStore

Monthly

Dataset	Update Rate	Source
Building permits	Monthly	DataStore
Criminal acts	Monthly	DataStore
Fire interventions	Monthly	DataStore
Road collisions	Monthly	DataStore

Annual or Periodic

Dataset	Update Rate	Source
Budget	Annual (Jan)	DataStore
Property assessment	Every 3 years	DataStore
Tree inventory (main)	Monthly field updates, periodic full refresh	DataStore
Building footprints	Periodic	GeoJSON download
BIXI trip history	Annual	CSV download

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How to Check Freshness / Comment vérifier la fraîcheur

Method 1: Dataset Metadata

# Check when a dataset was last modified
curl -s 'https://donnees.montreal.ca/api/3/action/package_show?id=DATASET_SLUG' | \
  python3 -c "
import json, sys
d = json.load(sys.stdin)['result']
print(f'Title: {d[\"title\"]}')
print(f'Metadata modified: {d[\"metadata_modified\"]}')
for r in d['resources']:
    print(f'  Resource: {r.get(\"name\",\"?\")[:50]}')
    print(f'    Last modified: {r.get(\"last_modified\", \"unknown\")}')
    print(f'    Created: {r.get(\"created\", \"unknown\")}')
"

Method 2: Data Timestamps

Many datasets have date fields. Query the most recent record:

-- Most recent permit
SELECT MAX("DATE_EMISSION") as latest FROM "RESOURCE_UUID"

-- Most recent 311 request
SELECT MAX("DATE_CREATION") as latest FROM "RESOURCE_UUID"

-- Most recent tree survey
SELECT MAX("Date_Releve") as latest FROM "64e28fe6-ef37-437a-972d-d1d3f1f7d891"

Method 3: BIXI (GBFS)

The GBFS status feed includes a last_updated Unix timestamp:

import requests
status = requests.get('https://gbfs.velobixi.com/gbfs/en/station_status.json').json()
print(f"Last updated: {status['last_updated']}")  # Unix epoch

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Staleness Thresholds / Seuils de péremption

Use these as rules of thumb when presenting data to users:

Dataset Type	"Fresh" if updated within	Warning after
Real-time feeds	5 minutes	30 minutes
Daily datasets (311, snow)	48 hours	1 week
Monthly datasets (permits, crime)	45 days	90 days
Annual datasets (budget)	13 months	18 months
Periodic (assessment, footprints)	3 years	5 years

Agent behavior when data is stale:

1. Still fresh: Present data normally.
2. Approaching stale: Present data with note: "This data was last updated on [date]."
3. Stale: Present data with warning: "⚠️ This data may be outdated (last updated [date]). Check the portal for updates."

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Seasonal Data / Données saisonnières

Some datasets are only meaningful during certain periods:

Dataset	Active Season	Off-Season Behavior
BIXI	April–November	Stations show 0 bikes (normal, not broken)
Skating rinks	December–March	No condition updates
Ski conditions	December–March	No condition updates
Snow removal	November–April	No operations data
Pool schedules	June–September	May show off-season programming

Agent should know the current month and adjust expectations accordingly. If a user asks about BIXI in January, explain it's seasonal rather than reporting "no bikes available."

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The catalog-stats.json Shortcut

After running scripts/catalog-refresh.py, the file reference/catalog-stats.json contains a timestamp of the last catalog pull. Agents can check this before making API calls:

import json
from datetime import datetime, timezone
stats = json.load(open('reference/catalog-stats.json'))
age_hours = (datetime.now(timezone.utc) - datetime.fromisoformat(stats['timestamp'])).total_seconds() / 3600
if age_hours > 168:  # > 1 week
    print("Catalog cache is stale. Run catalog-refresh.py.")

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Provenance / Provenance

Field	Value
Publisher	N/A — agent-side freshness detection patterns
Applies to	All Montréal open data sources
Last verified	March 2026

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Related Skills / Compétences connexes

• error-recovery — Handle stale endpoints that return errors
• query-dataset — Timestamp field patterns
• discover-datasets — Metadata freshness checks