| name | download-resource |
| description | Download raw files (CSV, GeoJSON, Shapefile, PDF, GBFS) from Montréal's
open data portal. Handles non-DataStore resources that require direct download.
/ Télécharger des fichiers bruts (CSV, GeoJSON, Shapefile, PDF, GBFS) du
portail de données ouvertes de Montréal.
|
| triggers | ["download, file, GeoJSON, shapefile, CSV, export, raw data","télécharger, fichier, exporter, données brutes"] |
Download Resources / Télécharger des ressources
When to Download vs Query / Quand télécharger vs interroger
| Scenario | Use DataStore API | Download File |
|---|
| Tabular data with filters | ✓ | |
| SQL aggregation/counting | ✓ | |
| GeoJSON for mapping | | ✓ |
| Shapefile for GIS | | ✓ |
| PDF documentation | | ✓ |
| GBFS (BIXI) | | ✓ (live API) |
| Full dataset (all rows) | Sometimes* | ✓ |
| Dataset > 32,000 rows | Paginate | ✓ |
*DataStore has a 32,000 row limit per query. For large datasets, downloading the full CSV is often simpler.
Step 1: Find the Download URL
curl -s 'https://donnees.montreal.ca/api/3/action/package_show?id=DATASET_SLUG' | \
python3 -c "
import json, sys
for r in json.load(sys.stdin)['result']['resources']:
ds = '✓ DS' if r.get('datastore_active') else '✗'
sz = r.get('size', 0)
sz_mb = f'{sz/1048576:.1f}MB' if sz else '?'
print(f'{r.get(\"format\",\"?\")} | {ds} | {sz_mb} | {r[\"url\"][:100]}')
"
Step 2: Download
curl -LO 'URL_FROM_ABOVE'
curl -L -o trees.csv 'https://donnees.montreal.ca/dataset/b89fd27d-4b49-461b-8e54-fa2b34a628c4/resource/64e28fe6-ef37-437a-972d-d1d3f1f7d891/download/arbres-publics.csv'
Always use -L (follow redirects). Some URLs redirect.
Format-Specific Handling
CSV
- Encoding: UTF-8
- Separator: comma (standard)
- Load with pandas:
pd.read_csv('file.csv')
GeoJSON
- Standard GeoJSON (RFC 7946)
- Load with geopandas:
gpd.read_file('file.geojson')
- Or parse with
json.load()
Shapefile (ZIP)
- Downloaded as
.zip containing .shp, .dbf, .prj, .shx
- Unzip first, then load with geopandas:
gpd.read_file('file.shp')
- Coordinate system is usually NAD 83 MTM Zone 8 (EPSG:32188)
GBFS (BIXI)
- Not a downloadable file — it's a live REST API
- Entry point:
https://gbfs.velobixi.com/gbfs/gbfs.json
- Returns URLs for station info, station status, system info
curl -s 'https://gbfs.velobixi.com/gbfs/en/station_status.json'
curl -s 'https://gbfs.velobixi.com/gbfs/en/station_information.json'
GTFS / GTFS-Realtime (STM)
- GTFS static: ZIP file with
stops.txt, routes.txt, trips.txt, etc.
- GTFS-Realtime: Protocol Buffer format (requires
gtfs-realtime-bindings library)
- STM developer registration may be required for real-time feeds
- See the
transit domain skill for details
PDF
- Documentation, data dictionaries, methodology descriptions
- Not machine-queryable — use for reference only
Large File Considerations
Some datasets are substantial:
- Tree inventory CSV: ~135 MB (333,556 rows)
- Road condition data: large GeoJSON files
- Historical BIXI trips: multi-GB across years
For large files:
- Download once and cache locally
- Use
head or pandas.read_csv(nrows=100) to preview before full load
- For analysis, consider loading into a local SQLite database
Provenance / Provenance
Note on non-CKAN downloads: BIXI GBFS and STM GTFS feeds are hosted on separate infrastructure. See the transit domain skill for their specific URLs, auth requirements, and publishers.
Related Skills / Compétences connexes
understand-ckan — API reference and URL patternsdiscover-datasets — Find the right dataset firstquery-dataset — Query without downloading (DataStore API)
