Metro daylight demand features, daily
Daily daylight-geometry features for 20 US/Canadian metros (15 US + 5 CA, keyed by ICAO airport code, joining 1:1 with metro_degree_days), 2025-01-01 to 2027-12-31 (21,900 day x metro rows), joinable to shop orders, payments, or leads by ISO date and metro. Computed with the NOAA solar-position equations from metro latitude/longitude in local IANA time: sunrise/sunset and civil twilight (HH:MM local), day_length_min, darkness_hours, evening_daylight_min (daylight after 17:00 local — the post-work shopping window), morning_daylight_min (before 09:00), is_dst and dst_transition flags, daylight_delta_min (day-length momentum vs the previous day), and daylight_demand_score (0-100 = round(100*(0.7*evening min-max + 0.3*day-length min-max)) per metro). Units: minutes, hours, HH:MM local, 0/1 flags, 0-100 score. Caveats: computed, not observed (NOAA approximate equations; no terrain/elevation effects); scores are relative within each metro, not comparable across metros; Phoenix observes no DST.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Source
- metro_daylight_intel
- Lignes
- 21 900
- Colonnes
- 21
- Cadence de la source
- Annuelle
- Dernière actualisation
- 29 sept. 2026
- Thème
- economy
| Colonne | Type | Description |
|---|---|---|
| date | date | Calendar day (ISO date). (unit: date) |
| country_code | string | ISO alpha-3 country code: USA or CAN. |
| metro | string | Metro name (matches metro_degree_days). |
| state_province | string | US state or Canadian province. |
| icao | string | ICAO airport code of the metro's primary international airport — the join key shared with metro_degree_days. |
| lat | float | Station latitude, decimal degrees. (unit: deg) |
| lon | float | Station longitude, decimal degrees. (unit: deg) |
| tz | string | IANA timezone used for local times. |
| sunrise_local | string | Sunrise in metro local time (HH:MM), NOAA equations. (unit: HH:MM) |
| sunset_local | string | Sunset in metro local time (HH:MM), NOAA equations. (unit: HH:MM) |
| day_length_min | float | Minutes from sunrise to sunset. (unit: min) |
| civil_dawn_local | string | Civil dawn (sun 6 deg below horizon), local HH:MM. (unit: HH:MM) |
| civil_dusk_local | string | Civil dusk (sun 6 deg below horizon), local HH:MM. (unit: HH:MM) |
| evening_daylight_min | float | Minutes of daylight after 17:00 local — the post-work shopping window (retail foot-traffic proxy). (unit: min) |
| morning_daylight_min | float | Minutes of daylight before 09:00 local. (unit: min) |
| darkness_hours | float | 24 minus day_length_min / 60. (unit: h) |
| is_dst | integer | 1 when the metro observes daylight saving time that day. |
| dst_transition | integer | 1 on the spring-forward / fall-back day (DST status differs from the previous day). |
| daylight_delta_min | float | Change in day_length_min vs the previous day (momentum signal). (unit: min) |
| daylight_demand_score | integer | 0-100: round(100 * (0.7 * evening_daylight_min min-max + 0.3 * day_length_min min-max)) normalized per metro over the panel. 100 = the year's longest retail evenings. Relative within a metro only. |
| row_id | string | Deterministic row identifier: 16-hex sha256 of 'metro_daylight|<date>|<icao>'. |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| date | country_code | metro | state_province | icao | lat | lon | tz | sunrise_local | sunset_local | day_length_min | civil_dawn_local | civil_dusk_local | evening_daylight_min | morning_daylight_min | darkness_hours | is_dst | dst_transition | daylight_delta_min | daylight_demand_score | row_id |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2025-01-01 | CAN | Ottawa | Ontario | CYOW | 45,323 | -75,669 | America/Toronto | 07:41 | 16:29 | 527,4 | 07:07 | 17:03 | 0 | 78,1 | 15,21 | 0 | 0 | 0 | 0 | 608bb612bf53602f |
| 2025-01-01 | CAN | Montreal | Quebec | CYUL | 45,467 | -73,733 | America/Toronto | 07:34 | 16:20 | 526,3 | 07:00 | 16:55 | 0 | 85,3 | 15,23 | 0 | 0 | 0 | 0 | 55460fa799d2f342 |
| 2025-01-01 | CAN | Vancouver | British Columbia | CYVR | 49,194 | -123,184 | America/Vancouver | 08:07 | 16:23 | 496,3 | 07:30 | 17:01 | 0 | 52,5 | 15,73 | 0 | 0 | 0 | 0 | 36e7ef62b9a68fe7 |
| 2025-01-01 | CAN | Calgary | Alberta | CYYC | 51,114 | -114,02 | America/Edmonton | 08:39 | 16:38 | 478,6 | 08:00 | 17:17 | 0 | 20,3 | 16,02 | 0 | 0 | 0 | 0 | 9e48b4cb30dabf1c |
| 2025-01-01 | CAN | Toronto | Ontario | CYYZ | 43,677 | -79,631 | America/Toronto | 07:51 | 16:50 | 539 | 07:19 | 17:23 | 0 | 68,1 | 15,02 | 0 | 0 | 0 | 0 | 759ea28ae56a3ed4 |
| 2025-01-01 | USA | Atlanta | Georgia | KATL | 33,63 | -84,442 | America/New_York | 07:41 | 17:39 | 597,5 | 07:14 | 18:06 | 39,4 | 78,1 | 14,04 | 0 | 0 | 0 | 4 | b6f78ed6b25bd047 |
| 2025-01-01 | USA | Boston | Massachusetts | KBOS | 42,361 | -71,01 | America/New_York | 07:13 | 16:20 | 547,9 | 06:41 | 16:52 | 0 | 107 | 14,87 | 0 | 0 | 0 | 0 | bbd590121f19e522 |
| 2025-01-01 | USA | Denver | Colorado | KDEN | 39,847 | -104,656 | America/Denver | 07:19 | 16:43 | 563,6 | 06:49 | 17:13 | 0 | 100,3 | 14,61 | 0 | 0 | 0 | 0 | 1216b57efbd915be |
| 2025-01-01 | USA | Dallas | Texas | KDFW | 32,897 | -97,022 | America/Chicago | 07:30 | 17:31 | 601,1 | 07:03 | 17:58 | 31,5 | 89,6 | 13,98 | 0 | 0 | 0 | 4 | 02f56ad073f40dfe |
| 2025-01-01 | USA | Detroit | Michigan | KDTW | 42,231 | -83,331 | America/Detroit | 08:01 | 17:10 | 548,7 | 07:30 | 17:42 | 10,6 | 58,1 | 14,85 | 0 | 0 | 0 | 3 | a4366701858492bb |
- Actuelle
20260929T085645Z-2545f17ea24b · sha256 2545f17ea24b…
21 900 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/metro_daylight_intel/metro_daylight_demand_daily" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/metro_daylight_intel/metro_daylight_demand_daily").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/metro_daylight_intel/metro_daylight_demand_daily
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260929T085645Z-2545f17ea24b et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
metro_daylight_intel. (2026). Metro daylight demand features, daily [Data set, snapshot 20260929T085645Z-2545f17ea24b, sha256 2545f17ea24b]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/fr/datasets/metro_daylight_intel/metro_daylight_demand_daily?snapshot=20260929T085645Z-2545f17ea24b
@misc{dz_metro_daylight_intel_metro_daylight_dema_2545f17e,
title = {{Metro daylight demand features, daily}},
author = {{metro\_daylight\_intel}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/metro_daylight_intel/metro_daylight_demand_daily?snapshot=20260929T085645Z-2545f17ea24b}},
note = {Snapshot 20260929T085645Z-2545f17ea24b, sha256 2545f17ea24b1e606dea9b5e4fa88021b29342463152daef0351e7c5ba9a8560; accessed 2026-10-02}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=metro_daylight_intel%2Fmetro_daylight_demand_daily&lang=fr&theme=auto&snapshot=20260929T085645Z-2545f17ea24b&x=date&y=lat&agg=avg" title="Metro daylight demand features, daily" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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