Trends
Historical analytics derived from the dataset and git history. Updates on every push. As the dataset is young, many series are sparse — that's honest, not a bug.
60
providers
25
hospitals
1
user reports
1
contributors
Freshness
How many records are in each verification state right now.
verified 0 stale 0 source-published 54 unverified 6 dead 0 disputed 0
Provider types
- 12 — govt-108
- 22 — hospital-owned
- 26 — private-aggregator
Geographic coverage (by service-area pincode)
- 10 — Bengaluru
- 8 — Delhi NCR
- 7 — Mumbai
- 7 — Pune
- 6 — Hyderabad
- 6 — Chennai
- 5 — Kolkata
A provider counts toward a city if any of its declared service-area pincodes falls in that city's range.
Fare dispersion (provider-claimed base fare, INR)
No fare data yet. Fares are populated on verification calls — when we ask "what's your base fare?" and log the answer with a date.
Response time (from user reports, minutes)
- Min: 17 min
- Median: 17 min
- Max: 17 min
- n = 1 dispatched-call reports
Activity (last 12 weeks)
Commits to data/ by category. Verify = re-verification calls. New = record additions. Correct = field fixes. Report = user reports merged.
Records added (last 12 months)
Net new YAML records (providers + hospitals) per calendar month.
What this page shows that others don't
- Verification velocity — how fast we move records from grey to green. A leading indicator of project health.
- Fare dispersion — the spread between cheapest and most expensive base fare. Useful for journalism on price transparency.
- Response time bands from user reports — the closest we can get to "how long do ambulances actually take?" without operating any fleet.
- Geographic concentration — where the data is richest. Surfaces the gaps you'd want to address with a city-captain programme.
- Contributor count over time — the difference between "open in licence" and "open in practice".
Raw JSON: /v1/trends.json · Generated on each build · CC BY-NC-SA 4.0