Election scenario lab
Choose a published state or survey dataset, explore constituency-level estimates, and test hypothetical vote-share swings. Each dataset is stored separately by administrators.
Scenario controls
Each state or survey is stored separately. Publishing a dataset with the same name replaces only that dataset.
Adjust a party's support in percentage points. The remaining parties are proportionally rescaled so the constituency total stays at 100%. Seats marked Tough Fight remain separately classified and are not credited to a party.
Party seat projection
Constituency results
| Constituency | Region | Seat classification | Winner vote % | Margin (pp) | Runner-up |
|---|---|---|---|---|---|
| Waiting for published data… | |||||
Constituency map
Assembly constituency map The map loads state-wise post-2008 Assembly constituency boundaries and colours seats using the selected published survey. Punjab uses a 117-seat Assembly layer. Boundary data source India constituency maps. Some states may have source-specific boundary limitations.
Loading constituency boundaries…
Data quality checklist
Use stable IDs and official constituency names. Names alone may be ambiguous across states.
If provided, use respondent weights for estimates; keep sample size and effective sample size available.
Add design effects, nonresponse adjustments and intervals before presenting a forecast.
Never upload names, phone numbers, addresses or other direct identifiers to the public simulator.