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India Choropleth Map Tool

Generate interactive choropleth maps for India using state-wise JSON data.

Data & settings

Instructions

  • Use full state names exactly as they appear in official records
  • Common names: "Uttar Pradesh", "Tamil Nadu", "West Bengal", "Madhya Pradesh"
  • Use the "Show district lines" toggle to see internal boundaries

Map

Ready to map India

Add your state-wise data and load it to get started

Visual Creation

About Indian Map Generator

Visualise a dataset across Indian states and union territories as a choropleth, joining your rows to map regions by name or code. Most of the work is the join, not the drawing: India has 28 states and 8 union territories whose boundaries and names have changed repeatedly since 2014, so any dataset older than the map — or newer — will fail to match on some regions.

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Frequently asked questions

Why do some of my rows fail to join to a region?
Name variance. Orissa versus Odisha, Pondicherry versus Puducherry, Uttaranchal versus Uttarakhand, and the persistent NCT of Delhi versus Delhi versus National Capital Territory of Delhi. Add trailing whitespace, differing ampersand handling in Jammu & Kashmir, and casing. Join on ISO 3166-2:IN codes where you have them — IN-MH, IN-KA — since they are stable across renames. Where you only have names, normalise both sides aggressively and surface unmatched rows rather than silently dropping them, which is how a state ends up blank without anyone noticing.
Which boundary changes will break an older dataset?
Telangana separated from Andhra Pradesh in 2014, so pre-2014 figures for Andhra Pradesh include territory now attributed elsewhere. Jammu and Kashmir was reorganised in 2019 into two union territories, J&K and Ladakh, removing a state. Dadra and Nagar Haveli merged with Daman and Diu in 2020, so two entries in older data map to one region. Any per-capita or growth figure spanning these dates compares differently defined areas and needs explicit reconciliation.
Should I map raw counts or normalise them?
Choropleths encode value by area fill, and area is already doing visual work, so raw counts simply redraw population. Uttar Pradesh at roughly 240 million will dominate any absolute measure and tell you nothing you did not know. Normalise to a rate — per hundred thousand, per capita, per unit area — so colour carries actual signal. If absolute magnitude genuinely matters, use proportional symbols placed on the map instead, where size encodes the count and the boundary does not distort it.
How should I choose colour scales and class breaks?
Use a sequential scale for ordered quantities and a diverging one only when there is a meaningful midpoint, such as change against a national average. The break method changes the story: equal intervals expose outliers but leave most regions in one bin when the distribution is skewed, which Indian socioeconomic data usually is; quantiles guarantee even fill but imply differences between adjacent bins that may be trivial. State the method in the legend, and keep the class count at five to seven since readers cannot match more shades to a key.
Does the projection matter for an India map?
Yes, for area comparison. Web Mercator, the default in most web mapping, inflates area with latitude — across roughly 8 to 37 degrees north, the northern states are exaggerated relative to the southern ones, which subtly overweights them visually. An equal-area projection such as an Albers conic tuned to Indian latitudes preserves relative area and is the right choice for a thematic map. Also, boundaries in the north are politically contested, so different source files disagree; pick one deliberately and note the source.