Visualizing India's Health Data: How ICMR Uses iipmaps
ICMR's National Institute of Nutrition uses iipmaps to visualize national health survey data across India's states and districts producing publication-ready maps for policy research without GIS expertise.
April 8, 2026
India's public health research generates enormous volumes of data. The National Family Health Survey, district-level health surveys, and programs like SAMPADA produce datasets that span every state and district in the country. But raw numbers in spreadsheets don't drive policy - visualizations do.
ICMR's National Institute of Nutrition (NIN) needed a way to transform their survey data into clear, publication-ready maps. Maps that could go directly into research papers, policy briefs, and government reports. Maps that told the story of India's health landscape at a glance.
The Challenge: From Survey Data to Policy-Ready Visuals
Health researchers at ICMR-NIN work with complex, multi-dimensional datasets. The SAMPADA (Survey on Assessment of Malnutrition, Prevalence, Anaemia, Diet and Activities) study, for example, covers nutritional indicators across Indian states and districts.
The challenge wasn't collecting data - ICMR has decades of experience there. The challenge was visualization:
Publication standards are strict. Maps in research papers need to be self-contained, a reader should understand what they're seeing without reading surrounding text. That means clear titles, legends, source attributions, and metadata must be part of the image itself.
Consistency across a publication. A research paper might contain 10+ maps. Each needs to follow the same visual template same fonts, same legend placement, same color logic.
India-specific geography. Health data is collected at state and district levels. The mapping tool needs accurate, up-to-date boundaries for all 700+ districts, including recently created ones.
Researchers aren't designers. The scientists and statisticians producing these maps are experts in biostatistics and epidemiology, not graphic design or GIS.
The Solution: Self-Contained, Template-Driven Maps
iipmaps addressed each of these challenges with features that align naturally with the research publication workflow.
Self-contained map images
Every map exported from iipmaps includes the title, legend, data source, and any additional metadata as part of the image. When a map is shared whether in a journal, a policy document, or an email it carries its own context. No separate caption needed.
"Another benefit is the ability to add content beyond specific datasets, like total counts or data source, so the image can be shared as a singular entity with all relevant information."
— Dr. Raghavendra Rao, Scientist C (Biostatistics), ICMR-National Institute of Nutrition
Consistent templates for styling
Once the team established their preferred visual format - color palette, font choices, legend style, layout they could apply it consistently across every map in a publication. This template-driven approach means the 15th map in a series looks exactly like the first.
Spreadsheet-style data entry
The data workflow is straightforward: researchers prepare their data in standard tabular format (state or district names with corresponding values), paste it into iipmaps, and the map populates automatically. No data transformation, no GIS file preparation.
The Results: 28+ States Visualized for Policy Research
Using iipmaps, ICMR-NIN has produced maps covering:
State-wise health indicators from the SAMPADA study malnutrition prevalence, anaemia rates, dietary patterns across all Indian states,
District-level granularity where the data supports it revealing local patterns that state-level aggregates miss,
Publication-ready outputs used in research papers and government reports,
Consistent visual language across multiple publications, establishing a recognizable format for ICMR's data presentations.
Why Health Data Needs Better Visualization
India's health challenges are geographically varied. Malnutrition patterns in Rajasthan look nothing like those in Kerala. Anaemia prevalence varies dramatically between districts within the same state. Maternal health indicators cluster regionally in ways that tables can't convey.
When health data is presented as maps:
Regional patterns become immediately visible - clusters of high or low values that would take hours to spot in a spreadsheet
Policy interventions can be geographically targeted - instead of state-level averages, policymakers see which specific districts need attention
Comparisons across states become intuitive - a choropleth map makes relative performance obvious at a glance
Public communication improves - maps are more accessible than statistical tables for media, legislators, and the general public
How Other Health Organizations Can Use iipmaps
ICMR's use case is replicable for any organization working with India-specific health data:
State health departments visualizing disease surveillance data, immunization coverage, or hospital infrastructure
NGOs and development organizations mapping program coverage, impact metrics, or needs assessments
Public health researchers creating maps for journal submissions, conference presentations, or policy briefs
International health bodies (WHO, UNICEF) creating India-specific sub-national visualizations
The workflow is the same: take your existing tabular data, map it to India's states or districts in iipmaps, style it for your publication standards, and export.
iipmaps is trusted by 4+ government bodies including ICMR, PNGRB, NITI Aayog, and the Election Commission of India. Learn more about enterprise and government plans.
