Your recent study, Mapping Heat Inequality Across Neighbourhoods in Delhi, goes beyond temperature readings by weaving in lived experience and household-level data. Why was it important to bring that human dimension (qualitative data) into the analysis, and what did it reveal that physical (quantitative) data alone could not?

Answered on: August 3, 2026
Answered by:
Karan Shah Karan Shah COO Artha India
Answer

Two neighbourhoods just 2-3 km apart can differ by up to 5°C in experienced heat, the difference between an uncomfortable afternoon and a potentially dangerous one. But temperature alone doesn’t tell the full story. Heat maps can tell us where temperatures spike, but they don’t tell us who actually bears the brunt of it.

By layering in household-level data, we moved from mapping exposure to understanding real impact: the same heat hits very differently depending on housing conditions, access to cooling, and the ability to manage rising energy costs. Some households could keep their homes livable; others, just down the road, dealt with constant heat, broken sleep, and no real relief.

This qualitative survey layer helped surface things that spatial data alone just can’t capture – disrupted sleep, fatigue, lost work hours, and rising stress, often from relatively small increases in felt temperatures. It also made it clear how uneven people’s ability to cope is; some households can rely on appliances or better construction and tree cover while others are dealing with prolonged exposure and very limited options to adapt.

So it really shifts how we think about heat; it’s not just an environmental issue, it’s also about inequality. And unless we bring in these lived experiences, we risk designing solutions that miss the people who are most affected. With most heat action plans still based on citywide averages, this kind of granular insight is key to making responses more targeted and grounded in reality.


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