TALAMANA · THE AI LITERACY MAP FOR ARCHITECTURE AND DESIGN · Foundations · AGE 14—16 · POSITIONAL · EVOLVING
What Indian cities are actually facing
The Lab puts heat, water and the city first for a young Indian architect.
Our position
The Lab puts three forces first for a young Indian architect: heat, water and the city itself. Not because nothing else matters — seismic risk, air, land, money and carbon all do — but because these three are measured, dated and public, and they decide the section of almost every building here. The India Meteorological Department declares heat waves by published criteria. NITI Aayog's water index reported that, as of 2014, no major Indian city supplied water round the clock to all its people. Floods are mapped from satellites. AI tools can map these layers. Designing against them is still your job.
Why we hold it
A claim with a source can be checked and built on; a slogan like "the planet is burning" gives you nothing to draw. Three forces with public data behind them give a first-year student something to draw against on the first day, and the habit they teach — which report, which year, which number — transfers to every other force later.
The strongest objection
"Three" is a curricular choice, not a fact about India. Seismic zoning, air pollution, land and tenure, ecology, affordability, infrastructure, material supply, regulation and embodied carbon could each claim a place on the list, and in Guwahati, Delhi or Bhuj a different three would be truer. A tidy trio risks teaching students to stop counting at three.
What would make us revise it
If the Lab's own site dossiers, across regions, show a fourth force deciding more sections than one of these three — seismic in the north-east, air in the Indo-Gangetic plain — the list changes, and the card with it. We check the dossiers every edition.
See it in the studio
A first-year site in Hubballi. One student writes "the site is very hot" on the sheet. Another writes the climate zone, the months the IMD issued heat warnings for the district last year, and where the afternoon sun falls on the plot — then draws the shade to match. The jury can argue with the second sheet. The first sheet only says it was hot.
Watch for this
Asking a chatbot "is my city water-stressed?" and taking the fluent paragraph as a fact. Ask instead: which report, which year, which number. If it cannot name them, it is only repeating the usual paragraph.
Try it
Pick your own city. Find one number about its heat from the IMD, one about its water from NITI Aayog's index or your city's water board, and one flood map from Bhuvan. Write each with its source and its date. Then take one claim you heard this year — "it was the hottest May ever", "we had a heat wave all week" — and check it against the IMD's own published station data and its criteria: did the numbers meet the rule, or not? Three lines and one check, and you have the start of a site dossier.
Prove it
Name the three forces the Lab puts first, say why they are the Lab's choice rather than a fact, and for one of them show a number, where it came from, and when it was measured.
How it works
Heat, water and flood data come from different bodies. Temperature comes from the IMD's station network, and the heat-wave criteria are published in its own FAQ. The IMD sets thresholds by station type, by absolute temperature and by departure from normal. A station qualifies only if its maximum reaches at least 40 °C on the plains or 30 °C in the hills; at the coast a departure of 4.5 °C counts once the maximum reaches 37 °C. On the plains, then, a departure of 4.5 to 6.4 °C above normal is a heat wave and more than 6.4 °C a severe one; or, whatever the normal, an actual maximum of 45 °C is a heat wave and 47 °C a severe one. The IMD declares it when the criteria are met at two or more stations in a meteorological sub-division on two consecutive days. Water comes from state and city boards, gathered nationally in NITI Aayog's Composite Water Management Index, whose 2019 edition notes that five of the world's twenty largest cities under water stress are in India. Flood extent comes from satellites, served on ISRO's Bhuvan portal. AI tools can fetch, join and map these layers quickly, and a language model can help you write the steps — the maps-as-data card shows how. The figures are measured again every season and the reports are reissued. That is why this card is marked EVOLVING: the forces stay, the figures change.
What this idea builds on
What this idea opens up
Sources
Open this idea on the map · The complete map · Logika · RBDS AI Lab, India · revised every edition.