High-exposure cells across MH-36.
Surface cells where population sits in low-lying cropland / built / wetland LULC with thin canopy buffer. The proxy is morphology-only; the full catastrophe stack lives in the 336-col cube.
§1 The buyer's question
“Where in Maharashtra am I most exposed to flood/heat risk on populated cells with no canopy buffer? Sort by that, slice by district, attach lineage to every cell.”
§2 The cube columns it touches
scope : mh36
cube_fp : dcc1845460cbd47c…
weights : pop_density_per_km2 +0.40 (WorldPop India v3-2020)
built_fraction +0.20 (GOB v3 ÷ R5)
terrain_elev_m -0.30 (Copernicus GLO-30 — low-lying penalised)
ndvi_p50 -0.10 (low canopy buffer)
filter : lulc_class_primary IN (40 cropland, 50 built-up, 90 wetland, 95 mangrove)
min_witnesses ≥ 3
# IN the 45-dim morphology vector v2 (long-term climate dynamics, with per-cell GUM uncertainty):
# Tier-Q (IMD 125-yr): rainfall / monsoon / 4-season trends (Theil-Sen + Mann-Kendall),
# interannual CV, annual-max-1-day extreme trend, Oliver concentration index.
# Tier-U (NOAA GSOD ~50-yr): annual-mean temperature + warming trend (Theil-Sen +
# Mann-Kendall), summer-max / winter-min, IDW from stations.
# Each trend carries an honest per-cell uncertainty (Tier-Q slope SE, Tier-U IDW SD)
# so a noisy / sparsely-supported trend is down-weighted, never sold as exact.
# STILL cube-only (high-frequency / realtime, not yet vectorised):
# Tier-H/H′ IMERG precip return periods, Tier-J CHIRPS daily extremes,
# Tier-M CPCB AQI, Tier-N WRIS hydro, Tier-S WRIS-GW. All in the 336-col cube on S3.§3 Worked example — live from the cube
| # | Anchor | District | LULC | pop /km² | elev m | NDVI | score |
|---|---|---|---|---|---|---|---|
| 1 | 483:4830000240 | Mumbai Suburban | 50 built up | 56,184.4 | 28 | 0.145 | 15.94 |
| 2 | 497:4970000807 | Thane | 50 built up | 48,459.4 | 26 | 0.147 | 15.63 |
| 3 | 483:4830000257 | Mumbai Suburban | 50 built up | 55,204.5 | 24 | 0.138 | 15.61 |
| 4 | 497:4970001375 | Thane | 50 built up | 48,317.9 | 9 | 0.125 | 15.58 |
| 5 | 483:4830000241 | Mumbai Suburban | 50 built up | 49,870.1 | 42 | 0.150 | 14.35 |
| 6 | 483:4830000103 | Mumbai Suburban | 50 built up | 43,637.4 | 27 | 0.143 | 14.32 |
| 7 | 483:4830000221 | Mumbai Suburban | 50 built up | 50,563.3 | 20 | 0.206 | 14.06 |
| 8 | 483:4830000319 | Mumbai Suburban | 50 built up | 49,814.1 | 19 | 0.152 | 14.05 |
| 9 | 483:4830000347 | Mumbai Suburban | 50 built up | 46,560.3 | 17 | 0.173 | 14.03 |
| 10 | 483:4830000063 | Mumbai Suburban | 50 built up | 44,364.6 | 10 | 0.093 | 13.99 |
The 45-dim morphology vector v2 now carries the long-term climate-dynamics axes directly (IMD-125yr rainfall / monsoon / 4-season Theil–Sen trends, Mann–Kendall significance, interannual variability and the precipitation-concentration index via Tier-Q; plus NOAA-GSOD ~50-yr temperature-warming trends via Tier-U — each with an honest per-cell trend uncertainty). What it does not yet carry are the high-frequency catastrophe layers parametric insurance prices on directly — IMERG precip return periods, CHIRPS daily extremes, WRIS river-discharge percentiles, GLOFAS flood return curves. Those ship in the mh36-unified-v1.7 cube on S3 today and can be wired into a per-customer feature set on request. See audit artefacts for the per-tier coverage figures.
§4 The workflow in Explorer
Open Explorer → Inspect and pick a Mumbai-Suburban (483) or Palghar (665) anchor.
Find Twins to surface inland analogues — the difference tells you the coastal exposure premium.
Use the climate-risk weights above or tune your own to rank exposure across all 36 districts.
Parametric insurance customers get the full climate-tier feature set wired into a custom vector before pricing.