UDA-city heat risk is highest in dense, vulnerable hotspots.

Present hot-humid SUEWS run for 10 synthetic neighbourhoods. The final indicator keeps heat hazard, population exposure, and social vulnerability separate, then combines them with a blended equity-aware score.

Present hot-humid scenario SUEWS/SuPy 2026.6.5 Danger threshold: T2 > 35 C 14-day spin-up removed

62 h Most dangerous-heat hours: Jade Gardens

1.000 Highest combined risk score: Kampong Lama

3 pillars Hazard, exposure, and vulnerability are shown separately before combining.

Risk Ranking

Kampong Lama1.000

Dhobi Lines0.823

Fuzhou Lanes0.799

Mlima Moto0.521

Jade Gardens0.446

Taman Melati0.374

Serendib Rise0.191

Lusitano Square0.111

Victoria Exchange0.097

Zheng He Towers0.000

Why The Ranking Changes

Indicator A: no-zero geometric risk

A = (hazard x exposure x vulnerability)^(1/3)

This keeps the UNDRR-style trio together, but avoids the false-zero problem where a populated neighbourhood is treated as having no exposure.

Indicator C: equity-weighted risk

C = 0.25 hazard + 0.25 exposure + 0.50 vulnerability

This gives more weight to people with less ability to cope: low AC access, outdoor work, deprivation, older adults, and young children.

Final score: risk = 0.5 A + 0.5 C, re-scaled to 0-1. This keeps the physical heat result visible while making the social vulnerability argument explicit.

Hazard, Exposure, Vulnerability

Kampong Lama hazard 0.677 exposure 1.000 vulnerability 0.966

Jade Gardens hazard 1.000 exposure 0.267 vulnerability 0.543

Fuzhou Lanes hazard 0.355 exposure 1.000 vulnerability 0.981

Mlima Moto hazard 0.081 exposure 1.000 vulnerability 1.000

Where The Bridge Holds

The SUEWS run gives a clear environmental heat hazard for the same weather window across all 10 neighbourhoods.
The risk framing separates the physical heat signal from who is exposed and how able people are to cope.
The changed score fixes the original false-zero exposure problem while keeping the three-pillar structure visible.

Where It Breaks

T2 > 35 C is a dry-bulb heat proxy, not a health outcome. A humid-heat metric would be a strong next step.
The socio-economic layer is synthetic and neighbourhood-level. It supports relative ranking, not absolute claims about real people.
The final index is a judgement call. The weights make an equity argument, not a universal law.
0.000 means lowest relative score here, not no heat risk.

Data Table

Rank	Neighbourhood	Type	Heat hours	Hazard	Exposure	Vulnerability	Risk
1	Kampong Lama	hotspot	42	0.677	1.000	0.966	1.000
2	Dhobi Lines	hotspot	26	0.419	1.000	0.943	0.823
3	Fuzhou Lanes	hotspot	22	0.355	1.000	0.981	0.799
4	Mlima Moto	hotspot	5	0.081	1.000	1.000	0.521
5	Jade Gardens	refuge	62	1.000	0.267	0.543	0.446
6	Taman Melati	refuge	47	0.758	0.267	0.570	0.374
7	Serendib Rise	refuge	26	0.419	0.267	0.509	0.191
8	Lusitano Square	core	5	0.081	0.833	0.385	0.111
9	Victoria Exchange	core	5	0.081	0.833	0.362	0.097
10	Zheng He Towers	core	2	0.032	0.833	0.325	0.000

Download the derived data: risk_present_ac_combined.csv

SUEWS Citation

Jarvi, L., Grimmond, C.S.B. and Christen, A. (2011). The Surface Urban Energy and Water Balance Scheme (SUEWS): Evaluation in Los Angeles and Vancouver. Journal of Hydrology, 411(3-4), 219-237. DOI: 10.1016/j.jhydrol.2011.10.001.

Ward, H.C., Kotthaus, S., Jarvi, L. and Grimmond, C.S.B. (2016). Surface Urban Energy and Water Balance Scheme (SUEWS): Development and evaluation at two UK sites. Urban Climate, 18, 1-32. DOI: 10.1016/j.uclim.2016.05.001.

Guideline alignment: this page focuses on the public GitHub Pages story, policy relevance and honest bridging, presentation clarity, scientific caveats, and SUEWS citation requirements described in the hackathon task brief.