Hottest Isn't Highest-Risk
The hottest neighbourhood is not the highest-risk neighbourhood. Jade Gardens is hottest by dry-bulb dangerous-heat hours now and under +2.5°C, but Kampong Lama is where heat is most dangerous to people in this socio-economic risk framing because substantial heat hazard coincides with high exposure and high vulnerability.
Why the Hottest Place Isn't the Most Dangerous
Jade Gardens is the physical heat extreme, but Kampong Lama is the people-risk priority because heat overlaps with more exposure and more vulnerability.
The Question
Across the ten UDA-city neighbourhoods, where is heat most dangerous to people now and under the official +2.5°C hotter-future stress test, and is that the same as where it is simply hottest?
UDA-City SUEWS Configuration and Assumptions
The workflow uses uda-city.yml, the official ten-site synthetic hot-humid city config from UMEP-dev/uda-city-hackathon. The pinned UDA-city commit checked for this final package is 0df6835c5832fb0ec78094d6acd09a45a953826b. All neighbourhoods share the same forcing; differences come from morphology, land cover, and socio-economic sidecars.
Locked physics: NARP net radiation, classic OHM storage heat, QF/emissions off, and SUEWS/SuPy runtime 2026.6.5. The required assess_readiness and validate_config SUEWS-agent calls passed before SUEWS was run.
Official Present and +2.5°C Future Method
The present scenario uses forcing/present_hot_humid/UDA_2024_data_60.txt. The future scenario uses forcing/future_hot_humid/UDA_2024_data_60.txt, the official humidity-preserving +2.5°C pseudo-warming: air temperature is raised uniformly, relative humidity is held constant, longwave down is scaled for a warmer atmosphere, and wind, pressure, shortwave, and rain are unchanged. This is a stress test, not a downscaled climate projection.
Hazard, Exposure, Vulnerability, and Risk Indicator
Hazard
The modelled heat condition from SUEWS. The core hazard is dangerous-heat hours: post-spin-up hours where hourly mean 2 m air temperature, T2, exceeds 35°C in each neighbourhood.
Exposure
Who or what is present in the affected neighbourhoods. Here it is daytime population density from the UDA-city sidecar, scaled across the ten neighbourhoods.
Vulnerability
Why some exposed people may be more at risk. The index combines older age, young children, low air-conditioning access, outdoor work, and deprivation from the synthetic socio-economic sidecar.
Combination Rule
Each pillar is min-max scaled to 0-1 and combined as risk_index = (hazard * exposure * vulnerability)^(1/3), then scaled again to 0-1 for ranking. The geometric mean is conservative: weak exposure or vulnerability lowers risk even where hazard is high.
Results at a Glance
Present vs Future Hazard Hours
Future stress adds dangerous-heat hours across the city, but the largest physical heat signal is still not the same thing as highest people-risk priority.
Hazard Rank vs Risk Rank
Lines that cross show where temperature ranking and people-risk ranking diverge. Jade Gardens and Kampong Lama make the split visible.
Neighbourhood Types
hotspot
Dense, exposed, and vulnerable neighbourhoods where the bridge from modelled heat to people-risk is strongest. These areas may need early outreach even when they are not the hottest places on a temperature map.
core
Mixed central neighbourhoods with moderate exposure and lower vulnerability in the synthetic sidecar. They remain important for citywide heat management, but they are not the top people-risk priorities here.
refuge
Lower-exposure neighbourhoods in this dataset. Some are physically hot, including Jade Gardens, but their people-risk score is low because the synthetic exposure layer places fewer people there.
If you change what counts as risk, who needs help first?
Move the levers to compare heat, who is exposed, and who is more vulnerable. The key finding is visible before any numbers: the hottest neighbourhood is not automatically the most dangerous to people.
Core insight: Jade Gardens is the hottest place in the dry-bulb SUEWS results, but Kampong Lama becomes the top people-risk priority once exposure and vulnerability are included.
Quick definitions in everyday language
- Hazard
- The dangerous heat signal from SUEWS, such as hours above 35°C.
- Exposure
- How many people are present in the neighbourhood during the day.
- Vulnerability
- Why exposed people may be less able to cope, such as older age, young children, low air-conditioning access, outdoor work, or deprivation.
People-Risk Priority Ranking
Each row breaks a neighbourhood's risk into the three things that make it: how dangerous the heat is, how many people are exposed, and how vulnerable they are. That is why the hottest places are not always the highest risk.
Show the heat-vs-risk scatter
Hottest Versus Most Dangerous
Neighbourhoods toward the upper right combine stronger heat with higher people-risk. Jade Gardens sits far right; Kampong Lama sits highest.
Show the numbers
| Priority | Neighbourhood | Type | Hazard signal | Scaled hazard | Exposure | Vulnerability | Risk score |
|---|
| Type | Time of day | Dry-bulb danger hours | WBGT screen hours | Hot sunny low-wind hours | Max T2 | Max WBGT proxy |
|---|
Heat Hazard and Risk Result
Every neighbourhood gains dangerous-heat hours under +2.5°C, yet the people-risk ordering barely moves: Kampong Lama remains the top priority, while the hottest places are not necessarily the most dangerous to people.
People-risk combines modelled heat hazard, exposure, and vulnerability; it is not the same as hottest rank.
| Neighbourhood | Type | Hottest rank | Risk rank | Risk score |
|---|---|---|---|---|
| Kampong Lama | hotspot | 2 | 1 | 1.000 |
| Fuzhou Lanes | hotspot | 5 | 2 | 0.930 |
| Dhobi Lines | hotspot | 4 | 3 | 0.923 |
| Mlima Moto | hotspot | 7 | 4 | 0.761 |
| Lusitano Square | core | 8 | 5 | 0.280 |
| Victoria Exchange | core | 9 | 6 | 0.225 |
| Jade Gardens | refuge | 1 | =7 exposure = 0 | 0.000 |
| Taman Melati | refuge | 3 | =7 exposure = 0 | 0.000 |
| Serendib Rise | refuge | 6 | =7 exposure = 0 | 0.000 |
| Zheng He Towers | core | 10 | =7 vulnerability = 0 | 0.000 |
Show Full Present And Future Results Table
| Neighbourhood | Type | Present hazard hours | Present hottest rank | Present risk rank and score | Future hazard hours | Future hottest rank | Future risk rank and score | Added hours | Plain-English read |
|---|---|---|---|---|---|---|---|---|---|
| Kampong Lama | hotspot | 42 | 3 | 1 score 1.000 | 249 | 2 | 1 score 1.000 | 207 | Highest people-risk priority now and in the future because high hazard coincides with high exposure and high vulnerability. |
| Fuzhou Lanes | hotspot | 22 | 6 | 3 score 0.800 | 212 | 5 | 2 score 0.930 | 190 | Not among the hottest places, but very high vulnerability makes it the second-highest future people-risk priority. |
| Dhobi Lines | hotspot | 26 | 4= | 2 score 0.833 | 217 | 4 | 3 score 0.923 | 191 | High exposure and vulnerability turn moderate-to-high hazard into high people-risk. |
| Mlima Moto | hotspot | 5 | 7= | 4 score 0.429 | 149 | 7 | 4 score 0.761 | 144 | Risk grows strongly when future hazard rises over a very vulnerable exposed population. |
| Lusitano Square | core | 5 | 7= | 5 score 0.176 | 129 | 8 | 5 score 0.280 | 124 | Moderate exposure but lower vulnerability keeps risk below the hotspot group. |
| Victoria Exchange | core | 5 | 7= | 6 score 0.151 | 120 | 9 | 6 score 0.225 | 115 | Core area with lower vulnerability, so it is not a top risk priority in this framing. |
| Jade Gardens | refuge | 62 | 1 | =7 score 0.000exposure = 0 | 260 | 1 | =7 score 0.000exposure = 0 | 198 | Largest split: physically hottest in both scenarios, but low people-risk in this relative dataset because exposure is zero. |
| Taman Melati | refuge | 47 | 2 | =7 score 0.000exposure = 0 | 243 | 3 | =7 score 0.000exposure = 0 | 196 | Large split: hot refuge area; hazard is high, but the hazard-to-people bridge is weak here. |
| Serendib Rise | refuge | 26 | 4= | =7 score 0.000exposure = 0 | 205 | 6 | =7 score 0.000exposure = 0 | 179 | Substantial future warming, but low priority in this risk index because exposure is zero. |
| Zheng He Towers | core | 2 | 10 | =7 score 0.000vulnerability = 0 | 77 | 10 | =7 score 0.000vulnerability = 0 | 75 | Lowest dry-bulb hazard and lowest vulnerability score in this dataset, so the geometric-mean risk score is zero. |
Defended Choices and Caveats
I use hourly mean T2 > 35°C after 14 spin-up days for the core hazard because it is simple, reproducible, and consistent with the UDA-city reference bridge. The WBGT screen and time-of-day tables are robustness checks from the same completed SUEWS outputs, not a replacement for the core threshold. SUEWS gives environmental hazard, not health outcomes. The socio-economic layer is synthetic, min-max scaling is relative to this dataset, neighbourhood averages hide individuals, and QF is off so population affects exposure rather than modelled anthropogenic heat.
WBGT, HHI, and time-of-day checks were used as robustness checks, not as the core risk indicator.
Show Robustness Checks: WBGT, HHI, and Time of Day
Time-of-Day Robustness Check
Heat stress is not only an afternoon problem. Under +2.5°C, WBGT-screening hours remain high into evening and night, so worker protections and public-health outreach need to cover recovery time as well as daytime peaks.
What These Metrics Mean
WBGT
Wet Bulb Globe Temperature is the stronger occupational heat-stress standard for outdoor workers because it accounts for temperature, humidity, radiant heat, and wind. The analysis adds a humidity-aware WBGT screening proxy from SUEWS T2, RH2, U10, and Kdown, but it is not a full measured WBGT. A full WBGT assessment would use a field monitor or a validated globe-temperature model, consistent with OSHA guidance.
HHI
The CDC Heat & Health Tracker points toward the right public-health framing: combine heat, population health, environmental conditions, and social vulnerability. A literal Heat and Health Index was not computed because UDA-city is synthetic and not a ZIP-code health dataset. Instead, this analysis uses the HHI logic to make exposure and vulnerability explicit in the socio-economic risk bridge.
Where the Link Holds and Breaks
| Pattern | Evidence | Meaning for decisions |
|---|---|---|
| The link holds in hotspot settlements. | Kampong Lama, Fuzhou Lanes, Dhobi Lines, and Mlima Moto combine high exposure, high vulnerability, and increasing future hazard. | People-protection measures should not wait for these areas to be the absolute hottest. They are already high-risk because people and vulnerability are concentrated there. |
| The link breaks in refuge neighbourhoods. | Jade Gardens is the hottest by dry-bulb dangerous hours in both scenarios, but its exposure score is the minimum in this relative dataset. | Hottest-place maps alone would over-prioritise some physical hot spots and under-prioritise exposed/vulnerable communities. |
| Future warming strengthens the bridge. | All neighbourhoods gain dangerous-heat hours, but hotspot risks remain highest when those hours meet high exposure and vulnerability. | Adaptation should combine citywide heat reduction with targeted heat-health operations in high-risk neighbourhoods. |
| Time of day adds a second warning. | Future WBGT screening hours remain high in evening and night even when dry-bulb hours above 35°C are daytime only. | Recommendations must include overnight recovery, not only afternoon shade. |
| Uncertainty remains in the people layer. | The socio-economic sidecar is synthetic, and AC access, outdoor work, deprivation, and age are represented as neighbourhood averages. | Before implementation, local agencies should replace synthetic vulnerability values with household, labour, health, and infrastructure data. |
Policy Recommendations Across Governance Scales
| Scale Of Governance | Actionable Recommendation | Why This Follows From The Findings |
|---|---|---|
| Neighbourhood and community groups | In Kampong Lama, Fuzhou Lanes, Dhobi Lines, and Mlima Moto, organise door-to-door heat checks, map residents who need assistance, open shaded water points before the morning heat builds, and keep evening check-ins active when nights stay humid. | The highest-risk areas are not simply the hottest; they are places where exposed and vulnerable people are present during dangerous heat. |
| Municipal heat-health teams | Use the risk ranking to deploy cooling centres, mobile outreach, transport to cool spaces, public messaging, and alert escalation. Use the hazard map separately for citywide heat-reduction investments. | Hazard and risk answer different questions. The city needs both physical cooling and people-targeted operations. |
| Labour and occupational safety agencies | Require work-rest-shade-water plans for outdoor work, add on-site WBGT monitoring during alerts, shift heavy work away from the morning-to-afternoon hazard window, and include acclimatisation procedures for new or returning workers. | The WBGT screen shows that humidity, sun, wind, and recovery time matter for workers, not only air temperature. |
| Urban planning, housing, and utilities | Prioritise cool roofs, shaded pedestrian corridors, tree or shade structures where water is feasible, ventilation paths, reflective surfaces, and reliable electricity for high-risk hotspot areas. | Physical heat exposure and low adaptive capacity overlap most strongly in hotspot neighbourhoods. |
| Health and social protection systems | Pre-register older adults, households with low AC access, outdoor workers, and medically vulnerable residents for heat-wave calls, clinic triage, medication advice, and emergency cooling support. | The vulnerability pillar explains why the same hazard can produce very different human risk. |
| Regional and national government | Fund heat-resilient settlement upgrading, local sensor networks, occupational heat standards, heat-health surveillance, and social protection payments during extended heat events. | Local teams can target interventions, but they need finance, standards, and data systems beyond neighbourhood control. |
SUEWS-Agent Tool-call Log
The required tool-call log is included in the repository at transcripts/suews_agent_tool_log.md, with a CSV copy, workflow notes, and a public Codex session transcript in transcripts/. The readiness and validation JSON diagnostics are saved in transcripts/diagnostics/. The first two calls were assess_readiness and validate_config; both passed before SUEWS was run. The WBGT and time-of-day additions were post-processing checks from the completed SUEWS outputs and did not rerun SUEWS.
| Tool | Why It Was Called | Short Result |
|---|---|---|
assess_readiness | To confirm the manifest, official UDA-city configuration, forcing files, socio-economic sidecars, and runtime before modelling. | Passed. The agent found UDA-city, ten neighbourhoods, and SuPy 2026.6.5. |
validate_config | To validate the canonical configuration and locked physics before allowing any SUEWS run. | Passed. The configuration validated for ten grid cells with NARP radiation, classic OHM, and QF off. |
run_suews_present | To run the official present hot-humid SUEWS scenario for all ten neighbourhoods. | Completed for ten sites and 26,208 hourly steps per site. Results were saved as present SUEWS outputs. |
run_suews_future | To run the official +2.5°C humidity-preserving future stress test for all ten neighbourhoods. | Completed for ten sites and 26,208 hourly steps per site. Results were saved as future SUEWS outputs. |
apply_risk_bridge_present | To translate present dangerous-heat hours into hazard, exposure, vulnerability, and risk ranks. | The present bridge identified Kampong Lama as the highest-risk neighbourhood. |
apply_risk_bridge_future | To translate future dangerous-heat hours into hazard, exposure, vulnerability, and risk ranks. | The future bridge again identified Kampong Lama as the highest-risk neighbourhood. |
combine_hazard_risk_results | To compare the hottest neighbourhoods with the highest-risk neighbourhoods across both scenarios. | The combined table showed that hottest and highest-risk are not the same; Spearman rank correlation was weak. |
SUEWS Citation and Version Information
Model/runtime used: supy 2026.6.5. Cite SUEWS following the official guidance. The SUEWS-style colour palette and logo concept are credited to the SUEWS project.
- Järvi, 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. https://doi.org/10.1016/j.jhydrol.2011.10.001.
- Ward, H. C., Kotthaus, S., Järvi, 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. https://doi.org/10.1016/j.uclim.2016.05.001.
- SUEWS citation guidance: https://docs.suews.io/stable/#how-to-cite-suews.
How to cite this analysis: Dingal, Farrah Jasmine (2026). Final SUEWS Community Hackathon Submission: UDA-city Heat Hazard and Socio-Economic Risk.
Licence statement: This repository is prepared for hackathon review. No separate open-source licence file is bundled on this page; please cite this analysis, the UDA-city dataset, and SUEWS before reuse, and check the repository for any later licence update.
Version stamp: final submission page last updated 24 June 2026.