GCRI · Society and Human Development
Urban social vulnerability
In cities, hazards strike people unequally. Low-income residents, renters, migrants, older people, people with disabilities and those in informal settlements often face more exposure and have fewer resources to cope and recover. This page sets out how social vulnerability arises in cities, how it is measured and mapped, and what evidence serves the city governments and agencies that plan for it.
Urban social vulnerability on the record.
Urban social vulnerability: how the risk develops.
Community resilience and informal services. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Exposure follows affordability
Cheaper housing is often in flood zones, near industry or in hotter neighbourhoods, so low-income residents face more hazard.
Coping capacity differs
Savings, insurance, social networks, mobility and language skills shape how people prepare for and recover from shocks.
Some groups are invisible
Undocumented migrants, homeless people and informal workers may be missed by registers and warnings and avoid help from the authorities.
Recovery widens gaps
Owners with insurance rebuild while renters are displaced, so recovery can deepen inequality in a city.
Urban signals in Latin America and the Caribbean.
Population density, night lights and daytime surface heat over Latin America and the Caribbean, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Urban signals, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- OMI Aura (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-10
- Resolution
- about 2 km grid
- Uncertainty
- Satellite column of nitrogen dioxide in the lower atmosphere on the day shown, mostly from combustion; not a ground-level concentration or a health warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Urban signals, framed on Latin America and the Caribbean.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- OMI nitrogen dioxide (Aura) and OMPS aerosol index (Suomi NPP), NASA; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (16 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Nitrogen dioxide, lower atmosphere column: OMI Aura, NASA GES DISC · 10 Oct 2026
Urban vulnerability concentrates where services are weakest.
Findings are designed for the Country Systems Risk and Resilience series. UN-Habitat urban indicators provide the frame.
- Neighbourhood data with consent.
- Local vulnerability studies.
Humanitarian needs overview.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Population in hazard zones by income group | people | per assessment | Census and hazard overlays | ESTIMATED |
| Households without insurance | percent of households | per survey | Household surveys | REPORTED |
| Older people living alone | count | per census | Census | Published statistics |
| Renters as share of households | percent | per census | Census | Published statistics |
| Language barriers to warnings | percent of residents with limited national language | per census | Census | Published statistics |
| Homelessness | people counted by a stated definition | per count | Administrative counts | REPORTED |
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows Latin America and the Caribbean; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 11%
- Assets still working
- 17%
- Amplification of the initial damage
- 7.5 times
- Steps to settle
- 3
Numbers, methods and sources
| Step | Transport working (%) | Power working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 83 | 89 | 94 |
| 1 | 50 | 33 | 83 |
| 2 | 11 | 22 | 50 |
| 3 | 11 | 17 | 22 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Society and Human Development in Foundry
- Tested at Society and Human Development in Universe
- Codified in Governance, Security and Society in Standards
- Published in Society and Human Development in Reports
- Taught in Faculty of Health and Society in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| National censuses and small-area statistics | Population characteristics by neighbourhood | Licence per statistics office |
| CDC/ATSDR Social Vulnerability Index documentation | A published method for combining census variables, for method reference | Public domain, US government |
| Household surveys with urban samples | Assets, insurance and coping | Licence per survey |
| Local hazard maps | Flood, heat and other hazard zones | Licence per authority |
| UN-Habitat urban data | Urban inequality and housing indicators | Public, UN-Habitat terms (to check) |
Model urban social vulnerability as a system. Run the cascade.
Assembled from the Nexus Standards area profile Societal and information risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Civil unrest
- Supply-chain disruption (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H117Hazard family: Social cohesion and atrocity risk (NHS-1 soc.cohesion)NRS-X7Information integrity and public trustNRS-D2Society, inclusion and human development riskNRS-H52Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
Observation
- WorldPop gridded population (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Global SDG Indicators Database (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Proportion of people living below 50 per cent of median income
- Number of countries that adopt and implement guarantees for public access to information
Example geography
- The Horn of Africa: 253 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
Who decides, and what the evidence serves.
Society and Human Development prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| City governments | Where to target preparedness, outreach and recovery support | Vulnerability maps; equity reviews |
| Emergency managers | How to reach groups missed by standard warnings | Outreach gap analyses |
| Housing authorities | How to protect renters and low-income residents in recovery | Recovery equity evidence |
Outputs this topic produces
- Neighbourhood social vulnerability maps with uncertainty
- Equity reviews of emergency plans and recovery
- Outreach gap analyses for warnings and services
- Evidence notes for city resilience strategies
How urban social vulnerability is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in urban social vulnerability: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
The questions experts ask.
- Who lives in the most hazardous parts of a city, and why?
- How do informal services perform during shocks, and who relies on them?
- How does insecure tenure affect investment in safety and recovery?
- Which community institutions sustain resilience, and how can they be supported?
- How do heat, flooding and service failure interact in dense neighbourhoods?
Informality, density and exposure.
Informal settlements often occupy land left undeveloped because it is hazardous: floodplains, steep slopes, coastal margins or land near industrial sites. Dense housing with little green space intensifies urban heat, and poor drainage turns rain into flooding. Insecure tenure discourages households from investing in safer housing and can exclude them from formal recovery support.
Informal providers, such as water vendors, informal transport and savings groups, fill service gaps and can be flexible in crises, but they may also raise prices or fail when supply chains break. Community networks and organisations are central to everyday resilience, sharing information, resources and labour. Evictions and relocation after disasters can destroy these networks and livelihoods.
Urban heat illustrates how these factors combine. Dense, poorly ventilated housing, lack of shade and green space, outdoor work, unreliable power and limited access to cooling or health care mean that the same heatwave can be far more dangerous in an informal neighbourhood than in a planned district nearby.
Methods and limits.
Community mapping
Participatory and imagery-based mapping of settlements and services; government maps often omit informal areas.
Neighbourhood surveys
Household and community surveys of exposure, services and tenure.
Vulnerability indices
Spatial indices combine indicators; results depend on weighting choices.
Limits: informal areas are under-counted in censuses, vulnerability indices can hide the specific drivers that matter locally, and publishing detailed maps of settlements can expose residents to eviction.
How it is studied, and what the methods cannot say.
- 01Spatial statistics and geostatisticsSmall-area poverty estimation · Vulnerability and hazard overlayApplied here
- 02Social and behavioural scienceSurvey design for hard-to-reach groups · Qualitative and participatory researchApplied here
- 03Causal inference and impact evaluationEvaluating cash transfers · Effects of disasters on households · Education programme evaluationApplied here
- 04EconometricsPoverty dynamics with panel data · Price shock incidence · Inequality decompositionApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is social vulnerability?
How is social vulnerability mapped?
Why do some groups recover more slowly?
Does the platform publish maps of informal settlements?
Are informal services part of the analysis?
Does the platform work directly with communities?
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