The Global Centre for Risk and Innovation (GCRI)
Humanitarian risk is the chance that a crisis will overwhelm people's ability to cope and require outside assistance. It combines hazards and conflict, people's vulnerability and the capacity of institutions to respond. Assessing it helps agencies prepare and act early. This page sets out how humanitarian risk is analysed, how needs are estimated, and what evidence serves the humanitarian and development actors who use it. It names no third-party risk index.
GCRI · In development · service level R0
Humanitarian risk is the likelihood that a crisis will overwhelm people's capacity to cope and require outside assistance. It arises where hazards or conflict meet vulnerable people and weak response capacity, crises often compound, and early analysis lets agencies act before needs peak.
For humanitarian risk, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and land cover, framed on Latin America and the Caribbean; every other NASA layer, live feed and forecast waits in the layer list.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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For humanitarian risk, in GCRI's spatial-intelligence reading, the map opens on population density, true-colour imagery and land cover, framed on Latin America and the Caribbean; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
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The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Humanitarian and displacement analysis under safeguards. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Humanitarian need arises where hazards or conflict meet vulnerable people and weak response capacity, so all three must be assessed together.
Conflict, economic collapse, disease and disasters often occur together, with each making the others harder to address.
Analysis of risk before crises peak lets agencies preposition supplies and act early, which costs less than late response.
Estimates of people in need drive appeals and allocations, so their methods and assumptions matter greatly.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| People in need | The estimated number of people whose wellbeing is threatened and who require humanitarian assistance. |
| Complex emergency | A crisis combining conflict, breakdown of authority and large-scale humanitarian need. |
| IPC phase | A level on the five-phase scale of acute food insecurity severity. |
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 |
|---|---|---|---|---|
| People in need of humanitarian assistance | count | annual | Humanitarian needs overviews | ESTIMATED |
| Population in acute food insecurity | people by IPC phase | per analysis | Integrated Food Security Phase Classification | ESTIMATED |
| Humanitarian appeal funding coverage | percent funded | annual | OCHA Financial Tracking Service | REPORTED |
| Conflict events | count | weekly | Conflict event data | REPORTED |
| Anticipatory action triggers activated | count | per season | Agency records | REPORTED |
| Access constraints | count of reported incidents | monthly | Humanitarian access monitoring | REPORTED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
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 |
|---|---|---|
| OCHA Global Humanitarian Overview and Humanitarian Needs Overviews | People in need and appeal requirements | Public, UN terms of use |
| Integrated Food Security Phase Classification analyses | Acute food insecurity classifications | Public, IPC terms |
| ACLED conflict event data | Political violence and protest events | Free with registration, ACLED terms |
| OCHA Humanitarian Data Exchange | Shared humanitarian datasets | Open, licence per dataset |
| FEWS NET food security outlooks | Food security projections | Public domain, US government (to check current status) |
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 |
|---|---|---|
| Humanitarian coordinators and agencies | Where to prepare and prioritise assistance | Risk analyses; needs estimates |
| Donors | How to allocate humanitarian and anticipatory funding | Funding and risk evidence |
| National disaster and social protection authorities | How to link national systems with humanitarian response | Early action evidence |
Humanitarian risk models typically combine three dimensions: hazard and exposure, including conflict; vulnerability of the population; and lack of coping capacity, including weak institutions and infrastructure. Indicators are aggregated into indices that rank or classify areas. Forecasting of food insecurity, displacement or disease outbreaks uses climate forecasts, market prices, conflict data and seasonal patterns to give warning before needs peak.
Anticipatory approaches link forecasts to pre-agreed actions, aiming to act before a shock becomes a crisis. Their value depends on forecast skill, the time needed to act and the cost of acting when the event does not occur. Conflict-driven crises are harder to anticipate than seasonal hazards, and compound crises challenge models built for single shocks.
Verification closes the loop. Comparing past forecasts with what actually happened shows whether indicators and models deserve trust, which thresholds work and where biases lie. Publishing that verification, including failures, is part of responsible humanitarian analysis.
Indices summarize risk across many indicators; weighting choices and data gaps shape results.
Statistical and machine-learning forecasts of need; skill varies and must be verified.
Aggregation, access controls and review prevent data from identifying or targeting people.
Limits: indices can mask local drivers, models trained on past crises may miss new patterns, and false alarms and missed events both carry costs. Data about locations of vulnerable groups can be dangerous in conflict settings.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.