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Example 1: Aggregated SP Data Exchange for Risk Assessment

Process Flow

This example illustrates how the PRS.EWS.02: SP system share aggregated data for analysis process can be implemented. It describes how Social Protection (SP) systems provide aggregated vulnerability and beneficiary data to an Impact-Based Early Warning System (IB-EWS). The purpose is to enable the IB-EWS to combine hazard forecasts with exposure and vulnerability data in order to generate people-centred impact forecasts, risk analytics, and early warning products to support anticipatory action planning.

The SP system does not trigger any interventions through this process. It functions as a controlled data provider, while the IB-EWS is responsible for integrating the data into hazard models, producing impact-based risk outputs, and supporting decision-making by disaster risk management (DRM) and social protection authorities.

Process Flow: Sharing Data Aggregated to EWS for analysis assessment

Section
Description

Actors and Entities

Early Warning System for (EWS), Social Protection (SP) system

Assumptions

  • Formal data-sharing agreement between SP system and EWS institution.

  • Defined EWS framework linking hazard, exposure, and vulnerability components.

  • Standardized geographic reference system (admin boundaries, grids, or hazard zones).

  • Agreed vulnerability indicators and aggregation rules from SP system.

  • Secure interoperability mechanism (API, secure transfer, or scheduled data pipeline).

  • Data protection and anonymization rules approved and operational.

Prerequisites

  • The Social Protection system has operational access to the required databases for aggregation.

  • Aggregated data (counts, summaries, or indicators) is available in the system for extraction.

  • The SP system has the technical capability to process and transmit data in the agreed formats.

Process Inputs

Process Flow Steps

Step 1:

  • EWS initiates request for SP aggregated vulnerability data OR receives scheduled data feed.

  • Request specifies:

    • Geographic resolution

    • Time period

    • Required indicators (e.g., vulnerable households, poverty proxy, disability, livelihood type)

Step 2:

  • SP system extracts relevant beneficiary and vulnerability datasets.

  • Data is aggregated by agreed spatial and demographic dimensions.

  • Personally identifiable information (PII) is removed.

Step 3:

  • SP system performs:

    • Completeness checks

    • Threshold validation (minimum cell counts for anonymization)

    • Consistency checks with registry data

Step 4:

  • EWS ingests SP data into vulnerability layer.

  • Data is aligned with:

    • Hazard forecasts (meteorological inputs)

    • Exposure datasets (population, infrastructure, land use)

Step 5:

EWS runs analytical models combining:

  • Hazard intensity and probability

  • Exposure distribution

  • Vulnerability (SP aggregated data)

Outputs

  • EWS generates:

    • Impact-based early warning bulletins

    • Risk maps and dashboards

    • Scenario simulations for decision support

  • Outputs are shared with DRM and relevant agencies.

Control Points

  • Data Governance Approval

    • Ensures compliance with legal and ethical data-sharing frameworks.

  • Anonymization & Aggregation Control

    • Verifies no PII is included and minimum aggregation thresholds are respected.

  • Data Quality Assurance

    • Validates accuracy, completeness, and consistency before transfer.

  • Interoperability Validation

    • Ensures geographic and indicator alignment between SP and IB-EWS systems.

  • Access Control & Security

    • Ensures only authorized systems and users can access shared datasets.

  • Model Integrity Check (EWS)

    • Ensures SP data is correctly integrated into risk models without distortion or duplication.

Exception Handling

Incomplete or Missing SP Data

  • Trigger: Missing geographic coverage or incomplete registry data.

  • Action:

    • EWS uses fallback vulnerability datasets (census, proxies).

    • SP system flags gaps for data improvement cycle.

Data Quality Failure

  • Trigger: Validation errors (inconsistencies, outdated records).

  • Action:

    • Data is rejected and returned to SP system for correction.

    • No ingestion into EWS occurs until resolved.

Geographic Mismatch

  • Trigger: Misaligned administrative boundaries or coding systems.

  • Action:

    • Mapping layer applied OR data held until reconciliation is completed.

Diagram 1 - Aggregated SP Data Exchange for Risk Analysis

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