Overview

When conflict, disaster, or sudden crisis displaces thousands of people, humanitarian response teams must estimate how many individuals are affected—even when official counts are unavailable. Population Explorer (PopEx) enables humanitarian analysts to estimate populations within known or suspected displacement zones quickly, combining high-resolution datasets like WorldPop and LandScan with boundary overlays or field-drawn polygons.

Displacement zones are often fluid: populations move, camps expand, and administrative boundaries no longer align with reality. PopEx offers a transparent, replicable way to generate baseline estimates that can be refined later with field validation, ensuring decisions are grounded in consistent data. These same workflows benefit the private sector as well, from estimating population risk along flight-path corridors, to identifying optimal locations for new healthcare facilities.

Scenario Example

Imagine an emergency coordination team assessing a new influx near Goma, DRC. Satellite imagery shows several informal clusters forming north of the city. Field teams draw approximate boundaries based on GPS points. Within seconds, PopEx calculates the total population living in and around those polygons—helping responders estimate the number of displaced people and host community members affected.

Step-by-Step: Estimating Population in Displacement Zones

1. In the PopEx interface, create a new folder named after the affected area (e.g., “North Goma Displacement”).

2. Use New → Create Item → Custom Polygon to sketch each visible camp or settlement boundary. Alternatively, import shapes from File → Import KML/KMZ if field teams have provided coordinates.

3. Open the Layers → Settings panel to select your dataset (WorldPop 2024+ or LandScan 2023) and confirm the data year matches your reporting period.

4. Open each polygon’s summary panel to review Population Total, Density, and Income metrics. Record these as preliminary displacement estimates.

5. If you have multiple clusters, group them under one folder and view the aggregated total.

6. Export the results via Export → Excel for inclusion in situation reports or inter-agency updates.

Interpreting the Results

Remember that gridded population datasets reflect pre-crisis populations. In displacement contexts, these serve as baselines—not exact counts. PopEx estimates help identify order-of-magnitude figures: whether 5,000 or 50,000 people are affected. Adjustments should later incorporate field registration, camp enumeration, or satellite-based density updates.

Best Practices

• Use the most recent dataset (WorldPop preferred for displacement analysis).

• Keep polygons tight to visible boundaries to avoid inflating population counts.

• Document all assumptions—date, source imagery, who drew the polygons—for transparency.

• Pair PopEx outputs with partner data (UNHCR, IOM, OCHA) when reconciling figures.

Example Applications

Verification

Compare PopEx estimates with field registration data once available. Where large discrepancies occur, check polygon boundaries and dataset years. For fast-moving crises, treat PopEx numbers as indicative, suitable for initial humanitarian planning but requiring validation as conditions evolve.

Next Steps

• Measuring Populations in Camps

• Calculating Affected Populations after Disasters

Need More Help?

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