Context-Aware Displacement Estimation from Mobile Phone Data: A Methodological Framework

Timely population displacement estimates are critical for humanitarian response during disasters, but traditional surveys and field assessments are slow.

Mobile phone data enables near real-time tracking, yet existing approaches apply uniform displacement definitions regardless of individual mobility patterns, misclassifying regular commuters as displaced.

We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor, intended for humanitarian decision support rather than formal statistical inference.

The framework produces three complementary metrics scaled to population with uncertainty bounds: displacement rates, origin-destination flows, and return dynamics.

An Aparri case study following Super Typhoon Nando (2025, Philippines) applies the framework to vendor-provided daily locations from Globe Telecom.

Context-aware detection reduced estimated between-municipality displacement by 1.6-2.7 percentage points on weekdays versus naive methods, attributable to the commuter exception but not independently validated.

The method captures between-municipality displacement only.

Within-municipality evacuation falls outside scope.

The single-case demonstration establishes proof of concept.

External validity requires application across multiple events and locations.

The framework provides humanitarian actors with operational displacement information while preserving individual privacy through aggregation.