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

TL;DR

This paper introduces a novel methodological framework for estimating population displacement from mobile phone data, incorporating mobility profiles and context-aware detection to improve accuracy for humanitarian use.

Contribution

It presents a new framework that distinguishes local residents from commuters and accounts for expected mobility patterns, enhancing displacement detection accuracy.

Findings

Context-aware detection reduced displacement estimates by 1.6-2.7 percentage points.

The framework produces displacement rates, origin-destination flows, and return dynamics with uncertainty bounds.

Proof of concept demonstrated on case study following Typhoon Nando in the Philippines.

Abstract

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.