Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data

TL;DR

This paper presents a system for detecting unusual human behavior patterns using mobile phone data, successfully identifying a range of emergency and non-emergency events in Rwanda, with implications for real-time disaster response.

Contribution

It introduces an efficient spatiotemporal anomaly detection system that captures complex behavioral responses to diverse events, advancing real-time emergency detection methods.

Findings

Behavioral anomalies correlate with various events including holidays, earthquakes, and protests.

Responses to extreme events show significant temporal and spatial variability.

The system effectively distinguishes between emergency and non-emergency behavioral changes.

Abstract

With the aim to contribute to humanitarian response to disasters and violent events, scientists have proposed the development of analytical tools that could identify emergency events in real-time, using mobile phone data. The assumption is that dramatic and discrete changes in behavior, measured with mobile phone data, will indicate extreme events. In this study, we propose an efficient system for spatiotemporal detection of behavioral anomalies from mobile phone data and compare sites with behavioral anomalies to an extensive database of emergency and non-emergency events in Rwanda. Our methodology successfully captures anomalous behavioral patterns associated with a broad range of events, from religious and official holidays to earthquakes, floods, violence against civilians and protests. Our results suggest that human behavioral responses to extreme events are complex and multi-dimensional, including extreme increases and decreases in both calling and movement behaviors. We also find significant temporal and spatial variance in responses to extreme events. Our behavioral anomaly detection system and extensive discussion of results are a significant contribution to the long-term project of creating an effective real-time event detection system with mobile phone data and we discuss the implications of our findings for future research to this end. KEYWORDS: Big data, call detail record, emergency events, human mobility