Analyzing Spatio-Temporal Patterns and Their Evolution via Sequence Alignment

Abstract

Temporal patterns indicate consistency and change, providing insight into social processes and phenomena. This article contributes to understanding patterns in social science by confirming the existence of known patterns under new conditions and quantifying the amount of observed deviation. We introduce a technique for matching a pattern in real-world events using an extension to the sequence alignment algorithm developed in biology. We demonstrate our algorithm and its utility for social science applications using event data collected from RSS news feeds. By comparing patterns derived from events in Yemen during the Arab Spring of 2011–2012 to events in Yemen's history and to other countries during the same time period, this algorithm contributes to time geographic concepts and comparative political research.