Exploring Descriptions of Movement Through Geovisual Analytics

TL;DR

This paper introduces GeoMovement, a system that combines machine learning, rule-based extraction, and visualization to analyze and understand descriptions of movement and lack of movement in text, aiding sensemaking of geographic phenomena.

Contribution

It presents a novel integrated framework for extracting and visualizing movement-related information, including negation, from text, which is a rare capability in geographic information analysis.

Findings

GeoMovement effectively extracts movement and lack of movement information.

The system aids in understanding migration, travel impediments, and other movement phenomena.

Case studies demonstrate its usefulness in real-world scenarios.

Abstract

Sensemaking using automatically extracted information from text is a challenging problem. In this paper, we address a specific type of information extraction, namely extracting information related to descriptions of movement. Aggregating and understanding information related to descriptions of movement and lack of movement specified in text can lead to an improved understanding and sensemaking of movement phenomena of various types, e.g., migration of people and animals, impediments to travel due to COVID-19, etc. We present GeoMovement, a system that is based on combining machine learning and rule-based extraction of movement-related information with state-of-the-art visualization techniques. Along with the depiction of movement, our tool can extract and present a lack of movement. Very little prior work exists on automatically extracting descriptions of movement, especially negation and movement. Apart from addressing these, GeoMovement also provides a novel integrated framework for combining these extraction modules with visualization. We include two systematic case studies of GeoMovement that show how humans can derive meaningful geographic movement information. GeoMovement can complement precise movement data, e.g., obtained using sensors, or be used by itself when precise data is unavailable.