# Exploring Descriptions of Movement Through Geovisual Analytics

arXiv: https://arxiv.org/abs/2204.09588 · HTML: https://mlsanity.com/paper/2204.09588/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 110 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [A Survey of Visual Analytics Techniques for Machine Learning](https://mlsanity.com/paper/2008.09632/) (2020)
- [NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution](https://mlsanity.com/paper/1911.04211/) (2019)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [Pragmatic factors in image description: the case of negations](https://mlsanity.com/paper/1606.06164/) (2016)
- [A Natural-language-based Visual Query Approach of Uncertain Human Trajectories](https://mlsanity.com/paper/1908.00277/) (2019)
- [Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness](https://mlsanity.com/paper/1908.02588/) (2019)
- [Extracting and Analyzing Semantic Relatedness between Cities Using News Articles](https://mlsanity.com/paper/1809.02823/) (2018)
- [Deck.gl: Large-scale Web-based Visual Analytics Made Easy](https://mlsanity.com/paper/1910.08865/) (2019)
- [Geovisual Analytics and Interactive Machine Learning for Situational Awareness](https://mlsanity.com/paper/1910.05441/) (2019)
- [Differentiating Geographic Movement Described in Text Documents](https://mlsanity.com/paper/2201.04427/) (2022)
- [Recognition of Implicit Geographic Movement in Text](https://mlsanity.com/paper/2201.12799/) (2022)

## Abstract (arXiv)

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.