Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Issue 4 (4th July 2018)

• Record Type: Journal Article
• Authors: Resch, Bernd; Usländer, Florian; Havas, Clemens
• Is Part Of: Cartography and geographic information science. Volume 45:Issue 4(2018)
• Page Start: 362
• Page End: 376
• Publication Date: 2018-07-04
• Subjects: Social media -- disaster management -- machine-learning -- semantic topic analysis -- spatiotemporal analysis; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2017.1356242
• Languages: English
• ISSNs: 1523-0406

Abstract

ABSTRACT: Current disaster management procedures to cope with human and economic losses and to manage a disaster's aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time.