Zur Seitenansicht
 

Titelaufnahme

Titel
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment
VerfasserResch, Bernd ; Usländer, Florian ; Havas, Clemens
Erschienen in
Cartography and Geographic Information Science, Abingdon, 2017, Jg. 2017, H. 0, S. 1-15
ErschienenTaylor & Francis, 2017
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
ISSN1545-0465
URNurn:nbn:at:at-ubs:3-7037 Persistent Identifier (URN)
DOI10.1080/15230406.2017.1356242 
Zugriffsbeschränkung
 Das Werk ist frei verfügbar
Dateien
Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment [2.74 mb]
Links
Nachweis
Klassifikation
Zusammenfassung (Englisch)

Current disaster management procedures to cope with human and economic losses and to manage a disasters 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.

Notiz
Lizenz
CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz