Volunteered Geographic Information (VGI) has been used to complement or substitute authoritative data in flood management domain. The main issue regarding the use of volunteered information is to estimate its quality, mainly because it may suffer from heterogeneous quality.
Therefore, several methods have been developed in the past few years in order to assess VGI quality. However, existing works lack in assessing VGI quality for the purpose of flood management. To overcome this gap, we propose a method for assessing the quality of VGI for this purpose. This method uses a set of quality metrics that were developedfor measuring VGI plausibility.
A multiple linear regression was carried out in order to demonstrate the relationship between VGI plausibility and the quality metrics. The results showed that plausibility can be explained by different quality metrics.
As a result, we demonstrated that 2 metrics are statistically significant to our model and have a strong linear relationship. The metrics temporal difference to a known event and detection in another information source are the mainly metrics that can predict the plausibility of VGI since both metrics are statistically significant in the main and alternative models.
Thus, the proposed method is able to estimate the plausibility of VGI in flood management domain.
Castro Degrossi, L.; Porto de Albuquerque, J.; Restrepo-Estrada, C.E.; Mobasheri, A.; Zipf, A. (2017). Exploring the geographical context for quality assessment of VGI in flood management domain. In Proceedings of the 23rd Americas Conference on Information Systems, Boston, MA, USA.
http://www.geog.uni-heidelberg.de/gis/publikationen_conference.html
Related earlier work:
Steiger, E.; Porto de Albuquerque, J.; Zipf, A. (2015): An advanced systematic literature review on spatiotemporal analyses of Twitter data. Transactions in GIS, 19(6): 809–834. Wiley. doi:10.1111/tgis.12132
Porto de Albuquerque, J., B. Herfort, A. Brenning, A. Zipf (2015): A Geographic Approach for Combining Social Media and Authoritative Data towards Improving Information Extraction for Disaster Management: A Study on the Twitter usage in the River Elbe Flood of June 2013. International Journal of Geographical Information Science, 29(4): 667-689. Taylor & Francis. DOI: 10.1080/13658816.2014.996567.