Although our system had been running successfully for quite a while behind high walls, it now finally became available online:
Please note that
a) it’s still beta
b) currently, there are few forest fires in Europe, so you will likely see a higher ratio of noise
c) we haven’t put the clustering module online yet, because it still requires frequent human supervision to adjust parameters
If you’d like to know more, don’t hesistate to contact us!
Here’s some more info on the idea and technology behind it:
The rationale for the research is the emergence of new social media platforms that change the way people create and use information during crisis events. Most wide spread are platforms for micro blogging (e.g. Twitter), networking (e.g. Facebook), and photo sharing (e.g. Flickr). These social media platforms increasingly offer to include geographic information on the whereabouts of their users or the information posted. Potentially, this rich information can contribute to a more effective response to natural disasters. In fact, social media have been put into good use on various occasions. This increasing amount of bi-directional horizontal peer-to-peer information exchange also affects the traditional uni-directional vertical flow of information. Traditional broadcasting media open up to micro journalism and several official administrative agencies already adapt and use third-party social media accounts for communicating information. However, incorporation of UGC into the established administrative emergency protocols has not advanced significantly. It seems that public officials view such volunteered information often as a threat that could spread misinformation and rumours, as long as there is no reliable quality control.
So far, mainly human volunteers have carried out the tasks of filtering, validating and assessing the quality of UGC, and with great success. However, this approach is not sustainable and scalable for a continuous, reliable utilization of UGC in crisis response, because the amount of data is ever increasing, and volunteers might not be available in sufficient numbers. The research community has already begun to investigate in assessing trust, reputation and credibility of UGC and volunteered geographic information (VGI) in particular, but several issues pose enormous challenges to automated approaches: Among them a lack of unified interface and heterogeneous media formats and platforms lead to a wide variety of possible data structures, a lack of syntactical control over the data entered by the users, the ingenuity of users and software developers able to overcome device or interface limitations, and an unknown and variable proportion of disruptive or redundant content.
We propose that an integration with existing spatial data infrastructures (SDI) and the geographic contextualization of geo-coded UGC (UGGC) can greatly enhance the options for assessing its quality. We call this approach the GEOgraphic CONtext Analysis of Volunteered Information (GEOCONAVI). This approach emulates one of the basic heuristics which humans use to deal with information that has unknown quality: A comparison with “What do I already know?” By spatio-temporally clustering UGGC, we emulate another heuristic, that of social confirmation (“What do others say?”), and look for confirming or contradicting content. Both these heuristics influence the credibility assessment of the UGGC or VGI. Another criterion is that of relevance, which we assess from the quasi-objective point of view of the potential damage a forest fire can cause, by investigating again the geographic context.
To recapitulate, the GEOCONAVI system requires the following tasks to be carried out semi-automatically or automatically: First, the retrieval and storage of UGC or VGI from various sources. Second, the enrichment of the retrieved UGC with information about source, content, location, and geographic context turning it into UGGC or VGI. Third, the clustering of the UGGC in space and time. Fourth, the detection of new events, or the assignment to known events. Fifth, the dissemination of the results.
The following figure shows an overview of the workflow, plus the current implementation.
Even more info at: