Crowd sourcing frameworks, such as Ushahidi are based on the internet community, which analyses manually thousands of on-site disaster reports. This information supports non-governmental organizations in coordinating their operations based on current information and situation maps, so-called crowdmaps. At the same time this scattered collected on-site information allows a detailed reconstruction of the disaster event. Accordingly crowd-sourced disaster information is essential for a forensic analysis. Manual analysis and preparation of the crowd-maps is currently done by a anonymous internet community, meaning we have no information about their reliability and accuracy. For a robust forensic disaster analysis, such an evaluation is absolutely necessary. So the aim of this project is to develop methods and algorithms that process automatically the spatial information gathered from different sources and evaluate its quality and reliability. A global approach is seen in a separate modeling of functional and semantic aspects. Thereby, specific spatial relationships are modeled individually and linked functionally to a spatial scene. This way both the embedded spatial context and the global context are taken into account.
The FDA-project "crowd sourcing in disaster management" covers a fundamental component of the process chain of a forensic disaster analysis. On the one hand is a direct link to the FDA-project "Social sensor for rapid (damage) assessment in disaster management". This project investigates which kind of information is required for an FDA and which platforms provide it. On the other hand the results of our project provide the information for other FDA-projects such as „Methodik zur (schnellen) Abschätzung der ökonomischen Auswirkungen von Naturkatastrophen in Industrieunternehmen“ and „Schnelleinschätzungen der Auswirkungen von Naturereignissen auf den Verkehr und deren volkswirtschaftlicher Bewertung“.
|Pic. 1: Textdataformat, structured as XML
|Pic. 2: Screenshot of local event based on three descriptions in urban disastermanagement
|Pic. 3: Spatial estimation function
|Pic. 4: Prototy seneca for processing textual messages