The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Download
Citation
Articles | Volume XLI-B6
https://doi.org/10.5194/isprs-archives-XLI-B6-13-2016
https://doi.org/10.5194/isprs-archives-XLI-B6-13-2016
17 Jun 2016
 | 17 Jun 2016

FRAMEWORK SEE-THINK-DO AS A TOOL FOR CROWDSOURCING SUPPORT – CASE STUDY ON CRISIS MANAGEMENT

R. Netek and J. Panek

Keywords: Crowdsourcing, Collaboration, Awareness, See-Think-Do, Crisis map

Abstract. See-Think-Do is a framework originally used as an approach focused on a service and product marketing on the Internet. Customers can be classified into three groups according to their involvement from potential users to real customers.

The article presents an idea of public involvement in community mapping in three levels: “See”—almost any user; “Think”—potential contributors; and “Do”—interested users. The case study implements the See-Think-Do framework as an awareness-based approach used for The Crisis Map of the Czech Republic. It is an Ushahidi-based crowdsourcing platform for sharing spatial and multimedia information during crisis situations, e.g. disaster floods in 2013. While the current crisis projects use public mapping just at the onset of the disaster, according to See-Think-Do any user can be considered as a potential contributor even during the dormant period. The focus is put on the "See" and "Think" groups of contributors, which are currently ignored.

The objective of this paper is to summarize approaches (social networks, mass-media, emailing, gamification, …) and tools (GIT/GIS, ICT, multimedia) for increasing the awareness about the project within the resting phase. That recruits a higher number of both active and passive users during the disaster. It allows the training in ICT, cartographical, spatial and GIS skills in a non-stressful way and the targeting on specific operators. Volunteers from the "Think" group may be used for data processing or rectification, GIS professionals from the "Do" group for data verification. The results refer that contributors with already established skills and required literacy (interface, data uploading) provide data faster and more accurate, the usability of the project increases based on users' comments.