Fueled by the increasing proliferation of citizen generated spatio-temporal data -- especially in participatory urban infrastructure monitoring -- municipal authorities are in need for ways to process and understand increasingly overwhelming amounts of data. However, duplicate issue reporting by citizens such as broken traffic lights, potholes or garbage can lead to bottlenecks in manual processing of such data. As contribution this paper examines which city issue report presentation methods are useful to support a human in analyzing and processing them. We compare presentation methods such as automatically clustered information, manual clustered information and mixes of both. Automatically clustering of information is performed by a data analytics algorithm which is also presented in this paper together with EstaVis, a prototype of an interactive visual urban analytics platform. Evaluation studies with 282 crowd-workers show how the platform can potentially help to speed-up report processing by detecting and aggregating duplicate reports by up to 3 orders of magnitude and discuss which lessons can be learned in terms of features and user experience pitfalls for this kind of system.
['Julio De Melo Borges', 'Matthias Budde', 'Oleg Peters', 'Till Riedel', 'Andrea Schankin', 'Michael Beigl']
EstaVis: A Real-World Interactive Platform for Crowdsourced Visual Urban Analytics