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The emergence of Web 2.0 and ubiquitous mobile platforms makes it possible to collect a vast amount of information contributed by people (VGI). For example, crowdsourcing applications collect information from domains such as biodiversity, urban planning, and risk management, and other sources such as social media connect citizens that exchange voluntarily huge amount of posts on platforms like Twitter, Flickr, and Facebook. VGI differs from data coming from sensors, simulations, and mathematical models. It is highly dependent on the human wills to share the information, and the background and knowledge of the user, which introduces uncertainy. In this paper, we explore different dimensions of VGI uncertainty from the perspective of the human that contributes with the data, as well as the technology and systems used to collect the data. Our contributions include a new taxonomy that explicitly differentiates among the uncertainty introduced by the humans, we named User-Uncertainty and analyzed it at different steps of the Visual Analytics Workflow, and several use cases that illustrate our approach for the case of User-Uncertainty coming from the producers. We conclude our paper with a discussion about the potential uses and future work to be done to understand User-Uncertainty.

Understanding traffic events is important for urban policy making and transport management. Traffic events could be related to traffic congestion, transportation infrastructure issues, parking issues, to name a few. Currently, traffic events are monitored through static sensors e.g., CCTV camera, loop detectors which have limited spatial coverage and high main- tenance cost. Thus, we attempt to use the concept of citizens as sensors and develop a cost-effective model to understand urban traffic events from unstructured and informal tweets. So far existing works attempted to classify tweets either in traffic or non-traffic categorization [1], [3], [4]. Most of the state-of- the-art have used geotagged tweets for identifying traffic events [2], which accounted for only 1%-3% total tweet population, and thus lots of useful information in the ungeotagged tweets may be lost. Some other works explored a number of abstract topics related to urban transportation and environment, however without retrieving any spatial information from the tweet [5], [6]. The main contribution of this work is, in contrast to the earlier works, this research explores ungeotagged tweets to detect traffic events and developed a novel framework (Fig. 1, 2) that does not only categorize traffic related tweets but also retrieve locations of the traffic events from the tweet content. The model has been tested in the city of Mumbai in India where people use different local place names which are often informal and hard to detect using a traditional named entity recognition systems. To detect the locations of the traffic events we developed a hybrid georeferencing model that consists of a supervised model and a number of spatial rules that can handle informal place names and vernacular geographical aspects. For tweet categorization we used a binary classifier based on Decision Tree (DT) with 0.65 precision and 0.57 recall. The tweets are manually labelled into either traffic or non-traffic. Then the cl