Crowd-sorting: reducing bias in decision making through consensus generated crowdsourced spatial information

2 hours
Crowdsourced Spatial InformationProvenanceCyclingPlanning
The crowd is uniquely placed to provide a valuable insight into their world. However, public perspective is often not taken into consideration early enough in the planning process. This is the result of a reliance on data and expert driven systems as well as the difficulty posed by the inherent diversity of crowdsourced spatial information. This paper seeks to address the bias expert oriented planning induces by presenting an approach that enables practical insights from the crowd to be included earlier in the planning process. With a focus on the performance of cycle infrastructure, Sensibel is introduced as a mechanism for obtaining crowdsourced spatial data and information. Crowd-sorting is achieved by using the information’s underlying spatial attributes in conjunction with its provenance to produce spatially defined local perspectives. This is achieved by examining how the crowd interacts with both the information and the area it describes.
University of Canterbury

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