A New Algorithm for Continuous Area Cartogram Construction with Triangulation of Regions and Restriction on Bearing Changes of Edges

Collective intelligence is the idea that under the right circumstances collections of individuals are smarter than even the smartest individuals in the group, that is a group has an “intelligence” that is independent of the intelligence of its members. The ideology of collective intelligence undergirds much of the enthusiasm about the use of “volunteered” or crowd-sourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this article identifies four pre-conditions for the emergence of collective intelligence and then examines the extent to which collectively generated mapping systems satisfy these conditions. However, the “intelligence” collectively generated maps is hard to assess because there are two difficult to reconcile perspectives on map quality – the credibility perspective and the accuracy perspective. Much of the current literature on user-generated maps focuses on assessing the quality of individual contributions. However, because user-generated maps are complex social systems and because the quality of a contribution is difficult to assess this strategy may not yield an “intelligent” end product. The existing literature on collective intelligence suggests that the structure of groups is more important than the intelligence of group members. Applying this idea to user-generated maps suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The article concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs.