Spatial Collective Intelligence? credibility, accuracy, and Volunteered Geographic Information

Area of Science:

• Geographic Information Science
• Social Computing
• Collective Intelligence Studies

Background:

• Collective intelligence posits that groups can be smarter than individuals under specific conditions.
• This concept underpins the enthusiasm for crowdsourced geographic information (CGI).
• Not all groups exhibit collective intelligence; preconditions are necessary for its emergence.

Purpose of the Study:

• To identify preconditions for collective intelligence.
• To examine how collectively generated mapping systems meet these preconditions.
• To address challenges in assessing the quality of crowdsourced maps from credibility and accuracy perspectives.

Main Methods:

• Analysis of collective intelligence literature to define preconditions.
• Evaluation of existing collectively generated mapping systems against these preconditions.
• Review of literature on assessing user-generated map quality.

Main Results:

• Identified four key preconditions for collective intelligence.
• Found that collectively generated mapping systems' 'intelligence' is difficult to assess due to conflicting quality perspectives (credibility vs. accuracy).
• Emphasized that group structure is more critical than individual member intelligence for collective intelligence.

Conclusions:

• User-generated map quality assessment should focus on system design fostering collective intelligence, not solely on individual contributions.
• Design recommendations for collectively generated mapping systems are provided.
• Discussed implications for expert knowledge and traditional mapping programs.