Building Geospatial Data Collections with Location-Based Games

Abstract

The traditional, expert-based process of knowledge acquisition is known to be both slow and costly. With the advent of the Web 2.0, community-based approaches have appeared. These promise a similar or even higher level of information quantity by using the collaborative work of voluntary contributors. Yet, the community-driven approach yields new problems on its own, most prominently contributor motivation and data quality. Our former work [1] has shown, that the issue of contributor motivation can be solved by embedding the data collection activity into a gaming scenario. Additionally, good games are designed to be replayable and thus well suited to generate redundant datasets. In this paper we propose semantic view area clustering as a novel approach to aggregate semantically tagged objects to achieve a higher overall data quality. We also introduce the concept of semantic barriers as a method to account for interaction betwen spatial and semantic data. We also successfully evaluate our algorithm against a traditional clustering method.