Swarming in the Urban Web Space to Discover the Optimal Region

People moving to a new place usually look for a suitable region with respect to their multiple criteria of interests. In this work we map this problem to the migration behavior of other species such as swarming, which is a collective behavior exhibited by animals of similar size which aggregate together, milling about the same region. Taking the swarm intelligence perspective, we present a novel method to find relevant geographic region for citizens based on Particle Swarm Optimization (PSO) framework. Particles represent geographic regions which are moving in the map space to find a region most relevant with respect to user´s query. The characterization of geographic regions is based on the multi-criteria distribution of geo-located facilities or landscape structure from the Open Street Map data source. We enable end users to visualize and evaluate the regional search process of PSO via a Web interface. The proposed framework demonstrates high precision and computationally efficient performance for regional search over a vast city based dataset.