Characterizing the Swarm Movement on Map for Spatial Visualization
نویسندگان
چکیده
Visualization of maps to explore relevant geographic areas is one of the common practices in spatial decision scenarios. However visualizing geographic distribution with multidimensional criteria becomes a nontrivial setup in the conventional point based map space. In this work we present a novel method to generalize from point data to spatial distributions, captivating on the swarm intelligence. We exploit the particle swarm optimization (PSO) framework, where particles represent geographic regions that are moving in the map space to find better position with respect to user’s criteria. We track the swarm movement on map surface to generate a relevance heatmap, which could effectively support the spatial analysis task of end users.
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