Directional flows created from an origin/destination matrix have been traditionally difficult to visualize because of the number of flows to be rendered in a small cartographic space. Because visualizing geographic flow dynamics are useful for understanding the complex dynamics of human and information flow that connect non-adjacent space, techniques that allow for visual data mining or static representations of system dynamics are a growing field of research. Here, we use a Weighted Radial Variation (WRV) technique to classify places based on their group’s radially-emanating vector flows. Each entity’s vector are syncopated in terms of cardinality, direction, length, and flow magnitude. The WRV process unravels each star-like entity’s individual flow vectors on a 0-360° spectrum, to form a unique signal whose distribution depends on the flow presence at each step around the entity, and is further characterized by flow distance and magnitude. The signals are processed with a supervised classification method that clusters entities with similar signatures or trajectories in order to learn about types and geographic distribution of flow dynamics. We use U.S. county-to-county human incoming and outgoing migration data to test our method.
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