  Connections created from a node-edge matrix have been traditionally difficult
to visualize and analyze because of the number of flows to be rendered in a
limited feature or cartographic space. Because analyzing connectivity patterns
is 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 create a Weighted Radial Variation (WRV) technique to classify a set
of nodes based on the configuration of their radially-emanating vector flows.
Each entity's vector is 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{\deg} 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 an unsupervised classification method that clusters entities
with similar signatures in order to provide a typology for each node in the
system of spatial flows. We use a case study of U.S. county-to-county human
incoming and outgoing migration data to test our method.
