Network operators need to assess the effects of routing policies and traffic engineering methods in order to guide planning and operational decisions related to peering. We propose a peering analysis framework based on the correlation of inferred AS paths and traffic flow information. We define the data sets needed and a four-step methodology designed to reduce network data dimensionality, determine traffic proximity, identify traffic propensity, and quantify the impact of traffic locality. We demonstrate how the correlation of traffic flow and AS paths, and the application of our four-step approach, uncovers rich information when applied to a real-world case study of a local Internet service provider.