Resource management mechanisms for large-scale, globally distributed network services need to assign groups of clients to servers according to network location and expected load generated by these clients. Current proposals address network location and traffic modeling separately. In this paper, we develop a novel clustering technique that addresses both network proximity and traffic modeling. Our approach combines techniques from network-aware clustering, location inference, and spatial analysis. We conduct a large, measurement-based study to identify and evaluate Web traffic clusters. Our study links millions of Web transactions collected from two world-wide sporting event websites, with millions of network delay measurements to thousands of Internet address clusters. Because our techniques are equally applicable to other traffic types, we expect they will be useful in a variety of wide-area distributed computing optimizations, and Internet modeling and simulation scenarios.