It is natural to think of geographic proximity as an indicator of how likely a city or region's pattern of influenza mortality mirrors that of another region. However, we hypothesize that the relatively high level of travel within the country will affect the pattern of mortality such that cities across the nation may resemble one another more closely than cities within a predefined geographic region. Our approach involved the creation of a discrepancy measure specifically designed for time series data and the application of a clustering routine that seeks to create high quality clusters (rather than high-inclusion clusters). The largest cluster held roughly a third of the observed cities and yet still had a low within-cluster distance when compared to the geographic census regions. This result suggests that many cities observe similar influenza and pneumonia mortality patterns despite varying geographical locations. There are several limitations to this study. First, while our discrepancy works well in the presence of missing data, a preponderance of consecutive missing time points can negatively affect performance. We determined that 25 cities out of the original 122 cities reported too sporadically for analysis. Furthermore, our clustering technique depends upon the arbitrary selection of a 'quality criterion' that is very data driven. High quality clusters can be obtained, but this often leads to a large number of clusters. Conversely, a small number of clusters can be obtained by lowering the quality criterion. Future work will determine if we can use this time-seriesclustering approach to find repeatable clusters that may or may not suggest changes to current geographic boundaries in an effort to coordinate future influenza surveillance activities. 