Data on influenza and pneumonia mortality were collected from 97 cities over 11 years (1996 through 2007), as reported in the MMWR (1). We used a novel method of computing the pairwise distance between two time series based on the Mahalanobis Distance derived from the time-series state-space-modeling framework. Mahalanobis Distance is a scale invariant form of Euclidean Distance that also takes correlations of the data set into account. This is an extension of a previously devised Kullback-Leibler Information-based time-series-clustering discrepancy measure (2) . All pairwise distances between cities were then used in a clustering procedure known as QT_Clust (3). This procedure was initially developed for the clustering of high dimensional genomic data. However, QT_Clust may be applied to many time-series-data sets where the trajectory rather than the process of a time series is of interest. A measure of cluster size and within-cluster distance is used to compare how geographically based influenza surveillance performs as opposed to nongeographically based surveillance.