Though significant progress has been made in recent years, traditional work in social networks has focused on static network analysis or dynamics in a large-scale sense. In this work, we explore ways in which temporal information from sociographic data can be used for the analysis and prediction of individual and group behavior in dynamic, real-world situations. Using the MIT Reality Mining corpus, we show how temporal information in highly-instrumented sociographic data can be used to gain insights otherwise unavailable from static snapshots. We show how pattern of life features extend from the individual to the group level. In particular, we show how anonymized location information can be used to infer individual identity. Additionally, we show how proximity information can be used in a multilinear clustering framework to detect interesting group behavior over time. Experimental results and discussion suggest temporal information has great potential for improving both individual and group level understanding of real-world, dense social network data.