Email has become an integral method of communication. However, it is still plagued by vast amounts of spam. Many statistical techniques, such as Bayesian filtering, have been applied to this problem, and been proven useful. But these techniques in general require training. Another common method of spam prevention is blacklisting known spam sources. In order to do this, the sources must be identified. What this paper presents is a set of visualization techniques designed to show patterns in incoming email which can reveal misidentified pieces of spam, common spam sources, and patterns such as periods of increased spam activity, while maintaining the privacy of the email. This can aid system administrators in rapidly and effectively adjusting system level filters, which would improve the quality of service and decrease the time and resources wasted by spam.