Burst identification has been extensively studied in the context of document streams, where a burst is generally exhibited when an unusually high frequency is observed for a term t. Previous works have focused exclusively on either temporal or spatial burstiness patterns. The former represents bursty timeframes within a single stream, while the latter characterizes sets of streams that simultaneously exhibited a bursty behavior for a user-specified timeframe. Our previous work  was the first to study the spatiotemporal burstiness of terms. In this context, a burstiness pattern consists of both a timeframe and a set of streams, both of which need to be identified automatically. In this paper we describe STEM (Spatio-TEmporal Miner), a system for finding spatiotemporal burstiness patterns in a collection of spatially distributed frequency streams. STEM implements the full functionality required to mine spatiotemporal bursti-ness patterns from virtually any collection of geostamped streams. Examples of such collections include document streams (e.g. online newspapers), geo-aware microblogging platforms (e.g. Twitter). This paper describes the STEM system and discusses how its features can be accessed via a user-friendly interface. Copyright © 2013 ACM.