Sequenced spatiotemporal aggregation (SSTA) is an important query for many applications of spatiotemporal databases, such as traffic analysis. Conceptually, an SSTA query returns one aggregate value for each individual spatiotemporal granule. While the data is typically recorded at a fine granularity, at query time a coarser granularity is common. This calls for efficient evaluation strategies that are granularity aware. In this paper, we formally define an SSTA operator that includes a data-to-query granularity conversion. Based on a discrete time model and a discrete 1.5 dimensional space model, we generalize the concept of time constant intervals to constant rectangles, which represent maximal rectangles in the spatiotemporal domain over which an aggregation result is constant. We propose an efficient evaluation algorithm for SSTA queries that takes advantage of a coarse query granularity. The algorithm is based on the plane sweep paradigm, and we propose a granularity aware event point schedule, termed gaEPS, and a granularity aware sweep line status, termed gaSLS. These data structures store space and time points from the input relation in a compressed form using a minimal set of counters. In extensive experiments, we show that for coarse query granularities gaEPS significantly outperforms a basic EPS that is based on an extension of previous work, both in terms of memory usage and runtime.