Every day, users publish hundreds of millions of microblog postings in popular social-networking platforms such as Twitter and Facebook. When considered in aggregation, microblog postings have been shown to exhibit temporal patterns that reflect events of global significance. In this paper, we propose techniques to identify and quantify spatial patterns: for instance, a hashtag that is popular in one city on a given day, may become popular in a different city on the next day. Detecting these patterns is challenging given that the data are noisy and posts are not physically moving, i.e., they are not continuous trajectories in space like vehicles. Second, we introduce a multi-granular summarization model to describe the movement of a hashtag between two time periods. For interpretability, we seek representations of spatial changes that follow natural or administrative boundaries on a map, such as cities and states. We compare various movement measures using quantitative approaches and user surveys. We evaluate our movement summarization schemes by analytical loss and coverage functions. Our results show that it is possible to reliably detect relevant spatial changes automatically, and to produce simple summaries that represent accurately these changes.