Vast amount of data are routinely collected, and analyzing them effectively has become a central challenge we face across science and engineering. Topological data analysis (TDA) is a field that has recently emerged in order to tackle this challenge. This talk will focus on the problem of comparing two road networks (for example, to detect where and by how much a road network has changed over the course of a year). Surprisingly, only recently have distance measures between embedded graphs (representing road networks) been studied. We will see how one of the tools from TDA, namely, persistent homology, can be used to define a local distance measure between two graphs. Persistent homology describes the homology (in particular, the number of connected components and loops) of a data set, at different scales. An example to keep in mind is impressionistic paintings: at one scale, all that is seen are brush strokes; at a larger scale, the brush strokes blur together to form the subject of the painting. The (local) persistent homology distance measure is one of the first theoretically justified approaches to road network comparison. This talk should be accessible to both students and faculty.