Our micromap tour now continues with a detailed look at conditioned micromaps. The goal of this type of micromap plot is to help us think about the geographic patterns and associations in our data using more than one variable at a time. Conditioning partitions the geographic regions shown in a single choropleth map. When conditioning is based on the values of two other variables, the resulting two-way layout of micromaps highlights different subsets of the regions. This conditioned structure often reveals patterns that beg for explanation, encourages hypothesis generation and further study, which leads to better understanding about the relationships among three variables. Put simply, most people recognize and have ideas about map patterns even if they do not yet understand all of the details about the processes that generated the data being mapped. The widespread appeal of maps and easy interaction with the dynamic sliders of conditioned micromaps encourage users to become more involved, learning more about the data collection process and even about the statistical methods involved. Thus, conditioned micromaps provide a wonderful framework for data exploration that incorporates both pattern discovery and hypothesis generation.