Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize people's use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individual's activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities.