Dockless bike share has been booming globally and in China. This paper aims to study what the mobile bike share data can help in terms of understanding our cities from the bikers’ perspective. The hypothesis is that mobile bike data would reveal a universal pattern in the bikeability distribution among Chinese cities. The research method is to construct a bikeability rating system, Mobike Riding Index (MRI), which measures bikeability in terms of usagefrequency and the builtenvironment. The first component of this paper is to investigate what Mobike ridership data entails and what relevant built environment data can be considered. The study of Mobike data found that people tend to ride shared bikes at a speed close to 10km/h, a length of 2km, and a frequency of three times a day. The implications tourban planners are addressed in the discussion. Following that is to establish the MRI framework and calculate its scores. After calculating the MRI, we studied the nationwide distribution of MRI in different analytical levels and discuss the noticeable patterns. Results show that at the street level, the weekday MRI distribution is analogous to that of the weekend’s with an average score of 49.8; at the township level, highly scored townships are those close to the city center; at the city level, the MRI distributes unevenly, with high MRI cities staying along the southern coastline or the middle inland area, such as Shenzhen, Chengdu, and Wuhan. This study is the first to incorporate mobile bike share data into bikeability measurement in China, and the largest in scale with 202 cities included, setting the ground for further research in this subject.