A refined dichotomy on returners and explorers in urban mobility pattern

Abstract

With the change in people’s lifestyle and travel mode, understanding the individual and population mobility patterns in urban areas remains to an outstanding problem. Pervasive mobile communication technologies generate voluminous data related to human mobility, such as mobile phone data. To further study the characteristics of returning and exploration patterns of human movement in urban space, a multi-index model is proposed based on the original radius of the gyration index. In this paper, the classification mechanism of a single ratio of the radius of gyration for k-explorers and k-returners is illustrated. Some disadvantages of this mechanism are noted. A few indices of the model are proposed for deep mining of data on human mobility exploration and returning characteristics. Taking a mobile phone data during an entire month as a sample, and after data processing on the Spark platform, the characteristics of various indicators and their correlations are analyzed. The classification effects of different spatial indices for human exploration and returning are compared by using a support vector machine and the binary classification algorithm and are further compared with existing research results. The differences in the classification effects of these indicators are analyzed, which is helpful for in-depth studies of urban mobility patterns.