| Abstract: | The connectedness between cities has become one of the most widely discussed topics in urban and regional research in the mobile and big data era. One problem identified is the asymmetric city connectivity, partially due to data availability. We present a data-driven approach based on location and toponym(place name) extracted from social media data, to assess the asymmetric connectivity between cities. The assumption is that a higher frequency of occurrences of the name of city i in posts located in city j would imply that the city i is more influential than other cities upon city j. In addition, we've developed a group of measurements such as the relatedness index, impact index, link strength index, dependence index, and structure similar index to characterize such interactions. This framework of connectivity measurements can also be used to support smart planning taking into account the evolving interplay among cities. The space-time structure of urban systems in China is examined as the case study. |
