Yurong Chen

Calculating the availability of bicycles and racks is a traditional method for detecting imbalance usage in a public bicycle system (PBS). However, for bike-sharing systems in Asian countries, which have compact layouts and larger system scales, an alternative docking station may be found within walking distance. In this paper, we proposed a synthetic and spatial-explicit approach to discover the imbalance usage by using the Hangzhou public bicycle system as an example. A spatial filter was used to remove the false-alarm docking stations and to obtain true imbalance areas of interest (AOI), where the system operation department installs more stations or increases the capacity of existing stations. In addition, sub-nearest neighbor analysis was adopted to determine the average distance between stations, resulting in an average station spacing of 190 m rather than 15.5 m, which can reflect the nonbiased service level of Hangzhou’s public bicycle systems. Our study shows that neighboring stations are taken into account when analyzing PBSs that use a staggered or face-to-face layout, and our method can reduce the number of problematic stations that need to be reallocated by about 92.81%.

Urban land use is constantly changing via human activities. These changes are recorded by imprint data. Traditionally, urban dynamics studies focus on two-dimensional spatiotemporal analysis. Based on our best knowledge, there is no study in the literature that uses imprint data for better understanding urban dynamics. In this research, we propose a spatial–temporal–semantic triple analytical framework to better understand urban dynamics by making full use of the imprint data, toponyms. The framework includes a text classification method and geographical analysis methods to understand urban dynamics in depth. Based on the inherent temporal and spatial information, we enrich semantic information with street names to explain urban dynamics in multiple dimensions. Taking Hangzhou city as an example, we used street names to reproduce the city changes over the past century. The results obtained through analysis of street names may accurately reflect the real development process of Hangzhou. This research demonstrates that imprint data left by urban development may play a pivotal role in better understanding urban dynamics.