In recent decades, the availability of diverse location-based service (LBS) data has largely stimulated the research in individual human mobility. However, less attention has been paid on the intra-city movement of cyclists coupled with their spatiotemporal dynamics. To fill the knowledge gap, drawing on bicycle-sharing data over one week in Shanghai, China, this study investigates the dynamics of bicycle-sharing users at two spatial scales (i.e., city level and subdistrict level) and explores the intra-city spatial interactions by those cyclists. At the city level, by applying the analysis of variance (ANOVA) test and the Wilcoxon signed-rank test, this study examines the temporal variation of cyclists across a seven-day period. At the subdistrict level, we develop a new index to capture the urban vitality using bicycle-sharing data with the consideration of trip flow allied with spatial weights. In terms of the computed urban vitality over the course of a day, 98 subdistricts are partitioned into 7 groups by using K-means clustering. In addition, spatial autocorrelation and hot spot analysis are also applied to examine the spatial features of urban vitality at different periods. Our results reveal that urban vitality has an obvious character of the spatial cluster and this cluster feature varies markedly over the course of a day. By shedding new lights on intra-city movement, we argue our results are important in informing urban planners on how to better allocate public facilities and increase bicycle usage as a way to progress towards more sustainable urban areas.

In recent decades, the ecological security pattern (ESP) has drawn increasing scientific attention against the backdrop of rapid urbanization and worsening ecological environment. Despite numerous achievements in identifying and constructing the ecological security pattern, limited attention has been paid on applying ESP to predict urban growth. To bridge the research gap, this paper took Quanzhou, China as a study case and incorporated the identified ESP into an urban growth simulation with three distinct scenarios. Following the “ecological source–ecological corridor–ecological security pattern” paradigm, the ESP identification was carried out from four single aspects (i.e., water, geology, biodiversity, and recreation) into three levels (i.e., basic ESP, intermediate ESP, and optimal ESP). Grounded in an equally weighted superposition algorithm, the four single ESPs were combined as an integrated ESP (IESP) with three levels. Taking IESP as an exclusion element, urban growth simulation in 2030 was completed with thee SLEUTH model. Drawing on the three levels of IESP, our urban growth simulation contained three scenarios. In terms of urban sprawl distribution coupled with urban growth rate, an optimal urban growth scenario is recommended in this paper to balance both urban development and eco-environment protection. We argue that our ESP-based urban growth simulation results shed new light on predicting urban sprawl and have the potential to inform planners and policymakers to contribute to more environmentally-friendly urban development.

This paper examines the influence that weather conditions exert on transit ridership in sub-tropical Brisbane, Australia. Drawing on transit smart card and weather station records covering a period of six months, the weather-transit ridership relationship across three different travel modes is modeled. Our results reveal that weather does impose an effect on transit ridership and that its influence varies by both transit mode and across weekdays and weekends. On weekdays, the heat index, an indicator of human-perceived temperature, was found to exert a negative effect on bus and train ridership, while ferry ridership was shown to be most sensitive to rainfall. On weekends, the ridership across all three transit modes decreased on rainy days. Results from quantile regression suggest that on weekdays during periods of higher ridership the influence of weather on transit ridership becomes stronger, while on weekends the influence of weather is lessened. We argue that our findings are important for transit agencies in their capacity to unveil how an important predictor of ridership variation, weather, exerts an influence across a transit system. These preliminary findings mark the first step in assembling the necessary evidence to build a more weather-resilient transit system.