Geospatial Network Analysis and Origin-Destination Clustering of Bike-Sharing Activities during the COVID-19 Pandemic

Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings

Fibroblast growth factor 21 predicts arteriovenous fistula functional patency loss and mortality in patients undergoing maintenance hemodialysis

AbstractBackground Arteriovenous fistula (AVF) dysfunction is a common complication in patients undergoing maintenance hemodialysis (MHD). Elevated serum levels of fibroblast growth factor 21 (FGF21) are associated with atherosclerosis and cardiovascular mortality. However, its association with vascular access outcomes remains elusive. The present study evaluated the relationship of serum FGF21 levels with AVF dysfunction and all-cause mortality in patients undergoing MHD.Methods We included patients undergoing MHD using AVF from January 2018 to December 2019. FGF21 concentration was detected using enzyme-linked immunosorbent assay. Patients were followed up to record two clinical outcomes, AVF functional patency loss and all-cause mortality. The follow-up period ended on April 30, 2022.Results Among 147 patients, the mean age was 58.49 ± 14.41 years, and the median serum level of FGF21 was 150.15 (70.57–318.01) pg/mL. During the median follow-up period of 40.83 months, the serum level of FGF21 was an independent risk factor for AVF functional patency loss (per 1 pg/mL increase, HR 1.002 [95% CI: 1.001–1.003, p = 0.003]). Patients with higher serum levels of FGF21 were more likely to suffer from all-cause mortality (per 1 pg/mL increase, HR 1.002 [95% CI: 1.000–1.003, p = 0.014]). The optimal cutoffs for FGF21 to predict AVF functional patency loss and all-cause mortality in patients undergoing MHD were 149.98 pg/mL and 146.43 pg/mL, with AUCs of 0.701 (95% CI: 0.606–0.796, p < 0.001) and 0.677 (95% CI: 0.595–0.752, p = 0.002), respectively.Conclusions Serum FGF21 levels were an independent risk factor and predictor for AVF functional patency loss and all-cause mortality in patients undergoing MHD