Authors: Yuanyuan Tian*, Arizona State University, Wenwen Li, Arizona State University, Shaohua Wang, Arizona State University
Topics: Spatial Analysis & Modeling, Urban Geography, Transportation Geography
Keywords: GWR, MGWR, Megacity, Spatial analysis
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Harding, Marriott, Mezzanine Level
Presentation File: No File Uploaded
Dockless shared bikes rise as a demanding penicillin to heal the embarrassing “last mile problem” in megacities. Despite of accessibility, trajectory, equity analysis, the city spatial context tells more about potential usages of shared bikes, yet there’s a lack of attention for it. This study is conducted to discover shared bike usage context in megacities, using Shanghai, China as a case study. The research uses optimized hot spot analysis and regression modeling to reveal the spatial heterogeneity of city context and its implications on bike sharing. The case study uses over 100 thousand trip records from the Mobike bike sharing company (August 2016), and other data as city context variables such as commute, shopping, etc. To uncover the relationship and process of bike sharing and city context, ordinary least squares regression (OLS) is used as a global model, and geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) are used as local models. In addition, various city context variable bandwidth results in MGWR shed light on a multi-scale megacity context analysis, specifically at local, regional, and global scales. The result will help improve the management of shared bikes and public transportation, and also provide inspirations for urban planning.