A limited number of studies using static spatial approaches have found that built environment variables are associated with bike share use and fewer have used spatially dynamic activity spaces to examine these relationships. The aim of this pilot study was to examine associations between built environment characteristics of daily activity spaces and bike share using three different geographic information system methods. Thirty-two adult members of Boston’s Blue Bikes bike share wore a GPS unit for up to 7 days. GPS points were used to create buffered track, minimum convex hull (MCH), and standard deviational ellipse (SDE) activity spaces. Multilevel logistic regression was used to estimate associations between docking station density, overall bicycle network density, shared-use trail density, intersection density, land use mix, and greenness, with bike share use. Bike share station density within SDE activity spaces showed a significant positive association with bike share (odds ratio (OR) = 1.19; 95 % confidence interval (CI): 1.02, 1.39). Total bike network and shared-use trail densities within MCH activity spaces were positively associated with bike share (OR = 1.13; 95 % CI: 1.02, 1.26 and OR = 1.75; 95 % CI: 1.06, 2.89, respectively). Intersection density within SDE activity spaces was inversely associated with bike share (OR = 0.91; 95 % CI: 0.83, 0.99). GPS tracking of individuals allowed for spatially and temporally dynamic identification of environmental exposures potentially relevant to bike share use. Overall, the findings are consistent with prior research on the environmental correlates of bike share and reinforce the importance of bicycle infrastructure to support greater bike share use. At the same time larger studies are needed to explore optimal geographic methods to define activity spaces in relation to bike share.

中文翻译：

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日常活动空间的建筑环境特征及其与共享单车使用的关联分析


使用静态空间方法的有限研究发现，建筑环境变量与共享单车的使用相关，而很少有研究使用空间动态活动空间来检查这些关系。这项试点研究的目的是使用三种不同的地理信息系统方法来检查日常活动空间的建筑环境特征与自行车共享之间的关联。波士顿 Blue Bikes 共享单车的 32 名成年会员佩戴 GPS 设备长达 7 天。 GPS 点用于创建缓冲轨迹、最小凸包 (MCH) 和标准差椭圆 (SDE) 活动空间。使用多级逻辑回归来估计停靠站密度、整体自行车网络密度、共享小径密度、交叉口密度、土地利用组合和绿化与自行车共享使用之间的关联。 SDE 活动空间内的自行车共享站密度与自行车共享呈显着正相关（比值比 (OR) = 1.19；95% 置信区间 (CI)：1.02、1.39）。 MCH 活动空间内的自行车网络总量和共享路线密度与自行车共享呈正相关（OR = 1.13；95% CI：1.02、1.26 和 OR = 1.75；95% CI：1.06、2.89）。 SDE 活动空间内的交叉​​口密度与自行车共享呈负相关（OR = 0.91；95% CI：0.83，0.99）。对个人的 GPS 跟踪可以在空间和时间上动态识别与共享单车使用可能相关的环境暴露。总体而言，研究结果与之前关于共享单车环境相关性的研究结果一致，并强调了自行车基础设施对于支持更多共享单车使用的重要性。 与此同时，需要进行更大规模的研究来探索最佳地理方法来定义与共享单车相关的活动空间。