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首页> 外文期刊>Journal of geovisualization and spatial analysis >Discovering Spatiotemporal Patterns and Urban Facilities Determinants of Cycling Activities in Beijing
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Discovering Spatiotemporal Patterns and Urban Facilities Determinants of Cycling Activities in Beijing

机译：发现时空模式和城市设施自行车活动的决定因素北京

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The new generation of dockless bike sharing systems has been deployed on a large scale around the world, successfully promoting cycling activities. Analyzing cycling activity patterns can reveal people's travel behavior and urban dynamics with fine granularity. This paper aims to discover the spatiotemporal patterns and urban facilities determining cycling activities based on dockless bike sharing data in the downtown area of Beijing. We collected approximately 1.5 million cycling trip records for seven consecutive days. In urban spaces, roads are basic spatial elements for human movement. Thus, this study shifts the analysis perspective to a road network perspective. We use network kernel density estimation (NKDE) to analyze the spatiotemporal distribution of cycling activities. In the NKDE, the road unit is the analysis unit. The road unit is an approximate decomposition of a block and can minimize the effect of the modifiable areal unit problem (MAUP). We then apply a colocation mining method, network-distance-based global colocation quotient (GCLQ), which does not need to divide the study area into analysis units and is not affected by the MAUP, to examine the association between cycling activities and four categories of urban facilities, including transportation facilities, company and business facilities, residences, and scenic spots. Finally, taking spatial heterogeneity into consideration, we apply network-distance-based local colocation quotient (LCLQ) to capture the variability of association across areas. The result shows that cycling activity hot spot areas are within the Fourth Ring Road or near subway line one and the Batong Line. In addition to exhibiting obvious morning and evening peaks, cycling activities exhibit a small peak at noon on weekdays. On weekends, cycling activities show a relatively uniform temporal distribution. LCLQ performs better than GCLQ. The results of LCLQ show the spatial variance of the association and identify the areas where cycling activities are associated with different urban facilities at different times.
机译：新一代的dockless自行车共享系统已被大规模部署世界,成功地促进循环活动。可以揭示人们的旅游行为和城市动力学与细粒度。发现和城市的时空模式基础设施决定骑自行车活动在市中心dockless自行车共享数据北京的面积。百万自行车出行记录7连续两天。人体运动的基本空间元素。本研究分析的视角转变道路网络的视角。密度估计(NKDE)分析骑自行车的时空分布,活动。分析单位。一块分解,可以最小化修改的区域单元问题的影响(MAUP)。network-distance-based全球主机托管商(GCLQ),不需要划分研究区域为分析单位和不受影响MAUP,检查之间的关系骑自行车活动,四类城市设施,包括交通设施,公司和商业设施,住宅,景区。异构性考虑,我们应用network-distance-based本地主机托管商(LCLQ)来捕获协会的可变性跨区域。在第四个活动热点区域环城公路或附近地铁八通线的线。和晚上的山峰,骑自行车活动展览小峰在工作日中午。显示一个相对统一的骑自行车活动时间分布。GCLQ。方差的协会和识别骑自行车活动相关联的领域不同的在不同的城市设施次了。

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