利用城市自行车中途停留揭示城市吸引力的空间模式
Using city-bike stopovers to reveal spatial patterns of urban attractiveness
AI总结：
本研究利用城市自行车的中途停留数字轨迹，提出带过滤与交叉验证的通用方法，在克拉科夫案例中有效识别城市吸引力空间模式，为城市空间消费分析提供支撑。
摘要：
我们展示了城市自行车出行的数字轨迹如何可用于识别城市空间吸引力。我们利用了其独特特征——中途停留（stopovers）：骑行者在出行途中做出的、与交通无关的短暂停留。正如我们在波兰克拉科夫的案例研究中所展示的，当该方法应用于大型数据集时，会出现有意义的模式：在顶级旅游休闲景点以及新兴场所中，都识别出了热点（即停留时间长、频次高的地点）。\n我们提出了一种通用方法，适用于任何时空城市自行车轨迹，其结果有助于理解城市空间的整体吸引力及其动态变化。通过提出的过滤操作（以缓解选择偏差）和实证交叉验证（以排除假阳性分类），研究结果有效揭示了城市吸引力的空间模式，对决策者和分析师深化理解游客与居民的城市空间消费模式具有价值。
英文摘要：
We demonstrate how digital traces of city-bike trips may become useful to identify urban space attractiveness. We exploit their unique feature - stopovers: short, non traffic-related stops made by cyclists during their trips. As we demonstrate on the case-study of Krakow (Poland), when applied to a big dataset, meaningful patterns appear, with hotspots (places with long and frequent stopovers) identified at both the top tourist and leisure attractions as well as emerging new places. We propose a generic method, applicable to any spatiotemporal city-bike traces, providing results meaningful to understand both the general urban space attractiveness and its dynamics. With the proposed filtering (to mitigate a selection bias) and empirical cross-validation (to rule-out false-positive classifications) results effectively reveal spatial patterns of urban attractiveness. Valuable for decision-makers and analysts to enhance understanding of urban space consumption patterns by tourists and residents.
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