论文标题
使用Google Maps位置历史数据来检测社交网络中的联合活动的有效性
The effectiveness of using Google Maps Location History data to detect joint activities in social networks
论文作者
论文摘要
这项研究评估了使用Google Maps位置历史数据来识别社交网络中的联合活动的有效性。为此，进行了一个实验，要求参与者执行旨在模拟每日旅行的日程安排，以结合联合活动。对于Android设备，四人组活动的检测率从严格的时空准确性标准的22％到在不太严格但仍在运行的标准下的60％不等。 iPhone的性能明显比Android设备要差，而不论准确的标准如何。此外，估计logit模型在不同的时空精度阈值下评估影响活性检测的因素。在效果幅度上，发现对位置，活动持续时间，Android设备比率，设备模型比率，目的地是否是开放空间和组大小的位置，活动持续时间，设备模型比率，设备模型比率（Android设备比率）的底面积比（FAR）的非平凡影响。 尽管当前的活动检测率并不理想，但必须权衡这些水平与长时间观察旅行行为的潜力，并且Google地图位置历史数据可能与其他数据收集方法结合使用，以补偿其某些限制。
This study evaluates the effectiveness of using Google Maps Location History data to identify joint activities in social networks. To do so, an experiment was conducted where participants were asked to execute daily schedules designed to simulate daily travel incorporating joint activities. For Android devices, detection rates for 4-person group activities ranged from 22% under the strictest spatiotemporal accuracy criteria to 60% under less strict yet still operational criteria. The performance of iPhones was markedly worse than Android devices, irrespective of accuracy criteria. In addition, logit models were estimated to evaluate factors affecting activity detection given different spatiotemporal accuracy thresholds. In terms of effect magnitudes, non-trivial effects on joint activity detection probability were found for floor area ratio (FAR) at location, activity duration, Android device ratio, device model ratio, whether the destination was an open space or not, and group size. Although current activity detection rates are not ideal, these levels must be weighed against the potential of observing travel behavior over long periods of time, and that Google Maps Location History data could potentially be used in conjunction with other data-gathering methodologies to compensate for some of its limitations.
