Performance and sensitivities of home detection from mobile phone data
Performance and sensitivities of home detection from mobile phone data
复制标题
手机数据家庭检测的性能和灵敏度
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Z. Smoreda
中科院分区：
文献类型：
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作者：
Maarten Vanhoof;Clement Lee;Z. Smoreda
大规模基于位置的轨迹，比如手机数据，已被视为一种有前景的数据来源，可用于补充甚至丰富官方统计数据。在很多情况下，利用大量收集的数据的一个先决步骤是从个体用户中检测家庭位置。问题在于，无论是在个体用户层面还是在全国层面，关于家庭位置检测方法的验证（与真实数据集进行比较）或不确定性评估的研究都很少。在本文中，我们对在法国的一个全国性手机数据集上进行的家庭位置检测方法进行了广泛的实证分析。我们分析了9种不同的家庭位置检测算法（HDA）的有效性，并评估了不同的不确定性来源。基于对约1800万用户进行家庭位置检测的225种不同设置，我们讨论了不同的验证措施，并研究了对用户选择（如HDA参数选择和观察期限制）的敏感性。我们的研究结果表明，全国范围内家庭位置检测的性能充其量是中等的，与真实情况的相关性最高仅为0.60。此外，我们表明观察的时间和时长对性能有明显影响，并且与其他不确定性相比，HDA标准和参数选择的影响相当小。我们的研究结果和讨论为其他希望在类似数据集上应用家庭位置检测的从业者，或者需要评估将手机数据用于官方统计相关的挑战和不确定性的从业者提供了有益的见解。
Large-scale location based traces, such as mobile phone data, have been identified as a promising data source to complement or even enrich official statistics. In many cases, a prerequisite step to deploy the massively gathered data is the detection of home location from individual users. The problem is that little research exists on the validation (comparison with ground truth datasets) or the uncertainty estimation of home detection methods, not at individual user level, nor at nation-wide levels. In this paper, we present an extensive empirical analysis of home detection methods when performed on a nation-wide mobile phone dataset from France. We analyze the validity of 9 different Home Detection Algorithms (HDAs), and we assess different sources of uncertainty. Based on 225 different set-ups for the home detection of around 18 million users we discuss different measures for validation and investigate sensitivity to user choices such as HDA parameter choice and observation period restriction. Our findings show that nation-wide performance of home detection is moderate at best, with correlations to ground truth maximizing at 0.60 only. Additionally, we show that time and duration of observation have a clear effect on performance, and that the effect of HDA criteria and parameter choice are rather small compared to other uncertainties. Our findings and discussion offer welcoming insights to other practitioners who want to apply home detection on similar datasets, or who are in need of an assessment of the challenges and uncertainties related to mobilizing mobile phone data for official statistics.