从CDR数据中检测家庭位置：将空间不确定性引入最新技术水平
Detecting home locations from CDR data: introducing spatial uncertainty to the state-of-the-art
摘要：
从人口规模的呼叫详细记录（CDR）推断的非连续位置轨迹越来越多地用于研究，并显示出自动检测有意义的地方的巨大潜力。然而，大多数家庭检测算法（HDA）遭受“盲目”部署的标准来定义家庭和有限的可能性验证。在本文中，我们调查的性能和能力的五个流行的标准，从法国（~ 1800万用户，6个月）的一个非常大的手机数据集的基础上，家庭检测。此外，我们构建了一个数据驱动的框架，以评估与HDA的应用空间不确定性。我们的研究结果适当的空间不确定性HDA，并在扩展，检测有意义的地方。我们展示了如何在没有地面实况注释的情况下评估个人层面的空间不确定性，它们如何与传统的高级别验证实践相关，以及它们如何用于改善结果，例如，全国人口估计。
英文摘要：
Non-continuous location traces inferred from Call Detail Records (CDR) at population scale are increasingly becoming available for research and show great potential for automated detection of meaningful places. Yet, a majority of Home Detection Algorithms (HDAs) suffer from "blind" deployment of criteria to define homes and from limited possibilities for validation. In this paper, we investigate the performance and capabilities of five popular criteria for home detection based on a very large mobile phone dataset from France (~18 million users, 6 months). Furthermore, we construct a data-driven framework to assess the spatial uncertainty related to the application of HDAs. Our findings appropriate spatial uncertainty in HDA and, in extension, for detection of meaningful places. We show how spatial uncertainties on the individuals' level can be assessed in absence of ground truth annotation, how they relate to traditional, high-level validation practices and how they can be used to improve results for, e.g., nation-wide population estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
