失业的移动通信特征
Mobile Communication Signatures of Unemployment
摘要：
人口社会经济福祉的测绘工作受到人口普查和调查后勤工作的严重制约。因此，很难评估以天、周、月甚至年为尺度的空间详细变化;因此，设计和评估政策的速度有限。然而，最近的研究表明，移动电话数据作为人口建模和测量的一种有利方法的价值。在这项工作中，我们调查从手机使用中提取的指标是否可以揭示有关小区（如地区）的社会经济状况的信息（即，平均空间分辨率< 2.7km）。为此，我们研究了匿名的移动电话元数据与失业救济计划的受益人记录相结合。我们发现，总的活动，社会和流动模式与失业密切相关。此外，我们还构建了一个简单的模型，仅从移动通信模式来准确重建地区层面的失业率。我们的研究结果表明，可靠和具有成本效益的经济指标可以建立基于被动收集和匿名的手机数据。由于世界各地的电信服务每天都在收集类似的数据，以调查为基础的衡量社区社会经济地位的方法将来可能会被这种被动传感方法所增强或取代。
英文摘要：
The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution < 2.7km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future.
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。
