移动使用可以预测发展中国家的文盲率吗？
Can mobile usage predict illiteracy in a developing country?
摘要：
目前的研究提供了第一个证据表明，文盲可以可靠地预测从标准的移动电话日志。通过推导出一套广泛的手机指标，反映用户的财务，社会和流动模式，我们展示了如何监督机器学习可以用来预测个人文盲在亚洲发展中国家，外部验证对大规模的调查。平均而言，该模型的性能比随机猜测好10倍，准确率为70%。此外，我们展示了如何个人文盲可以汇总和映射地理上的手机信号塔的分辨率。绘制文盲地理分布图对于了解文盲人口分布情况以及向何处投入资源至关重要。在不发达国家，这种绘图往往是基于过时的住户调查，空间和时间分辨率很低。全世界五分之一的人与文盲作斗争，据估计，文盲每年给全球经济造成的损失超过1万亿美元。这些结果有可能使成本效益高，免费调查文盲有关的问题在一个前所未有的规模
英文摘要：
The present study provides the first evidence that illiteracy can be reliably predicted from standard mobile phone logs. By deriving a broad set of mobile phone indicators reflecting users financial, social and mobility patterns we show how supervised machine learning can be used to predict individual illiteracy in an Asian developing country, externally validated against a large-scale survey. On average the model performs 10 times better than random guessing with a 70% accuracy. Further we show how individual illiteracy can be aggregated and mapped geographically at cell tower resolution. Geographical mapping of illiteracy is crucial to know where the illiterate people are, and where to put in resources. In underdeveloped countries such mappings are often based on out-dated household surveys with low spatial and temporal resolution. One in five people worldwide struggle with illiteracy, and it is estimated that illiteracy costs the global economy more than 1 trillion dollars each year. These results potentially enable costeffective, questionnaire-free investigation of illiteracy-related questions on an unprecedented scale
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。
