Information School, University of Washington, Seattle, WA 98195, USA.
Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.
Science. 2015 Nov 27;350(6264):1073-6. doi: 10.1126/science.aac4420.
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual's past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
准确和及时的人口特征估计是社会和经济研究和政策的关键投入。在工业化经济体中，新的数据来源正在为人口特征分析提供新的方法，但在发展中国家，大数据的来源较少。我们表明，一个人的过去手机使用历史可以用来推断他或她的社会经济地位。此外，我们还证明，数百万人的预测属性反过来又可以准确地重建整个国家的财富分布，或者推断仅由少数家庭组成的微观区域的资产分布。在资源有限的环境中，人口普查和家庭调查很少见，这种方法以传统方法的一小部分成本提供了一种收集本地化和及时信息的选择。
