Future Cities Laboratory, Singapore-ETH Centre, ETH Zürich, Singapore, Singapore.
Sociology and Economics of Networks and Services department, Orange Labs, Châtillon, France.
PLoS One. 2020 Jun 30;15(6):e0235224. doi: 10.1371/journal.pone.0235224. eCollection 2020.
High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries.
高质量的人口普查数据在发展中国家并不总是可用的。相反，手机数据正成为评估人口密度、活动和社会特征的一种流行替代方法。它们提供了额外的优势：它们实时更新，包括移动信息并记录访问者的活动。然而，我们以塞内加尔为例表明，手机平均活动与人口密度和夜间灯光强度之间的直接相关性可能不够高，无法准确反映实际情况。存在一些原因可以解释这种情况，例如市场份额的异质性或蜂窝塔分布的特殊粒度。相比之下，我们提出了一种基于每日、每周和每年手机活动曲线以及手机数据的网络特征的方法，该方法可以在不损害人们隐私的情况下更准确地估计这些信息。这些信息对于发展和基础设施规划至关重要。特别是，在发展中国家预算特别紧张的情况下，这种方法可以大大降低数据收集的物流成本。
