Assessing the socio-demographic representativeness of mobile phone application data
Assessing the socio-demographic representativeness of mobile phone application data
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DOI：
10.1016/j.apgeog.2023.102997
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发表时间：
2023-09
影响因子：
4.9
通讯作者：
Michael Sinclair;Saeed Maadi;Qunshan Zhao;Jinhyun Hong;A. Ghermandi;N. Bailey
中科院分区：
文献类型：
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作者：
Michael Sinclair;Saeed Maadi;Qunshan Zhao;Jinhyun Hong;A. Ghermandi;N. Bailey
使用移动电话应用程序产生的新形式的移动电话数据有可能推动一系列学科的科学研究。然而，这些数据的社会人口代表性存在不确定性的风险，这可能会导致偏见和误导政策建议。本文通过开发一种评估社会人口代表性的新方法直接解决了这个问题，并通过两个大型独立的移动电话应用程序数据集Huq和Tamoco来证明这一点，每个数据集都有一个拥有180多万人口的大型多样化城市地区（苏格兰格拉斯哥）的三年数据。我们提出了通过在过程中包含高分辨率土地利用数据和跨多个维度测试代表性来检测家庭位置的方法。我们的研究结果为使用手机应用程序数据进行研究和规划提供了更大的信心。与已知的总体分布相比，这两个数据集都具有很好的代表性。事实上，它们比“黄金标准”随机抽样调查实现了更好的人口覆盖率，随机抽样调查是该地区人口流动数据的另一种来源。更重要的是，我们的方法为将来评估类似数据源的质量提供了一个改进的基准。
Emerging forms of mobile phone data generated from the use of mobile phone applications have the potential to advance scientific research across a range of disciplines. However, there are risks regarding uncertainties in the socio-demographic representativeness of these data, which may introduce bias and mislead policy recommendations. This paper addresses the issue directly by developing a novel approach to assessing socio-demographic representativeness, demonstrating this with two large independent mobile phone application datasets, Huq and Tamoco, each with three years data for a large and diverse city-region (Glasgow, Scotland) home to over 1.8 million people. We advance methods for detecting home location by including high-resolution land use data in the process and test representativeness across multiple dimensions. Our findings offer greater confidence in using mobile phone app data for research and planning. Both datasets show good representativeness compared to the known population distribution. Indeed, they achieve better population coverage than the ‘gold standard’ random sample survey which is the alternative source of data on population mobility in this region. More importantly, our approach provides an improved benchmark for assessing the quality of similar data sources in the future.