Measuring economic activity in China with mobile big data

Abstract

Emerging trends in the use of smartphones, online mapping applications, and social media, in addition to the geo-located data they generate, provide opportunities to trace users' socio-economic activities in an unprecedentedly granular and direct fashion and have triggered a revolution in empirical research. These vast mobile data offer new perspectives and approaches to measure economic dynamics, and they are broadening the social science and economics fields. In this paper, we explore the potential for using mobile data to measure economic activity in China from a bottom-up view. First, we build indices for gauging employment and consumer trends based on billions of geo-positioning data. Second, we advance the estimation of offline store foot traffic via location search data derived from Baidu Maps, which is then applied to predict Apple's revenues in China and to accurately detect box-office fraud. Third, we construct consumption indicators to track trends in various service sector industries and verify them with several existing indicators. To the best of our knowledge, this is the first study to measure the world's second-largest economy by mining such unprecedentedly large-scale and fine-granular spatial-temporal data. In this way, our research provides new approaches and insights into measuring economic activity.

Economic outcomes predicted by diversity in cities

“…Many research studies have shown that these interactions lead to information exchange, idea creation, innovation, productivity, and more opportunities for citizens [19][20][21][22][23][24][25]. Recent studies have shown that the population density and volume of human flow in urban areas are extremely predictive of economic productivity and wealth in those areas [4][5][6][26][27][28].…”

Predicting neighborhoods’ socioeconomic attributes using restaurant data

“…"Nighttime lights" data, for instance, has contributed to estimating regional economic activities (4)(5)(6). Massive online data, generated by social media, mobile phone, and e-commerce platforms, have been used to infer individual's personality (7), unemployment (8)(9)(10), population distribution (11), wealth (12), consumption index (13), and so on. Moreover, current progress with machine-learning algorithms has also made "unstructured data" (e.g., satellite/street-view imagery and text content) valuable in inferring socioeconomic outcomes (14)(15)(16).…”

Analysis of the performance and robustness of methods to detect base locations of individuals with geo-tagged social media data

“…The rise of Spatial Big Data has provided researchers with new data sources to study various research problems. These new kinds of datasets have been introduced into different fields, to address such issues as measuring economic activity (Dong et al 2017, Sobolevsky et al 2017, Mancini et al 2018, Sinclair et al 2018, regionalization (Gao et al 2013, Li et al 2019, Jia et al 2019, urban understanding (Zhou et al 2019, Yao et al 2019, Zhu et al 2020 and human mobility (Yang et al 2019, Chen et al 2019, Soundararaj et al 2020).…”