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.