Institute of Remote Sensing and GIS, Peking University, Beijing, People's Republic of China.
Centre for Complexity Science and Department of Mathematics, Imperial College London, London, UK.
J R Soc Interface. 2024 May;21(214):20230495. doi: 10.1098/rsif.2023.0495. Epub 2024 May 8.
Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.
监测城市结构和发展需要高质量、高时空分辨率的数据。虽然传统的人口普查为城市生活的人口和社会经济方面提供了基础见解，但它们的速度可能并不总是与城市发展的速度保持一致。为了补充这些传统方法，我们探索了分析替代大数据源（如人类流动数据）的潜力。然而，这些通常嘈杂和非结构化的大数据带来了新的挑战。在这里，我们提出了一种从这些数据中提取有意义的解释变量和分类的方法。我们使用北京的移动数据，这些数据是移动通信的副产品，结果表明可以从中提取有意义的特征，例如揭示副中心的出现和吸收。该方法允许以高空间分辨率（这里为 500 米）和接近实时的频率以及高计算效率分析城市动态，这特别适合追踪事件驱动的流动变化及其对城市结构的影响。
