Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-scale geospatial data processing system that enables city analysis on an unprecedented scale. We demonstrate the system's capabilities by revisiting the urban scaling law across 21,280 cities globally, using a range of open-source datasets including road networks, nighttime light intensity, built-up areas, and population statistics. Analyzing the characteristics of 21,280 cities involves querying over half a billion geospatial data points, a task that traditional Geographic Information Systems (GIS) would take several days to process. In contrast, our cloud-based system accelerates the analysis, reducing processing time to just minutes while significantly lowering resource consumption. Our findings reveal that the urban scaling law varies across cities in under-developed, developing, and developed regions, extending the insights gained from previous studies focused on hundreds of cities. This underscores the critical importance of cloud-based big data processing for efficient, large-scale geospatial analysis. As the availability of satellite imagery and other global datasets continues to grow, the potential for scientific discovery expands exponentially. Our approach not only demonstrates how such large-scale tasks can be executed efficiently but also offers a powerful solution for data scientists and researchers working in the fields of city and geospatial science.

翻译：城市在人类发展与可持续性中扮演着关键角色，但由于时空数据的巨大规模与复杂性，对其研究仍面临重大挑战。其中一项挑战在于需要从全球数千座城市中揭示普遍的城市规律，例如城市规模法则。本研究提出了一种新颖的大规模地理空间数据处理系统，实现了前所未有的城市分析规模。我们通过重新审视全球21,280座城市的规模法则，展示了该系统的能力，所使用的开源数据集包括道路网络、夜间灯光强度、建成区面积及人口统计数据。分析21,280座城市的特征涉及查询超过5亿个地理空间数据点，传统地理信息系统（GIS）需要数日才能完成处理。相比之下，我们的云基系统将分析时间缩短至数分钟，同时显著降低了资源消耗。研究结果表明，城市规模法则在欠发达、发展中及发达地区的城市间存在差异，这拓展了以往针对数百座城市研究的认知。这凸显了基于云的大数据处理对高效、大规模地理空间分析的关键重要性。随着卫星影像及其他全球数据集的持续增长，科学发现的潜力呈指数级扩大。我们的方法不仅展示了如何高效执行此类大规模任务，更为城市科学与地理空间科学领域的数据科学家和研究者提供了强有力的解决方案。