The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities.

The urban scaling theory (UST) strives for a universal taxonomy that depicts relationships among urban indicators (e.g. energy consumption, economic output) with city size. However, the lack of international agreement on city definitions and statistics complicates cross-country comparisons of urban scaling performance. Remote sensing provides a uniform standard for measuring cities around the world. To scrutinize the consistency of UST, we quantified changes in remotely sensed urban built-up areas (UBA) and nighttime lights (NTL) distributions from 11,581 cities in 61 countries spanning 2000–2020, representing urban physical elements and socioeconomic activities, respectively. We find that UBA is well described by UST in all analyzed countries, while NTL aligns with 98% of them. UST quantified by remote sensing shows greater robustness than country-dependent aggregate statistics. We also observed disparities of scaling exponents (β) among countries, with UBA all being sublinear (β < 1), and NTL ranging from 0.46 to 1.22 with a median of 0.94. Both UST and rank-size distributions of urban area and population show stronger scaling relationships for countries with larger networks of built environments, (Brazil, China, Germany, Japan, Russia, United States) suggesting that both size and evolution of urban systems impact the underlying scaling processes. Comparison of scaling properties of remotely sensed UBA and NTL captures complementary physical characteristics of built environments while minimizing the Modifiable Area Unit Problem introduced using spatially aggregated metrics within administrative units. Our findings highlight the consistency of urban growth patterns while confirming the systematic socioeconomic disparities among urban systems of varying size and growth trajectory.