To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R2 ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

The interaction among social economy, geography, and environment leads to the occurrence of traffic accidents, which shows the relationship between time and space. Therefore, it is necessary to study the temporal and spatial correlation and provide a theoretical basis for formulating traffic accident safety management policies. This paper aims to explore the traffic accident patterns in 31 provinces of China by using statistical analysis and spatial clustering analysis. The results show that there is a significant spatial autocorrelation among traffic accidents in various provinces and cities in China, which means that in space, the number of traffic accidents and deaths is high with high aggregation and low with low aggregation. Positive spatial autocorrelation is primarily concentrated in the southeast coastal areas, while negative spatial autocorrelation is mainly concentrated in the western areas. Jiangsu, Anhui, Fujian, and Shandong are typical areas of traffic accidents, which deviate from the overall positive spatial autocorrelation trend. Traffic accidents in Sichuan are much more serious than those in neighboring provinces and cities; however, in recent years, this situation has disappeared.

In view of the deficiency that the traditional importance ranking method cannot be used to objectively and comprehensively evaluate the importance of the causes of hoisting injuries, an importance ranking method based on topological potential is proposed by using complex network theory and field theory in physics. First, the causes of 385 reported lifting injuries are divided into 36 independent causes at four levels through a systematic analysis approach, and the relationships among these causes are obtained through the Delphi method. Then, the accident causes are treated as nodes, and the relationships among the causes are used as edges to establish a network model of the causes of lifting accidents. The out-degree and in-degree topological potential of each node are calculated, and an importance ranking of lifting injuries causes is obtained. Finally, based on 11 evaluation indexes commonly used to assess node importance (node degree, betweenness centrality, etc.), the ability of the method proposed in this paper to effectively identify the key nodes in the cause network of lifting accidents is verified, and the conclusions can guide the safe implementation of lifting operations.