Spatio-temporal Analysis of Urban Disaster Risk Based on Fourier Series Fitting and Characteristic Function Staining Analysis Method

With the rapid development of modern society, disasters occur frequently, and urban disasters greatly threaten the safety of citizens. In order to build a smart city and ensure the safety of citizens to the greatest extent, it is necessary to analyze and predict disasters. In this paper, disasters are classified into seven categories, and correlation analysis is carried out from two aspects of time and space. Based on the historical disaster data, the series fitting model is used, and the regional disaster density in different regions is analyzed by the characteristic function staining analysis algorithm. At the same time, the relationship between disaster frequency and month is established, and the risk is divided from the perspective of time and space. The high law relationship between different types of disasters and time and the high correlation between some disasters in different regions are obtained. In order to prevent the occurrence of disasters and minimize losses, targeted measures can be taken in advance in high-incidence periods and regions.