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Most of the existing studies on the characteristics of precipitation in Shanghai only focus on the statistical characteristics of the precipitation series rather than the internal dynamic characteristic. The visibility graph algorithm has been proven to be an effective algorithm for grasping the internal dynamics characteristics of time series. Therefore, this paper exploits the visibility graph algorithm to analyze the daily precipitation time series in Shanghai from 2000 to 2020. Some characteristics of precipitation during this period are obtained: Typhoon Mesa and Typhoon Fitow had a profound impact on Shanghai's precipitation; There is a certain similarity of the precipitation structure in different periods; Only a few nodes in the precipitation network have a profound impact on the precipitation in Shanghai. • In order to better master the internal dynamic characteristic of precipitation time series in Shanghai, this paper exploits the visibility graph algorithm to analyze the daily precipitation time series in this area from 2000 to 2020. • We analyze the degree values of nodes in the precipitation network and find that Typhoon Mesa and Typhoon Fitow had a profound impact on Shanghai's precipitation. • The triadic time series motifs of precipitation time series are studied. In this way, the influence of Typhoon Mesa and Typhoon Fitow on the precipitation in Shanghai is quantified, and the similarity of the precipitation structure in different periods is analyzed. • Our research on the degree distribution of the precipitation network shows that only a few nodes have a profound impact on the precipitation in Shanghai. [ABSTRACT FROM AUTHOR]

Expo 2010 Shanghai China was a successful, splendid, and unforgettable event, leaving us with valuable experiences. The visitor flow pattern of the Expo is investigated in this paper. The Hurst exponent, the mean value, and the standard deviation of visitor volume indicate that the visitor flow is fractal with long-term stability and correlation as well as obvious fluctuation in a short period. Then the time series of visitor volume is converted into a complex network by using the visibility algorithm. It can be inferred from the topological properties of the visibility graph that the network is scale-free, small-world, and hierarchically constructed, confirming that the time series are fractal and a close relationship exists among the visitor volumes on different days. Furthermore, it is inevitable that will be some extreme visitor volumes in the original visitor flow, and these extreme points may appear in a group to a great extent. All these properties are closely related to the feature of the complex network. Finally, the revised linear regression is performed to forecast the next-day visitor volume based on the previous 10-day data. [ABSTRACT FROM AUTHOR]