Study of global illegal wildlife trade volume based on social network analysis and ARIMA model

Abstract

The illegal trade in wildlife and its products has become a global problem, and reducing the trade in illegal wildlife trade worldwide is important for the protection of biodiversity, social public safety and wildlife welfare. To explore ways to reduce illegal wildlife trade, the amount of wildlife trade needs to be integrated and predicted. Using CITES datasets and collected data, multiple linear regression was used to integrate the historical data of illegal wildlife trade, identify important factors, combine social network analysis and ARIMA time series analysis, build AR (2) model to fit the historical data, and forecast global trade records for the next five years. The results show that, the IWT will continue to rise globally over the next five years. At the same time, according to the influencing factors obtained by data integration, it provides the following suggestions and countermeasures to reduce illegal trade: strengthening legal effect, promoting national cooperation, etc.