Multidimensional Spatiotemporal Analysis of Air Quality Based on Big Data

It adopts a multidimensional spatiotemporal analysis method to analyze the temporal distribution and spatial distribution characteristics of haze pollution from the dimensions of time and space in Chengdu. Through the analysis of real-time data from six air monitoring points over the past three years in Chengdu, it is concluded that the frequency of haze pollution is higher in December and January, and the air quality is relatively poor inside the city compared to outside the city. In addition, adopting a multi criteria perspective to display the changes of air quality in a certain region or time period can discover potential patterns of spatiotemporal changes in air quality, and provide auxiliary decision-making support for formulating long-term prevention and control strategies under different spatiotemporal conditions.