The Classification Algorithm Based on Functional Logistic Regression Model with Spatial Effects and Its Application in Air Quality Analysis

With the acceleration of economic development and urbanization, air pollution has become increasingly severe and has been a crucial issue affecting social advancement. Considering the spatial correlation between regions in air quality analysis can improve the accuracy of model estimation for the data on air pollution. First, we propose the functional Logistic regression model with spatial effects. Second, we fit the original data into functional data using B-spline basis functions and apply functional principal component analysis for dimension reduction. Further, the model is estimated using the maximum likelihood method. Finally, the effectiveness of the proposed model is validated through numerical simulations and a real data analysis for PM2.5 air quality in the Sichuan-Chongqing region of China.