Research on multi-scale analysis methods of heat island and influencing factors in the core area of the capital

Based on Landsat8 OLI/TIRS data, this paper studies the interaction relationship between the heat island and influencing factors of the core area of the capital. Combined with comprehensive analysis of multi-source data and spatial data exploration, the spatial autocorrelation pattern and spatial correlation between the heat island and influencing factors in the core area of the capital are analyzed, agglomeration mode. The spatial heterogeneity of influencing factors and the interaction between factors were analyzed by using multi-scale geographic weighted regression model and geographic detector model, and the main influencing factors of heat island were detected. The study found that the spatial and temporal distribution of thermal environment in the core area of the capital has obvious spatial autocorrelation; the multi-scale geographically weighted regression model has high fitting accuracy and rich model interpretation information, and the model relaxes the broadband information of different factors. Geographic detector factor detection found that building density, night light and POI were the main influencing factors of the heat island in the core area of the capital, and the factor interaction analysis found that the single factor effect in the core area of the capital was more significant, and there was a weak interaction between the factors.