Statistical description of variables.

The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran’s I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development.