Data sources.

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, the Urban Heat Island (UHI) effect in cities has intensified year by year, jeopardizing the foundation of sustainable urban development. Prudent urban spatial planning holds the potential to robustly ameliorate the persistent deterioration of the UHI phenomenon. This study selects Jinan City as a case study and employs spatial autocorrelation and spatial regression algorithms to explore the spatiotemporal evolution of urban-rural patterns at the township scale. The aim is to identify key factors driving the spatiotemporal differentiation of Land Surface Temperature (LST) from 2013 to 2022. The research reveals a trend of initially rising and subsequently falling LST in various townships, with low-temperature concentration areas in the southern mountainous region and the northern plain area. The "West-Central-East" main urban axis and the southeast Laiwu District exhibit high-temperature zones. Significant influences on LST are attributed to pollution levels, topographical factors, urbanization levels, and urban greenness. The global Moran’s Index for LST exceeds 0.7, indicating a strong positive spatial correlation. Cluster analysis results indicate High-High (HH) clustering in the central Shizhong District and Low-Low (LL) clustering in the northern Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms Geographically Weighted Regression (GWR) and Ordinary Linear Regression (OLR), providing a more accurate reflection of the regression relationships between variables. By investigating the spatiotemporal evolution of LST and its driving factors at the township scale, this study contributes insights for future urban planning and sustainable development.

长期依赖工业发展的模式加剧了污染物的累积效应。与此同时，受土地建设活动与绿地缩减的双重影响，城市热岛效应（Urban Heat Island, UHI）逐年加剧，严重危及城市可持续发展的根基。科学审慎的城市空间规划，有望显著缓解城市热岛效应的持续恶化态势。本研究以济南市为案例区域，采用空间自相关与空间回归算法，探究乡镇尺度下城乡格局的时空演变规律，旨在识别2013年至2022年间地表温度（Land Surface Temperature, LST）时空分异的关键驱动因子。研究结果显示，各乡镇的地表温度整体呈现先升后降的变化趋势；南部山区与北部平原区域为低温聚集区，而“西-中-东”城市主轴线与东南部莱芜区则为高温聚集区。影响地表温度的核心因子包括污染水平、地形因素、城市化水平与城市绿化程度。地表温度的全局莫兰指数超过0.7，表明其存在较强的正向空间相关性。聚类分析结果表明，市中心中区呈现高-高（High-High, HH）聚集特征，北部商河县呈现低-低（Low-Low, LL）聚集特征。多尺度地理加权回归（Multiscale Geographically Weighted Regression, MGWR）的模型性能优于地理加权回归（Geographically Weighted Regression, GWR）与普通线性回归（Ordinary Linear Regression, OLR），能够更精准地刻画变量间的回归关系。本研究通过探究乡镇尺度下地表温度的时空演变规律及其驱动因子，可为未来城市规划与可持续发展提供切实可行的参考依据。