人为热通量（Anthropogenic Heat Flux, AHF）是单位时间内单位面积上产生的人为热排放总量。研究AHF的时空分布，有助于理解城市热环境的形成与变化，也对缓解和调节城市生态环境问题具有重要的理论和实际意义。为获取区域范围少量样本情形下人为热排放的空间分布并开展关中平原城市群人为热排放的时空演化特征分析，本研究首先采用修正后的源清单法估算地级市人为热通量，再结合POI数据、夜间灯光数据、建筑高度数据以及人口分布数据等多源空间数据通过多元线性回归构建不同类型人为热排放估算模型，获取了2016-2021年关中平原城市群空间分辨率为500米的逐年人为热排放数据，并开展了人为热排放时空特征分析。研究结果表明：(1)多元线性回归在AHF格网化中具有较高的可行性，其获得的拟合模型拟合精度较高，决定系数(R2)均超过0.9，其中建筑AHF的拟合模型精度最高，R2达0.98；(2)POI参与人为热的格网化分配能够很好地体现出不同类型人为热排放的空间异质性，是估算不同热源AHF空间分布的重要数据源；(3)关中平原城市群人为热通量空间分布不均匀，在经济发达、地形平坦、城市化程度高的城市群中心区域形成高值区，在时间上其总体呈现上涨趋势。研究获得的人为热排放空间分布数据可以为城市热环境评估和城市热舒适度模拟提供数据参考。

Anthropogenic Heat Flux (AHF) refers to the total amount of human-generated heat emissions per unit area within a unit of time. As a key factor in the formation of the urban heat island (UHI) effect, anthropogenic heat emissions significantly influence urban thermal environments by directly releasing waste heat into the atmosphere through human activities. Therefore, studying the spatiotemporal distribution of AHF helps to understand the formation and evolutyion of urban thermal environments, and holds important theoretical and practical significance for mitigating and regulating urban ecological issues. In order to obtain the spatial distribution of anthropogenic heat emissions under limited sample data at a regional scale and to analyze the spatiotemporal evolution characteristics of different types of AHFs in the Guanzhong Plain urban agglomeration in China, this study first employed a modified emission inventory method to estimate the AHFs of prefecture-level cities. The modification primarily addresses the overestimation of residential building heat emissions by excluding private vehicle energy consumption, which is already accounted for in residential energy use. Additionally, it refines the calculation of transportation AHF by incorporating heat emissions from public transportation. Subsequently, using multi-source spatial data, including point of interest (POI) data, nighttime light data, building height data from the Global Human Settlement Layer (GHSL), and population distribution data from WorldPop, a multivariate linear regression model was constructed to estimate different types of anthropogenic heat emissions. This approach enabled the acquisition of annual anthropogenic heat emission data from 2016 to 2021 for the Guanzhong Plain urban agglomeration at a 500-meter spatial resolution, followed by a spatiotemporal analysis of emission characteristics. The study results show that: (1) Multivariate linear regression is highly feasible for AHF gridding, as the fitted models achieve high accuracy, with R2 values all exceeding 0.9. Among them, the building AHF model has the highest accuracy, with an R2 of 0.98. (2) POI data contributes significantly to the gridded allocation of anthropogenic heat, effectively reflecting the spatial heterogeneity of different types of anthropogenic heat emissions. This makes it an important data source for estimating the spatial distribution of AHF from various heat sources. (3) The spatial distribution of AHF in the Guanzhong Plain urban agglomeration is uneven, with high-value areas concentrated in economically developed, flat, and highly urbanized central regions of the urban agglomeration, particularly in the northern central urban area of Xi’an. Temporally, AHF exhibits an overall upward trend. The spatiotemperal distribution data of anthropogenic heat emissions obtained from this study can provide valuable data references for urban thermal environment assessments and urban thermal comfort simulations. And this information can further support the development of targeted heat mitigation strategies, contributing to more sustainable urban planning and improved living environments.