Dasymetric areal interpolation is the process by which data are transferred from a spatial unit system for which they are available (source units) to another system for which they are required (target units) with the aid of ancillary information (control units). We propose a spatially disaggregated areal interpolation model for population data using light detection and ranging (LiDAR)‐derived building volumes as an ancillary variable. Innovative methods are proposed for model initialization, iterative regression and adjustment, and stopping criteria to deal effectively with control units of unequal size. The model is derived and applied at the control unit level to minimize the modifiable areal unit problem, and an iterative adjustment process is utilized to overcome the spatial heterogeneity problem encountered in earlier approaches. The use of building volume to disaggregate the population into finer scales ensures maximum correspondence with the unit at which the original population data were collected and models not only the horizontal but also the vertical population distribution. A case study for Round Rock, Texas, demonstrates that the proposed spatially disaggregated model using LiDAR‐derived building volumes outperforms earlier areal interpolation models using traditional area‐ and length‐based ancillary variables. 密度区域插值是在辅助信息的帮助下（控制元），从一个可测空间单元系统（源单元）转换至所需空间单元系统（目标元）的过程。本文提出了一个利用激光雷达（LiDAR）测量建筑物体积作为辅助变量的人口数据空间分解区域插值模型（SDAIM），一系列创新方法用于模型初始化、迭代回归和调整方法，并给出了可有效处理不等大小控制单元的停止准则。该模型从控制单元层次来最小化可变区域单元问题（MAUP），并通过迭代调整过程来克服现有方法中的空间异质性问题。利用建筑物体积将从更精细尺度进行人口空间分解，保证了原始人口数据与模型的符合度达到最大，且模型可同时反映人口的水平与垂直分布特征。以德克萨斯州的Round Rock城市为例，验证了SDAIM方法优于已有基于面积和长度辅助变量的区域插值方法。