建筑尺度的精细化人口分布数据在城市规划、灾害防治等多个领域具有重要作用。近年来，遥感（RS）与地理信息系统（GIS）技术的快速发展为人口分布制图研究提供了有力支持。然而，大多数研究仍聚焦于全球尺度的人口与环境变化，较少关注局部尺度的精细化人口制图，主要原因在于缺乏可靠的数据与模型。随着地理空间大数据的兴起，互联网获取的志愿地理信息（VGI） now 可用于解决这一问题。本文提出一种新型框架，通过整合多源地理空间大数据，实现城市建筑尺度的人口分布制图，对精细化人口分布制图具有重要意义。首先，利用随机森林算法分析百度兴趣点（POI）和腾讯实时用户密度（RTUD），将街道尺度的人口分布下放至网格尺度；其次，设计一种高效的迭代建筑-人口引力模型，实现建筑尺度的人口分布制图。同时，引入由该引力模型生成的高密度居住指数（DII），可用于估算居民聚集程度。与官方社区级普查数据及以往人口制图方法的结果对比表明，本方法精度最高（皮尔逊相关系数 R = .8615，均方根误差 RMSE = 663.3250，p < .0001）。生成的精细化人口分布图可为理解城市内部人口分布提供更全面的视角，有助于政策制定者优化资源配置。

Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.