Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

中文翻译：

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基于网格Voronoi方法的移动电话和建筑物使用数据的细尺度对称人口映射

精细的人口图对于捕捉城市人口的时空分布具有重要意义。与传统的人口普查数据相比，从手机数据获得的人口数据具有高可用性和实时性。然而，基站的空间分布不均匀，服务边界仍然不确定，这给大容量人口映射的准确性提出了重大挑战。本文提出了一种网格Voronoi方法，为基站提供可靠的空间边界，并基于手机和建筑使用数据构建后续回归。结果表明，网格Voronoi方法在建筑物使用回归中具有很高的适应性，并将传统的普通最小二乘（OLS）回归模型与地理加权回归（GWR）模型进行进一步比较表明，建筑使用数据可以很好地反映城市地理空间的异质性。该方法基于移动电话和建筑物使用数据，为捕获高精度人口分布提供了相对方便和可靠的想法。