孟瑶, 李锐, 蒋捷, 王顺利, 吴华意. 基于建筑物信息的城市街道尺度人口估算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1194-1200. DOI: 10.13203/j.whugis20190343
引用本文: 孟瑶, 李锐, 蒋捷, 王顺利, 吴华意. 基于建筑物信息的城市街道尺度人口估算[J]. 武汉大学学报 ( 信息科学版), 2021, 46(8): 1194-1200. DOI: 10.13203/j.whugis20190343
MENG Yao, LI Rui, JIANG Jie, WANG Shunli, WU Huayi. Urban Street Scale Population Estimation Based on Building Information[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1194-1200. DOI: 10.13203/j.whugis20190343
Citation: MENG Yao, LI Rui, JIANG Jie, WANG Shunli, WU Huayi. Urban Street Scale Population Estimation Based on Building Information[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1194-1200. DOI: 10.13203/j.whugis20190343

基于建筑物信息的城市街道尺度人口估算

Urban Street Scale Population Estimation Based on Building Information

  • 摘要: 随着城镇化进程的加快与城市人口的迅速膨胀,街道尺度的人口数据在城市经济、社会、资源与环境发展等方面都发挥着愈发重要的作用。研究如何利用高分辨率遥感影像进行城市街道尺度上的人口估算,对促进城市可持续发展具有十分重要的理论意义与实际价值。利用遥感影像建筑物信息与人口普查数据,分析街道建筑物信息与人口数量之间的关联性,提出基于建筑物信息的城市街道尺度人口估算模型,通过多元逐步回归法与赤池信息准则确定建筑物显著特征变量,建立了建筑物数量、几何特征与人口数量的估算模型。实验证明,提出的人口估算模型能够以较高的精度估算街道尺度的人口数量。

     

    Abstract:
      Objectives  With the acceleration of urbanization and the rapid expansion of urban population, the population data on the street scale plays an important role in urban economic, social, resource and environmental development. Therefore, estimating population on urban street scales with high-resolution remote sensing images is of great theoretical and practical value for promoting sustainable urban development.
      Methods  This paper uses the building information in remote sensing images and census data to analyze the correlation between street building information and population data, and the city street scale population estimation models based on building information are proposed. The best estimation model for the number and geometric characteristics of buildings and population is established based on the multivariate stepwise regression method and the Akaike information criterion that determine the significant feature variables of the building.
      Results  The Experiments show that the population estimation model proposed in this paper can estimate the population of street scale with high accuracy. On the one hand, the floor data can be directly applied to the estimation model. On the other hand, the floor data is also the basis for classifying street buildings. Dividing street buildings into low-rise, middle-rise and high-rise buildings according to the number of floors can significantly improve the population estimation effect of the model.
      Conclusions  The population estimation model proposed in this paper can estimate the population of street scale with high accuracy. This paper uses street-scale data in administrative divisions, but the estimation range of the proposed model is theoretically also valid in population estimation across street areas, which needs to be verified in subsequent studies.

     

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