基于腾讯位置大数据的精细尺度人口空间化——以南京市江宁区秣陵街道为例  

Fine-Scale Population Spatialization Based on Tencent Location Big Data: A Case Study of Moling Subdistrict,Jiangning District,Nanjing

作　　者：吴中元 许捍卫[1] 胡钟敏 WU Zhong-yuan;XU Han-wei;HU Zhong-min(College of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学地球科学与工程学院

出　　处：《地理与地理信息科学》2019年第6期61-65,共5页Geography and Geo-Information Science

基　　金：国家自然科学基金项目(41101374)

摘　　要：精细尺度的人口空间化研究成为当前GIS领域研究的热点。已有的人口空间化方法大多针对区域或城市尺度进行研究,少有对街道、社区甚至住宅小区的亚城市单元人口的研究。该文以南京市江宁区秣陵街道为例,基于腾讯位置大数据,结合人口统计数据、建筑物空间属性数据和住宅小区边界数据,提出了基于腾讯位置大数据的人口空间化方法和住宅小区级别的精细尺度人口估算方法。研究结果表明,该方法在住宅小区空间尺度下的估算结果与实际人口的线性拟合R^2达到0.9494,结果可信度较高,可为今后精细尺度人口空间化研究提供参考。With the rapid development of GIS technology,research about the fine-scale population distribution has been paid more and more attention in the GIS field.Most of the existing methods of population spatialization focus on regional or urban scales,while few sub-city populations such as subdistricts,communities,and even residential communities are studied.Taking the Moling Subdistrict in Jiangning District of Nanjing as an example,this paper proposes a population spatialization method and a fine-scale population estimation of residential level based on Tencent location big data,which combined with census data,the bottom of the study area and the number of layers and the boundary data of the residential area of the study area.A linear relationship is obtained between the estimated population by this method and the actual population at the spatial scale of the residential area with the linear correlation coefficient(R^2)of 0.9494.The results show that the reliability of the estimation results is high,which can provide a reference value for the future research of fine-scale population spatialization.

关 键 词：腾讯位置大数据 人口格网 居住空间数据 人口估算模型 

分 类 号：K901.3[历史地理—人文地理学]