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基于多源数据融合的北京市人口时空动态分析
引用本文:崔晓临,张佳蓓,吴锋,张倩,吴尧慧.基于多源数据融合的北京市人口时空动态分析[J].地球信息科学,2020,22(11):2199-2211.
作者姓名:崔晓临  张佳蓓  吴锋  张倩  吴尧慧
作者单位:1.西安科技大学测绘科学与技术学院,西安 7100542.中国科学院地理科学与资源研究所,陆地表层格局与模拟院重点实验室,北京 1001013.中国农业大学土地科学与技术学院,北京 1001934.中国矿业大学(北京)地球科学与测绘工程,北京 100083
基金项目:国家自然科学基金项目(71774151);北京市朝阳区社会发展科技计划项目(CYSF1906);北京市自然科学基金项目(9172018)
摘    要:高精度、动态、空间化的人口数据是科学核算城市资源环境压力、促进人口合理调控的重要参考依据。研究首先以2012和2017年北京市常住人口统计信息为基础,融合夜间灯光、土地利用、道路网络等数据,依据用地类型、VANUI指数对街道分割成的区域进行分类和分级,并在此基础上构建人口空间化模型,制备2期100 m格网尺度的人口空间化数据。验证表明,模型误差在10%以内;与已有成果对比分析发现,人口空间化数据整体与局部精度均有提升。最后,基于该数据分析北京市人口分布的时空动态变化特征,揭示其影响因素。结果表明:① 格网单元上的人口平均数量在-2542~190之间变化,以-500~500人为主。② 2期人口空间分布均呈“内密外疏”格局,且减少人口主要聚集于城六区,2012—2017年间减少近21万人;③ 首都功能核心区减少人口最为显著,减少量占城六区减少量的62%;④ 二三环间减少人口最多,共减少近11万人,减少量占城六区减少量的52%;⑤ 与城六区接壤的城郊街道,人口呈现增加趋势,可能形成新的人口聚集区。北京市人口时空变化特征与首都功能定位、产业升级转型、人口疏解政策实施等因素密切相关,该研究可为北京市未来人口空间合理布局与人口疏解政策完善提供定量化的科学依据。

关 键 词:人口空间化  格网尺度  时空动态  多源数据  夜间灯光  空间分析  人口疏解  北京市  
收稿时间:2019-12-24

Spatio-temporal Analysis of Population Dynamics based on Multi-source Data Integration for Beijing Municipal City
CUI Xiaolin,ZHANG Jiabei,WU Feng,ZHANG Qian,WU Yaohui.Spatio-temporal Analysis of Population Dynamics based on Multi-source Data Integration for Beijing Municipal City[J].Geo-information Science,2020,22(11):2199-2211.
Authors:CUI Xiaolin  ZHANG Jiabei  WU Feng  ZHANG Qian  WU Yaohui
Institution:1. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China2. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China3. College of Land Science and Technology, China Agricultural University, Beijing 100193, China4. School of Geosciences & Surveying Engineering, China University of Geosciences, Beijing 100083, China
Abstract:High-precision spatially-explicit population data performs a quantitative reference for evaluating urban resources and environment pressure and promoting a rational population distribution. This study first classified and ranked street blocks of Beijing based on land use categories and VANUI index. Based on this, a hierarchical population spatialization model was built to generate the spatial distribution of population at 100 m resolution. In addition, Beijing permanent resident demographic information of 2012 and 2017, NPP/VIIRS nighttime lights data, land use, road networks, and other auxiliary data were also used as model inputs. In our study, the model simulation error against the verified data was less than 10%. Compared with other published results, the population distribution result generated in this study had a higher overall accuracy and local accuracy. We further analyzed the spatio-temporal pattern of population in Beijing and its impact factors. Results show that the population of Beijing in each 100 m grid varied from -2564 to 1904, with -500~500 being the main change level. The spatial patterns of population in 2012 and 2017 both demonstrated that central Beijing was densely populated while Beijing suburb was sparsely populated. Between these two years, population of Beijing declined by approximately 210,000, which mainly happened in six main districts. The core functional area of Beijing had a remarkable reduction in population, accounting for 62% of the total population decline within the six districts of the city. In addition, population between the second and third ring of Beijing decreased the most, with nearly 110 000 people moved out, accounting for 52% of the population decline within the six districts. On the contrary, the population increased in the surrounding street blocks at the border of the six districts, which might form new population centers in the future. The spatial and temporal dynamics of Beijing's population were closely related to factors, such as the functional orientation of the capital, industrial upgrading and transformation, and the implementation of population redistribution policies. This study provides a scientific reference for the rational layout of Beijing's population space and formulation of Beijing's population redistribution policies in the future.
Keywords:population spatialization  grid scale  spatio-temporal dynamics  multi-source data  nighttime light  spatial analysis  population redistribution  Beijing  
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