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基于NPP/VIIRS夜间灯光数据的黄淮海地区城乡常住人口格局模拟

Simulation of the Spatial Pattern of Population of Urban and Rural Residents in Huang-Huai-Hai Area Based on NPP/VIIRS Nighttime Lights

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【作者】 刘艳姣王介勇王志炜

【Author】 LIU Yanjiao;WANG Jieyong;WANG Zhiwei;Institute of Geographic Sciences and Natural Resources Research,CAS;University of Chinese Academy of Sciences;Shandong Provincial Department of Land & Resources;

【通讯作者】 王介勇;

【机构】 中国科学院地理科学与资源研究所中国科学院大学山东省土地综合整治服务中心

【摘要】 以黄淮海传统农区为研究区,利用NPP/VIIRS夜间灯光数据、土地利用数据和人口统计资料,基于城乡人口活动状态及其空间特征,构建城乡常住人口空间化模型,模拟区域城乡常住人口分布,探究城乡人口空间格局特征。结果表明:基于NPP/VIIRS夜间灯光数据和土地利用数据构建的人口空间化模型,拟合优度为0. 80,精度检验平均误差为15. 6%,模拟精度高,适用性较好;模拟结果能够直观反映黄淮海地区人口集疏特征及县域城乡常住人口分布差异特征,真实展现局地微观尺度城乡常住人口实际分布状况;黄淮海地区城乡常住人口在空间上呈现"总体差异显著、局地内部均衡"的特征,区域尺度上城市地区集聚显著,局地尺度上县域和乡镇内部人口分布相对均衡。模拟结果可为区域城镇村空间布局优化和城乡公共服务设施配置提供参考。

【Abstract】 Taking the traditional farming area in Huang-Huai-Hai area as the study area,a population spatial model was built by using the data of NPP/VIIRS night light,land use and demography to simulate the distribution of resident population in urban and rural area and explore the spatial distribution characteristics of urban and rural population. The result indicates that:( 1) The population spatialization model based on NPP/VIIRS night light data and land use data has a goddess of fit of 0. 80 and an average error of accuracy test is 15. 6%,which suggests high fitting degree and strong applicability.( 2) The spatial distribution pattern of population can directly reflect the characteristics of population agglomeration in Huang-Huai-Hai area and the differences of resident population in counties,as well as show the actual distribution of urban and rural resident population on the local scale;( 3) The resident population in Huang-Huai-Hai area presents a characteristics of "the overall difference is significant,the internal of local area is balanced"in space,manifesting population aggregation in urban and relatively balanced in county.

【基金】 中国科学院战略性先导科技专项(A类)(XDA19040402);国家自然科学基金面上项目(41671178)
  • 【文献出处】 地域研究与开发 ,Areal Research and Development , 编辑部邮箱 ,2019年03期
  • 【分类号】C924.2
  • 【被引频次】13
  • 【下载频次】1049
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