地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (10): 1298-1305.doi: 10.3724/SP.J.1047.2017.01298
李翔1,2(), 陈振杰1,2,*(
), 吴洁璇1,2, 汪文祥1,2, 曲乐安1,2, 周琛1,2, 韩肖锋3
收稿日期:
2017-02-24
修回日期:
2017-07-26
出版日期:
2017-10-20
发布日期:
2017-10-20
通讯作者:
陈振杰
E-mail:leexiang_nju@foxmail.com;chenzj@nju.edu.cn
作者简介:
作者简介:李 翔(1991-),男,河北邯郸人,硕士生,主要从事土地覆被变化方面研究。E-mail:
基金资助:
LI Xiang1,2(), CHEN Zhenjie1,2,*(
), WU Jiexuan1,2, WANG Wenxiang1,2, QU Lean1,2, ZHOU Chen1,2, HAN Xiaofeng3
Received:
2017-02-24
Revised:
2017-07-26
Online:
2017-10-20
Published:
2017-10-20
Contact:
CHEN Zhenjie
E-mail:leexiang_nju@foxmail.com;chenzj@nju.edu.cn
摘要:
精确掌握常住人口的数量和分布特征有助于明确社会发展情况、提高人口管理能力。目前人口数据主要以行政区为单元统计,难以表现城市内部的人口分布特征。然而,在城市中,受道路、公共服务设施、城市亮化灯光的影响,利用夜间灯光数据对人口回归,精度降低。如何提高城市常住人口回归结果的精度,值得深入研究。上海是中国的国家中心城市之一,在快速城镇化进程中上海面临巨大人口压力。因此,本文以上海市为研究区,以NPP-VIIRS (National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite)夜间灯光数据、乡镇级常住人口统计数据为基础,提取商业和居住区的灯光数据来缓解交通、城市亮化区的影响,提高灯光累计值与常住人口数的相关性(相关系数从0.7032提高至0.8026)。然后,本文用空间回归模型对上海市2013年常住人口进行回归,相对误差为10.57%,并对回归结果进行分乡(镇、街道)修正。实验结果表明,使用空间回归模型对常住人口回归可以取得较高的精度,且格网化结果能够弥补传统统计数据空间分辨率低的缺点,更加详细地刻画常住人口的圈层特征与真实分布情况。
李翔, 陈振杰, 吴洁璇, 汪文祥, 曲乐安, 周琛, 韩肖锋. 基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究[J]. 地球信息科学学报, 2017, 19(10): 1298-1305.DOI:10.3724/SP.J.1047.2017.01298
LI Xiang,CHEN Zhenjie,WU Jiexuan,WANG Wenxiang,QU Lean,ZHOU Chen,HAN Xiaofeng. Gridding Methods of City Permanent Population Based on Night Light Data and Spatial Regression Models[J]. Journal of Geo-information Science, 2017, 19(10): 1298-1305.DOI:10.3724/SP.J.1047.2017.01298
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