| 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 |
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| 引用本文： | 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,2017,19(10):1298-1305. |
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| 作者姓名： | 李翔 陈振杰 吴洁璇 汪文祥 曲乐安 周琛 韩肖锋 |
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| 作者单位： | 1. 江苏省地理信息技术重点实验室,南京 2100232. 南京大学地理信息科学系,南京 2100233. 南京市国土资源局,南京 210005 |
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| 基金项目： | 国家自然科学基金项目（41571378）;中国土地勘测规划院外协项目（2016-63-3） |
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| 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。
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| 关 键 词： | 夜间灯光数据 常住人口 格网化 空间回归模型 上海市 |
| 收稿时间： | 2017-02-24 |
Gridding Methods of City Permanent Population Based on Night Light Data and Spatial Regression Models |
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| 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].Geo-information Science,2017,19(10):1298-1305. |
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| Authors: | LI Xiang CHEN Zhenjie WU Jiexuan WANG Wenxiang QU Lean ZHOU Chen HAN Xiaofeng |
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| Institution: | 1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China2. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China3. Bureau of land and resources of Nanjing, Nanjing 210005, China |
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| Abstract: | It is important to acquire the amount and the spatial distribution features of permanent population accurately, which can be used to clarify the development of social state. Thus, it would enhance the capacity of population management. Currently, population census data is mainly collected in administrative regions, making it difficult to describe the spatial distribution features of population in cities. Moreover, the precision decreases when using night light data to regress population, and it is clearly affected by roads, public service facilities and the lights of the cities. Therefore, it is necessary to improve the precision of population regression. This study takes Shanghai as the study area because it is one of the national center cities and faced with huge population pressure along with the rapid urbanization processes. Two types of data sources are involved in the study, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP -VIIRS) night light data and township-level permanent population census data. We extracted the night light data in commercial and residential land in order to mitigate the influence of roads and the lights of the city. Results showed that the correlation coefficient between summation of night light data and amount of permanent population was improved from 0.7032 to 0.8026. Further, we used a spatial regression model to derive the permanent population of Shanghai in 2013, and found that the relative error is 10.57%. Finally, we corrected the results in partition. Experimental results of high precision can be achieved when spatial regression model was used to regress permanent population. Moreover, the gridding results of permanent population can make up the shortcoming of low spatial resolution of traditional statistical data, and describe the circle feature and real distribution of permanent population with more details. |
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| Keywords: | night light data permanent population gridding spatial regression models Shanghai |
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