Journal of Geo-information Science ›› 2017, Vol. 19 ›› Issue (10): 1298-1305.doi: 10.3724/SP.J.1047.2017.01298

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Gridding Methods of City Permanent Population Based on Night Light Data and Spatial Regression Models

LI Xiang1,2(), CHEN Zhenjie1,2,*(), WU Jiexuan1,2, WANG Wenxiang1,2, QU Lean1,2, ZHOU Chen1,2, HAN Xiaofeng3   

  1. 1. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing 210023, China
    2. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China
    3. Bureau of land and resources of Nanjing, Nanjing 210005, China
  • 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

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.

Key words: night light data, permanent population, gridding, spatial regression models, Shanghai