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Remote Sensing Technology and Application  2020, Vol. 35 Issue (1): 219-232    DOI: 10.11873/j.issn.1004-0323.2020.1.0219
    
Spatialization of China’s Population Data based on Multi-source Data
Yuchen Guo1,2,3(),Jinchuan Huang1,2,3(),Haoxi Lin1,2,3
1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. Key Laboratory of Regional Sustainable Development Analysis and Simulation, Chinese Academy of Sciences, Beijing 100101, China
3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

The population data is usually calculated by administrative districts. When comprehensive analysis is carried out with other spatial data, there are often many problems such as different scales. The spatial data of population data is the process of exploring the spatial distribution of population. The final result can be other spatial correlation research. It lays the foundation and can also be directly applied to disaster impact assessment and other fields, which has important academic significance and application value. The population spatialization model is based on the spatial relationship between population and land use and traffic road network data. The population is divided into urban population and rural population. The land use type is empowered by the relative weight method, and the traffic network data is used to correct it. Spatial distribution of rural population; analysis of different ways of affecting different levels of traffic road network data, comprehensive distribution of urban population, and finally through the planning and other methods to superimpose and integrate different types of data. The results of population data spatialization results show that China's population is very sensitive to altitude and topography. Low-altitude plains and terraces only account for 16.49% of China's land area, while the population living on it accounts for 54.88% of the total population. On the contrary, China's mountains The proportion of hills is 55.78%, but only 22.11% of the population is distributed. The eastern population is concentrated in the Huanghuaihai area, the Sichuan Basin, the Yangtze River small and medium-sized tourist, and the Northeast Plain. The urban population in China presents a typical “point-axis” distribution. The distribution along the main traffic lines is more prominent.

Key words:  Population data spatialization      Land cover      Traffic network      Related weights     
Received:  03 October 2018      Published:  01 April 2020
ZTFLH:  TP75  
Corresponding Authors:  Jinchuan Huang     E-mail:  guoyc.17s@igsnrr.ac.cn;huangjc@igsnrr.ac.cn
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Yuchen Guo
Jinchuan Huang
Haoxi Lin

Cite this article: 

Yuchen Guo,Jinchuan Huang,Haoxi Lin. Spatialization of China’s Population Data based on Multi-source Data. Remote Sensing Technology and Application, 2020, 35(1): 219-232.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.1.0219     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I1/219

Fig.1  Technology route of population data spatialization research
Fig.2  Identification between human activity area and no man’s area
Fig.3  Type division of no man’s area
序号 分区 序号 分区
1 东北区 7 黄土高原地区
2 黄淮海区 8 四川盆地区
3 长江中下游地区 9 云贵高原区
4 江南区 10 横断山区
5 华南区 11 西北区
6 内蒙古高原及长城沿线区 12 青藏高原区
Table 1  Twelve major agro-ecological zones in China
东北区 黄淮海区 长江中下游地区 江南区 华南区 内蒙古高原及长城沿线区 黄土高原地区 四川盆地区 云贵高原区 横断山区 西北区 青藏高原区
农村居民点 39.9 49.0 14.4 4.5 20.7 11.1 55.0 2.0 0.4 0.5 22.5 7.7
其他建设用地 0.5 2.1 1.0 8.1 1.9 8.7 3.3 14.6 0.5 0.2 12.5 3.8
旱地 37.2 36.1 29.2 18.5 18.4 32.0 18.1 22.4 51.5 17.2 25.2 26.1
水田 11.6 2.9 10.7 31.6 8.3 0.6 0.5 21.3 0.7

13.9

(22.6)

3.2 8.5
有林地 1.1 0.5 5.1 13.3 9.3 5.4 6.2 3.2 4.7

8.1

(13.2)

1.9 9.7
灌木林 0.5 0.8 3.9 1.9 4.6 24.1 0.7 8.1 15.7 11.0 2.1 2.5
疏林地 0.7 0.6 4.3 13.3 13.0 4.9 1.7 6.7 2.5

3.0

(5.0)

6.4 3.2
其他林地 0.8 1.1 5.5 3.5 10.9 4.2 4.2 15.4 1.9 9.8 14.3 10.9
高覆盖草地 2.8 2.6 9.6 2.7 6.5 4.0 2.0 1.3 1.8

4.3

(7.0)

3.8 11.0
中覆盖草地 4.0 2.4 9.1 1.1 3.9 2.6 3.7 4.1 17.6

16.0

(26.1)

5.5 8.8
低覆盖草地 1.0 1.8 7.2 1.4 2.5 2.3 4.8 0.9 2.7

16.0

(26.2)

2.5 7.7
Table 2  Matrix of partitioned land use’s weights
Fig.4  Cleveland point map of land use type’s weight
Fig.5  Network density
Fig.6  Urban land area and distance from national highway
距离 回归公式 R Square P-value
<20 km l n ? y = - 0.1471 x + 8.6643 0.899 2.2008E-10***
<10 km l n ? y = - 0.2518 x + 9.1703 0.978 6.39126E-08***
Table 3  Results of ln (town land area) and distance interval median regression
距离区间 距离区间中值xi | d y d x ( x = x i ) |
0 ~ 1 km 0.5 2133.0
1 ~ 2 km 1.5 1658.2
2 ~ 3 km 2.5 1289.1
3 ~ 4 km 3.5 1002.1
4 ~ 5 km 4.5 779.1
5 ~ 6 km 5.5 605.6
6 ~ 7 km 6.5 470.8
7 ~ 8 km 7.5 366.0
8 ~ 9 km 8.5 284.5
9 ~ 10 km 9.5 221.2
Table 4  Weights in different distance intervals
Fig.7  Spatial population data of national kilometer grid
自由度 总方差和 平均方差和 F value Pr(>F)
土地覆盖 发展阶段分组 3 15 995 5 332 0.728 0.536
残差 339 2 481 619 7 320
县道 发展阶段分组 3 370 548 123 516 14.53 6.30E-09***
残差 339.0 2 881 140 8 499
Table 5  One-way analysis of variance of rural population estimation error
发展阶段 观测值 土地覆盖系数 道路网络系数
城市化成熟区 31 0.959 0.041
城市化发展区 143 0.569 0.431
城市化提速区 150 0.584 0.416
城市化起步区 19 0.238 0.762
Table 6  Analysis of the proportion of rural population integration
自由度 总方差和 平均方差和 F value Pr(>F)
城市主干道 发展阶段分组 3 4 240 560 1 413 520 16.02 9.20E-10***
残差 339 29 910 800 88 232
国道 发展阶段分组 3 290 854 96 951 5.54 0.001 01**
残差 339 5 932 187 17 499
Table 7  One-way analysis of variance of urban population estimation error
R Square 城市主干道独立解释份额 国道独立解释份额
城市化成熟区 0.673 44.19 55.81
城市化发展区 0.746 31.39 68.61
城市化提速区 0.658 32.47 67.53
城市化起步区 0.715 46.16 53.84
Table 8  Interpretation power weight of the two types of road
交通通道两侧距离/km 面积占比/% 人口占比/%
5 1.08 7.07
10 2.17 12.95
15 3.27 17.12
20 4.38 20.22
Table 9  Population distribution on both sides of the main traffic corridor
地形 面积占比/% 人口占比/%
低海拔平原 11.48 43.56
中海拔平原 6.82 4.90
高海拔平原 2.99 0.80
极高海拔平原 0.94 1.26
低海拔台地 5.11 11.33
中海拔台地 2.45 1.50
高海拔台地 1.20 0.35
极高海拔台地 0.71 0.79
低海拔丘陵 6.67 6.31
中海拔丘陵 8.79 1.50
高海拔丘陵 1.23 0.17
极高海拔丘陵 1.05 0.45
山地 38.03 13.67
Table 10  Demographic results of different terrains across the country based on population spatialization data
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