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Remote Sensing Technology and Application  2022, Vol. 37 Issue (4): 929-937    DOI: 10.11873/j.issn.1004-0323.2022.4.0929
    
Analysis of Inequality of Socioeconomic Development on both Sides of Hu Huanyong Line Using Nighttime Light
Dan Zou1(),Yuke Zhou2(),Jintang Lin1,Tianyu Chen3,Zhijie Wu1,Hong Wang1
1.School of Resource Engineering，Longyan University，Longyan 364012，China
2.Key Laboratory of Ecosystem Network Observation and Modeling，Institute of Geographic and Nature Resources Research，Chinese Academy of Sciences，Beijing 100101，China
3.School of Geographic Science and Surveying engineering，Suzhou University of Science and Technology，Suzhou 215004，China
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Abstract  

Using remote sensing technology to evaluate the social and economic development situation and differences between East and west China is of great significance for China to formulate development strategies and implement them. In this paper， we use remote sensing-derived nighttime light data to characterize the social and economic development， and analyze the development rate and gravity center transfer of East and West （on both sides of Hu Huanyong line） at the county level. Combined with remote sensing vegetation index， the ratio index of "light/vegetation" is introduced to analyze the dynamic trade-off between economic development and green space. The proportion of light in different distance buffer zone of coastal zone was compared with that in the West. Gini coefficient is used to measure the unbalanced development of the East and the West. The results show that： with the rapid development of social economy in the whole country， the East and the west， the lighting center is basically stable， drifting in a small range in Kaifeng City， Huaibei City and the south of Alashan； The coastal zone of China has gathered high-intensity social and economic activities， and the total amount of light in the 30 km buffer zone is almost equal to that in the West； The Gini coefficient in the East and West decreased year by year. The spatial correlation analysis of the ratio index shows that the areas along the Bohai Sea， Yellow Sea and East China Sea are high-intensity development areas tending to be saturated， the adjacent inland counties are potential high-intensity development areas， and the Qinghai Tibet and southwest regions are weak development areas. The results show that the internal economic development difference between the East and the west is still significant， but the balance is getting better. The eastern development should pay more attention to the maintenance of greening trend. Affected by the spillover of coastline development results， the inland areas will gradually enter the stage of rapid development. The conclusions of this study can be used for reference in accurate identification of key areas of Rural Revitalization and ecological restoration planning in China.
Key words:  Nighttime light      Hu huanyong line      Development inequality      Gini coefficient      Coastal zone     
Received:  24 June 2021      Published:  28 September 2022
:  F24  
Corresponding Authors:  Yuke Zhou     E-mail:  chinazoudan@126.com;zhouyk@igsnrr.ac.cn
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Dan Zou
Yuke Zhou
Jintang Lin
Tianyu Chen
Zhijie Wu
Hong Wang

Cite this article: 

Dan Zou,Yuke Zhou,Jintang Lin,Tianyu Chen,Zhijie Wu,Hong Wang. Analysis of Inequality of Socioeconomic Development on both Sides of Hu Huanyong Line Using Nighttime Light. Remote Sensing Technology and Application, 2022, 37(4): 929-937.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.4.0929     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I4/929

Fig.1  Spatial distribution of multi-year average night light and NDVI data (1992—2015)
Fig.2  Analysis of the multi-year trend of nighttime light and NDVI in China, east and west of Hu Line
Fig.3  Trajectories of economic center of gravity in China, the east and the West
Table 1  Comparative analysis table of coastal zone lights and western county lights of Hu Line
Fig.4  Change curve of Gini coefficient
Fig.5  Change slope of NTL/NDVI
Fig.6  NTL/NDVI index aggregation and hot/cold spot distribution
1 Wu Ruijun， Zhu Baoshu. The unbalanced distribution of population in China and the stability of Heihe-Tengchong Line［J］. Chinese Journal of Population Science， 2016，36（1）：14–24.
1 吴瑞君， 朱宝树. 中国人口的非均衡分布与“胡焕庸线”的稳定性［J］. 中国人口科学，2016，36（1）：14–24.
2 Zhong Maochu. How to characterize regional ecological carrying capacity and ecological environment quality？： on the redistribution of the ecological carrying capacity of the East， Central and West with the meaning of Hu Huanyong Line［J］. Journal of China University of Geosciences （Social Sciences Edition）， 2016， 16（1）： 1–9.
2 钟茂初. 如何表征区域生态承载力与生态环境质量？——兼论以胡焕庸线生态承载力涵义重新划分东中西部［J］. 中国地质大学学报： 社会科学版， 2016， 16（1）： 1–9.
3 Wang Kaiyong， Deng Yu . Can new urbanization break through the Hu Huanyong Line？ Further discussion on the geographical connotations of the Hu Huanyong Line［J］. Geographical Research， 2016， 35（5）： 825–835.
3 王开泳， 邓羽. 新型城镇化能否突破 “胡焕庸线”——兼论 “胡焕庸线” 的地理学内涵［J］. 地理研究， 2016， 35（5）： 825–835.
4 Li Jiaming， Lu Dadao， Xu Chengdong， et al. Spatial heterogeneity and its changes of population on the two sides of Hu Line［J］. Acta Geographica Sinica， 2017， 72（1）： 148-160.
4 李佳洺， 陆大道， 徐成东，等. 胡焕庸线两侧人口的空间分异性及其变化［J］. 地理学报， 2017， 72（1）： 148-160.
5 Chen Mingxing， Li Yang， Gong Yinghua， et al. The population distribution and trend of urbanization pattern on two sides of Hu Huanyong population line： A tentative response to Premier Li Keqiang［J］. Acta Geographica Sinica， 2016，71（2）： 179-193.
5 陈明星， 李扬， 龚颖华，等. 胡焕庸线两侧的人口分布与城镇化格局趋势——尝试回答李克强总理之问［J］. 地理学报， 2016，71（2）：179-193.
6 Qi Wei， Liu Shenghe， Zhao Meifeng. Study on the stability of Hu Line and different spatial patterns of population growth on its both sides［J］. Acta Geographica Sinica， 2015， 70（4）： 551-566.
6 戚伟， 刘盛和， 赵美风. “胡焕庸线” 的稳定性及其两侧人口集疏模式差异［J］. 地理学报， 2015， 70（4）： 551-566.
7 Dou Y， Liu Z， He C， et al. Urban land extraction using VIIRS nighttime light data： An evaluation of three popular methods［J］. Remote Sensing，2017，9（2）. DOI： .
doi: 10.3390/rs 9020175
8 Ma T， Zhou C， Pei T， et al. Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data： A comparative case study from China’s cities［J］. Remote Sensing of Environment， 2012， 124： 99–107. DOI： .
doi: 10.1016/j.rse.2012.04.018
9 Liu Y， Wang Y， Peng J， et al. Correlations between urbanization and vegetation degradation across the world’s metropolises using DMSP/OLS nighttime light data［J］. Remote Sensing， 2015， 7（2）： 2067–2088. DOI： .
doi: 10.3390/rs70202067
10 Li X， Xu H， Chen X， et al. Potential of NPP-VIIRS nighttime light imagery for modeling the regional economy of China［J］. Remote Sensing， 2013， 5（6）： 3057–3081. DOI： .
doi: 10.3390/rs5063057
11 ShiK， Yu B， Huang Y， et al. Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales： A comparison with DMSP-OLS data［J］. Remote Sensing， 2014， 6（2）： 1705–1724. DOI： .
doi: 10.3390/rs6021705
12 Li X， Chen F， Chen X. Satellite-observed nighttime light variation as evidence for global armed conflicts［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing，2013，6（5）：2302-2315. DOI： .
doi: 10.1109/JSTARS. 2013.2241021
13 Jiang W， He G， Long T， et al. Ongoing conflict makes Yemen dark： From the perspective of nighttime light［J］. Remote Sensing， 2017， 9（8）. DOI： .
doi: 10.3390/rs9080798
14 DaiZ， Hu Y， Zhao G. The suitability of different nighttime light data for GDP estimation at different spatial scales and regional levels［J］. Sustainability （Switzerland）， 2017， 9（2）. DOI： .
doi: 10.3390/su9020305
15 Ji X， Li X， He Y， et al. A simple method to improve es-timates of county-level economics in China using night-time light data and GDP growth rate［J］. ISPRS International Journal of Geo-Information，2019，8（9）. DOI： .
doi: 10.3390/ijgi 8090419
16 Wu J， Wang Z， Li W， et al. Exploring factors affecting the relationship between light consumption and GDP based on DMSP/OLS nighttime satellite imagery［J］. Remote Sensing of Environment，2013，134：111-119. DOI： .
doi: 10.1016/j.rse. 2013.03.001
17 Liu J， DengY， Wang Y， et al. Urban nighttime leisure space mapping with nighttime light images and POI data［J］. Remote Sensing， 2020， 12（3）. DOI： .
doi: 10.3390/rs12030541
18 He X， Zhou C， Zhang J， et al. Using wavelet transforms to fuse nighttime light data and POI big data to extract urban built-up areas［J］. Remote Sensing， 2020， 12（23）： 1–20. DOI： .
doi: 10.3390/rs12233887
19 Shi K， Chang Z， Chen Z， et al. Identifying and evaluating poverty using multisource remote sensing and Point Of Interest （POI） data： A case study of Chongqing， China［J］. Journal of Cleaner Production，2020：255. DOI： .
doi: 10.1016/j.jclepro. 2020. 120245
20 Li Tingting. Study on temporal and spatial evolution of urban green space and its correlation with ecological environment effect［D］. Chongqing： Southwest University， 2018.
20 李婷婷. 城市绿色空间时空演变及其对生态环境效应的关联性研究［D］. 重庆： 西南大学， 2018.
21 Su Lei， Li Junying， Fan Mengxue. Review of urban green space spatial-temporal evolution and its ecological effects based on remote sensing［J］. Yunan Geographic Environment Research， 2018，30（6）：1-8.
21 苏雷，李俊英，樊梦雪.基于遥感的城市绿色空间时空演变与生态效应研究综述［J］.云南地理环境研究，2018，30（6）：1-8.
22 Max Finlayson C. Millennium ecosystem assessment［J］. The Wetland Book： I： Structure and Function， Management， and Methods，2018：355-359. DOI： .
doi: 10.1007/978-90-481-9659-3_81
23 Mulkey D， Hackett W B， Gordon J R. Assessing economic activity in coastal areas： A shift‐share approach［J］. Coastal Zone Management Journal， 1982， 10（1–2）： 79–96. DOI： .
doi: 10.1080/08920758209361911
24 Li X， Zhou Y， Zhao M， et al. A harmonized global nighttime light dataset 1992-2018［J］. Scientific Data， 2020，7（1）：1-9. DOI： .
doi: 10.6084/m9.figshare.12312125
25 Zhao M， Cheng W， Zhou C， et al. Assessing spatiotemporal characteristics of urbanization dynamics in Southeast Asia using time series of DMSP/OLS nighttime light data［J］. Remote Sensing， 2018， 10（1）. DOI： .
doi: 10.3390/rs10010047
26 Pinzon J E， Tucker C J. GIMMS 3g NDVI set and global NDVI trends［J/OL］. Second Yamal Land-Cover Land-Use Change Workshop， 2010（March）. .
27 Anselin L. Local Indicators of Spatial Association—LISA［J］. Geographical Analysis， 1995，27（2）：93-115. DOI： .
doi: 10.1111/j.1538-4632.1995.tb00338.x
28 Zhang C， Luo L， Xu W， et al. Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway， Ireland［J］. Science of the Total Environment， 2008， 398（1-3）： 212-221. DOI： .
doi: 10.1016/j.scitotenv.2008.03.011
29 Gastwirth J L. The Estimation of the Lorenz Curve and Gini Index［J］. The Review of Economics and Statistics， 1972， 54（3）： 306. DOI： .
doi: 10.2307/1937992
30 Wang X， Xiao J， Li X， et al. No trends in spring and autumn phenology during the global warming hiatus［J］. Nature Communications， 2019， 10（1）： 2389. DOI： .
doi: 10.1038/s41467-019-10235-8
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