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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 184-193     DOI: 10.6046/zrzyyg.2020340
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Impacts of COVID-19 epidemic on the spatial distribution of GDP contributed by secondary and tertiary industries in Guangdong Province in the first quarter of 2020
WANG Zheng1,2(), JIA Gongxu1,2(), ZHANG Qingling1,3, HUANG Yue1,2
1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Aeronautics and Astronautics, Sun Yat-sen University, Guangzhou 510275, China
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Abstract  

Gross Domestic Product (GDP) is commonly regarded as the best measure of a country's economic health. In 2020, China suffered from a relatively serious COVID-19 epidemic, which had a huge impact on economic development. This paper aims to accurately analyze the spatial and temporal variation pattern of the GDP contributed by the second and tertiary industries in Guangdong Province, China in the first quarter under the background of the epidemic. To this end, the remote sensing data of night-time light was taken as an indicator of GDP contributed by the secondary and tertiary industries (GDP 23). By combining the real-time monitoring data of the epidemic and point of interest (POI) data of Guangdong Province, the authors firstly determined that the epidemic was the factor that caused the decrease in urban total night light intensity (TNLI). Then they analyzed the fitting of various night light indices and different regression models to the GDP 23 of Guangdong Province. Based on this, the optimal index and model were selected for the spatial grid partition of GDP 23 and the comparison of GDP 23 with that in 2019. Afterward, the authors analyzed the impacts of COVID-19 on GDP 23 of Guangdong Province in the first quarter and the reasons from spatial-temporal perspectives according to the spatial simulation results of GDP 23. For the cities most affected by the epidemic, the most affected industries were obtained through the statistical analysis of POI data, aiming to scientifically guide the precise resumption of work and production in Guangdong Province. The results are as follows. The spatial distribution of GDP 23 in 2019 was highly consistent with that in 2020, and the heart of Guangdong's economic development consisted of Guangzhou, Shenzhen, Dongguan, and Foshan cities in the two years. In terms of temporal distribution, 21 cities in Guangdong Province were affected by COVID-19 at different degrees in 2020 compared to 2019. Among them, the cities with relatively developed economies were affected the most, including Shenzhen, Guangzhou, Dongguan, and Foshan. According to POI data and the spatial distribution difference of GDP 23 between 2019 and 2020, the cities having suffered the biggest economic impacts were Guangzhou and Zhongshan, where the leading industries included shopping, real estate, and enterprises and companies, while the cities with the highest increased amplitude of GDP 23 included Shaoguan and Shenzhen, where the leading industries consisted of finance, real estate, and shopping. Therefore, the provincial and municipal governments should formulate corresponding policies on the financial industry, life service industry, and shopping consumption in Guangzhou and Zhongshan cities, in order to accurately assist enterprises and companies to early resume work and production.

Keywords NPP-VIIRS data      GDP contributed by secondary and tertiary industries      spatialization      Guangdong Province      POI data      COVID-19     
ZTFLH:  TP79  
Corresponding Authors: JIA Gongxu     E-mail: emailwangz@163.com;854628263@qq.com
Issue Date: 24 September 2021
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Zheng WANG
Gongxu JIA
Qingling ZHANG
Yue HUANG
Cite this article:   
Zheng WANG,Gongxu JIA,Qingling ZHANG, et al. Impacts of COVID-19 epidemic on the spatial distribution of GDP contributed by secondary and tertiary industries in Guangdong Province in the first quarter of 2020[J]. Remote Sensing for Natural Resources, 2021, 33(3): 184-193.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020340     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/184
Fig.1  General map of Guangdong Province
数据名称 数据来源
NPP-VIIRS夜间灯光数据 NOAA/NGDC(http://ngdc.noaa.gov/eog/viirs/downloaddaily.html)
广东省各市第二、三产业GDP增量 广东省各市的统计局官网
广东省市级边界行政区矢量图 ENVI APP Store( www.enviidl.com/appstore )
深圳市、广州市、韶关市和中山市POI数据 百度地图API接口
广东省第一季度疫情实时监测数据 广东省卫健委通报
Tab.1  Description of data source
Fig.2  Spatial distribution of existing cases in Guangdong Province
Fig.3  Time series change chart of daily new cases and city TNL in Guangdong Province
年份 变量1/(Y) 变量2/(X) 拟合函
数类型
拟合优
R2
F得分
2019年 GDP23 I 线性 0.563 24.486
S 线性 0.086 1.790
aI+bS 线性 0.563 24.484
CNLI 线性 0.563 24.462
TNL 线性 0.809 80.266
lnGDP23 lnI 幂函数 0.622 31.205
lnS 幂函数 0.176 4.058
ln(aI+bS) 幂函数 0.634 32.891
lnCNLI 幂函数 0.621 31.116
lnTNL 幂函数 0.837 97.309
2020年 GDP23 I 线性 0.551 23.303
S 线性 0.055 1.099
aI+bS 线性 0.551 23.303
CNLI 线性 0.551 23.303
TNL 线性 0.735 52.743
lnGDP23 lnI 幂函数 0.614 30.239
lnS 幂函数 0.089 1.863
ln(aI+bS) 幂函数 0.628 32.093
lnCNLI 幂函数 0.614 30.226
lnTNL 幂函数 0.841 100.718
Tab.2  Regression model parameters for 2019 and 2020 between GDP23 and night-time data indicators
Fig.4  Regression model of linear function between GDP23 and TNL in 2019
Fig.5  Regression model of linear function between GDP23 and TNL in 2020
Fig.6  The GDP23 spatial distribution map of Guangdong Province with 500 m spatial resolution in First quarter of 2019
Fig.7  The GDP23 spatial distribution map of Guangdong Province with 500 m spatial resolution in First quarter of 2020
Fig.8  Spatial distribution of GDP23 difference between 2019 and 2020 in Guangdong Province
城市 2019年 2020年
统计值/
亿元
空间化
值/亿元
相对误
差/%
统计值/
亿元
空间化
值/亿元
相对误
差/%
汕尾 181.76 181.76 <1 190.12 190.12 <1
惠州 932.62 932.62 <1 829.63 829.63 <1
佛山 2 232.24 2 232.24 <1 2 124.19 2 124.19 <1
梅州 234.06 234.06 <1 204.74 204.74 <1
韶关 177.71 177.71 <1 240.11 240.11 <1
河源 214.85 214.85 <1 187.22 187.22 <1
汕头 509.36 509.36 <1 511.82 511.82 <1
东莞 1 960.61 1 960.61 <1 1 917.62 1 917.62 <1
中山 730.26 730.26 <1 585.97 585.97 <1
江门 607.4 607.40 <1 585.33 585.33 <1
阳江 254.75 254.75 <1 200.28 200.28 <1
湛江 576.75 576.75 <1 519.91 519.91 <1
茂名 633.91 633.91 <1 553.56 553.56 <1
肇庆 363.19 363.19 <1 346.56 346.56 <1
清远 276.2 276.20 <1 280.1 280.1 <1
潮州 224 223.31 <1 201.2 201.2 <1
揭阳 412.99 412.99 <1 364.04 364.04 <1
云浮 173.31 173.31 <1 147.83 147.83 <1
广州 5 462.54 5 462.54 <1 5 182.22 5 182.22 <1
深圳 5 728.2 5 728.20 <1 5 780.38 5 780.38 <1
珠海 656.48 656.48 <1 688.98 688.98 <1
Tab.3  The relative error between the simulated value and the actual statistical value of GDP23 in 2019 and 2020 for each city in Guangdong Province
Fig.9  Guangzhou - Zhongshan economic recession difference
Fig.10  Shenzhen-Shaoguan economic growth difference
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