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Report-About-Exploration-on-The-Impact-of-COVID-19-to-GDP-of-Hubei-China

2021-12-03 16:33:30 作者：互联网

Information Visualization We Selected

By selecting the GDP data of Hubei province from one year before the outbreak of COVID-19, which happened at the end of 2019, until now, our project aims to explore the impact of COVID-19 on the GDP of Hubei province to search whether the epidemic has any negative effects on its GDP. And if true, how much did it go down, and how long this kind of downturn lasted. In addition, we looked at how long it would take for GDP to recover to the former standard, due to the opposite forces of the pandemic.

Why we select it

Back to the Spring Festival at the beginning of 2020,which is the greatest traditional festival of China, the COVID-19 outbreak broke out suddenly in Wuhan and spread to the whole country and even many countries around the world. Hubei became the worst-hit area of the epidemic. In order to contain the spread of the epidemic and ensure the safety and health of the people, several cities in the province have been “closed down”, taking emergency measures such as closing traffic channels, suspending public transport, delaying the resumption of work and production, and restricting the gathering and movement of people in communities. While effectively controlling the spread of the epidemic, these measures inevitably have a great impact on production, investment, export and service consumption, bringing economic losses to enterprises and increasing pressure on the economy of Hubei province.
Thus, in order to explore what happened to the GDP due to the impact, we select the data of Hubei province’s GDP from 2018 to 2021, to see whether the GDP was effected negatively and for what time it lasted.

Its impact in the society

As we all know, GDP refers to the total value of all final products produced by all resident units in a country or region during a certain period, covering all sectors of the national economy. It is the overall reflection of the macro economy, including various industries of the national economy. Therefore, GDP is the most core and comprehensive statistical indicator to reflect the macro economy. It can reflect the national economic development, national income and consumption power changes in a certain period of time.
GDP directly reflects the manufacturing level of a country or region, therefore, some people may think that GDP is only a macroeconomic indicator, far from daily life. In fact, GDP is closely related to each and every one of us. GDP reflects the results of economic growth. When GDP grows, it means that the output of production increases and enterprises expand their production scale, which helps to create job opportunities, increase people’s income and increase tax revenue, which will be used to develop public services and ultimately benefit people’s livelihood.

Detailed explanation of the information visualization

The story behind the visualization

It is exactly two years since December 1, 2019. Two years ago today, Wuhan, the capital of China’s Hubei province, reported its first case of pneumonia of unknown cause, followed by a spate of similar cases within a month. In early 2020, COVID-19 broke out in Wuhan, then spread across the country, and finally around the world, just before the Traditional Chinese New Year holiday.
The Spring Festival was not as lively as usual, as most Chinese were stuck in their regions or cities due to the epidemic, unable to travel. A good friend of mine was exactly in Wuhan and I was told by him that street vendors were unable to come out for business normally, supermarkets were closed, and People’s Daily necessities were delivered by the government.
I was pretty shocked and scared at the greatly negative effect of the epidemic. Thus I and my group members chose to search the GDP of that period to see what happened to it after all.

The first impression of the graph

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In the visualization, we can see in a year, GDP is growing quarter by quarter.
Between 2018 to 2019, before COVID-19 happening, the GDP’s quarter-over-quarter value is increasing with a stable rate (approximately 7%), in 2019 Q4 to Q1 2020, when the COVID-19 happened and struck this province, it’s GDP fell down rapidly. However, when the epidemic got controlled the GDP recovered rapidly.

How to read it

The visual patterns are used in the graph

Time , using a quarter, usually three months of a year, which is the x axis’s value to be depicted.
Number of GDP, using a unit of RMB yuan, in a year’s different season between 2018_1 to 2021_3, which uses bar chart to depict.
Year-on-year change: year-on-year GDP growth in different quarters of the same year is depicted using line charts.

Data types are represented in each visual patterns

Position in X-axis: Quarter of year(Datetime)
Position in Y-axis in bar chart: The amount of the GDP(Ordinal)
Position in Y-axis in plot chart:

increase or decrease of GDP(Categorical)
The amount of the rate of GDP year-over-year change rate(Ordinal)

Visual variables

Time:

Position: the closer time point is to the right side, the closer it is to the present.
2. Number of GDP

Position: it is determined by time and displayed above time.
Size: the higher the rectangle is, the higher the value is.
3. Year-on-year change
Position: it is decided by time, it is on the top of it’s time, and more right side of the data is more close to the present. A higher (lower) position stands for a far more rapid change.
Ontologies: Stands for change rate of the variable, 0-90°it is a positive change, and more close to 90° stands for a far more rapid change.

Summarize

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Replicate the information visualization

Process

Get x and y data

First we get the GDP of Hubei from 2018 to 2021 from https://www.hubei.gov.cn/hbfb/sj/tjgb/ , sort and store them in the GDP_2018 to GDP_2021 list ,and add them together in GDP_Total, which is prepared to be the value of y axis.
And we set time series from Q1 2018 to Q3 2021 and store them in to the list Time.

#Get axis data
GDP_2018 = [8188,17958,27634,39366]
GDP_2019 = [9110,19895,30509,45828]
GDP_2020 = [6379,17480,29779,43443]
GDP_2021 = [9872,22777,34731]
GPT_Total = np.array(GDP_2018 + GDP_2019 + GDP_2020 + GDP_2021)
Time=['Q1 2018','Q2 2018','Q3 2018','Q4 2018','Q1 2019','Q2 2019','Q3 2019','Q4 2019','Q1 2020','Q2 2020',
     'Q3 2020','Q4 2020','Q1 2021','Q2 2021','Q3 2021']

Calculate the quarter-on-quarter growth rate by Excel

First we set the size of the figure, to make it more similar to the size of the previous one we selected.
The next step is to draw the bar chart, we also attempt to modify the visualization’s width, label ,color and lable_unit to make it more similar to the former one.
And this is the final result.

# Calculate the quarter-on-quarter growth rate by Excel
Increase_Values = [11.24957869,11.24957869,10.78940672,10.40397187,11.24957869,10.78940672,10.40397187,16.41434162,-29.9745885,-12.13947702,-2.392703631,-5.203879436,54.75981095,30.30329207,16.6294038]

Use matplotlib to draw the figure

First we set the size of the figure, to make it more similar to the previous one we select.
Next step is to draw the bar chart, we also try to debug the argument’s width, label and color and lable_unit to make it more similar to the chart.

#Use matplotlib to draw the figure
fig, ax1 = plt.subplots(figsize=(13, 5))

plt.xticks(rotation=45)


ax1.bar(Time, GPT_Total, width=0.85,label="GDP",color='#37A2DA')
ax1.set_ylabel("GDP(Unit: 100 million yuan)")
plt.yticks(list(range(0,50001,5000)))


ax2 = ax1.twinx()
ax2.plot(Time, Increase_Values, color="orange", label="Increase")
ax2.set_ylabel("Increase(percent)")
plt.yticks(list(range(-50,51,10)))


plt.title("GDP of Hubei province in each quarter of each year")
fig.legend(loc="upper right", bbox_to_anchor=(1, 1), bbox_transform=ax1.transAxes)
plt.figure(figsize=(720,480))
plt.show()

This is the final result.
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Propose changes that would improve the graph

Pitfalls of the graph

Unwanted biases issues

Plot unrelated things together; spurious correlation
In this project, the Number of GDP and Year-on-year change are in the same graph, this will bring a bias: The fact that year-on-year change is calculated from previous data, rather than adjacent data, which can be misleading.
Hard to compare a year’s change
In this project’s Number of GDP is depicted by quarter of year, when reader want to compare a year’s GDP size change (it is more important for GDP’s change comparation), they cannot directly get effective information.

Readability issues

Indirect look-up
It has no data labels for readers to directly look-up the data.
Rotating text make it hard to read
As there are too many data to present the time on X-axis, the reader has to rotate the image to get specific information, which makes reading very tough.
Unfriendly for color-blind readers
According to our survey, color-blind people find the plot chart they are in through the bar chart with weak contrast, so it is difficult for color-blind people to read.
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How to address the pitfalls

According to the issues mentioned above, we propose some measures to improve the project.

Unwanted biases issues

1. Plot unrelated things together; spurious correlation
Solution: We will create separate graph to depict Number of GDP’s change (Using bar chart) and Year-on-year change (Using plot chart).
2. Hard to compare a year’s change
Solution: We will add the GDP of four quarters in a year together, for easier comparison of a year’s change.

Readability issues

1.Indirect look-up
Solution: Add data labels, Add zero-line in the plot chart.
2.Rotating text make it hard to read
Solution: Same to Hard to compare a year’s change
3.Unfriendly for color-blind readers
Solution: change the color of the depict.

Implement the changes

Process

Get data of different quarters’ in a year.

#Gain data of different quarter’s in a year.
GDP1 = [8188,9110,6379,9872]
GDP2 = [17958,19895,17480,22777]
GDP3 = [27634,30509,29779,34731]
GDP4 = [39366,45828,43443,34731]

Convert the total GDP as of the quarter into the increment of GDP for the quarter ********

# Convert the total GDP as of the quarter into the increment of GDP for the quarter
L1 = GDP1
L1

L2 = []
for i in range(4):
    L2.append(GDP2[i] - GDP1[i])
L2

L3 = []
for i in range(4):
    L3.append(GDP3[i] - GDP2[i])
L3

L4 = []
for i in range(4):
    L4.append(GDP4[i] - GDP3[i])
L4

L = []
for i in range(4):
    L.append(L1[i])
    L.append(L2[i])
    L.append(L3[i])
    L.append(L4[i])
L

Compute Year-On-Year GDP change rate for graph 2

# Compute year to year GDP change ratio for graph 2
r = []
for i in range(1,len(L) - 1):
    r.append((L[i]- L[i - 1])/L[i - 1])
r

Gain data of time data for x-axis

r_lable = []
r_lable.append('2018_2')
y = 2019
for i in range(len(r) - 1):
    if i % 4 == 2:
        r_lable.append(str(y) + '_1')
        y = y + 1
    else:
        r_lable.append(' ')
r_lable

draw the figure

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig = plt.figure('GDP statistics of Hubei Province for each quater',figsize=(18,18))
fig.tight_layout()
labels = year
x_lables = np.arange(len(year))
width = 0.5
ax1 = fig.add_subplot(311)
ax1.bar(x_lables, L1, width, label='the first quarter')
ax1.bar(x_lables, L2, width, bottom=L1, color = "green", label='the second quarter')
ax1.bar(x_lables, L3, width, bottom=GDP2,label='the third quarter')
ax1.bar(x_lables, L4, width, bottom=GDP3, color = "orange",label='the fourth quarter')
ax1.set_title('GDP statistics of Hubei Province')
ax1.set_ylabel('GDP(100 million yuan)')
ax1.set_xticks(x_lables)
ax1.set_xticklabels(year)
ax1.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(4):
    ax1.annotate('{}'.format(GDP4[i]),
                    xy=(x_lables[i], GDP4[i]),
                    xytext=(0, 1),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L4[i]),
                    xy=(x_lables[i], GDP4[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L3[i]),
                    xy=(x_lables[i], GDP3[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L2[i]),
                    xy=(x_lables[i], GDP2[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L1[i]),
                    xy=(x_lables[i], GDP1[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
ax2 = fig.add_subplot(312)
x_lables = np.arange(len(r))
ax2.plot(x_lables, r, label="rate")
ax2.set_title('rate of improvement of GDP in Hubei Province')
ax2.set_ylabel('rate(<1)')
ax2.set_xticks(x_lables)
ax2.set_xticklabels(r_lable)
ax2.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(len(r)):
    if r[i] > 0:
        ax2.annotate('{:.2f}'.format(r[i]),
                    xy=(x_lables[i], r[i]),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    else:
        ax2.annotate('{:.2f}'.format(r[i]),
                    xy=(x_lables[i], r[i]),
                    xytext=(0, -10),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
ax3 = fig.add_subplot(313)
x_lables = np.arange(len(r1_lable))
ax3.bar(x_lables, r1, width, label='rate of improvement')
ax3.set_title('rate of improvement of GDP in Hubei Province')
ax3.set_ylabel('rate(<1)')
ax3.set_xticks(x_lables)
ax3.set_xticklabels(r1_lable)
ax3.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(len(r1)):
    if r[i] > 0:
        ax3.annotate('{:.2f}'.format(r1[i]),
                    xy=(x_lables[i], r1[i]),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    else:
        ax3.annotate('{:.2f}'.format(r1[i]),
                    xy=(x_lables[i], r1[i]),
                    xytext=(0, -10),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
fig.tight_layout()
plt.show()

Final Result:
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Compare the previous visualization project

Unwanted biases issues

1. Plot unrelated things together; spurious correlation
In this project, by separating the figure.
the Number of GDP in the first graph and Year-on-Year change in the second graph, we solve the biases issue
2. Hard to compare a year’s change
In our new graph, we grouped GDP by year, drawing four bar charts representing different years such as 2018, 2019, 2020.

Readability issues

1.Indirect look-up
We add data labels to solve this issue.
2.Hard to compare a year’s change
Since time data are added and sorted, it provides enough space to make the text of time data not necessary to be rotated, which makes it easier for readers.
3.Unfriendly for color-blind readers
We took Color Universal Design’s suggestion to change the color of the figure(refer:https://zh.wiki.hancel.org/wiki/%E8%89%B2%E5%BD%A9%E9%80%9A%E7%94%A8%E8%A8%AD%E8%A8%88), choosing four colors for the chart. These four colors meet the needs of color-blind people with different symptoms to be able to read this image. Regardless of the type of color blindness, each color can be distinguished by its depth or recognition. This design increases the range of the icon’s audience and makes it more human.
Here we simulated color-blind vision of the new visualization.
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publishment on Github and CSDN

1.GitHub

2.CSDN# Information Visualization We Selected

Why we select it

Its impact in the society

Detailed explanation of the information visualization

The story behind the visualization

The first impression of the graph

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In the visualization, we can see in a year, GDP is growing quarter by quarter.
Between 2018 to 2019, before COVID-19 happening, the GDP’s quarter-over-quarter value is increasing with a stable rate (approximately 7%), in 2019 Q4 to Q1 2020, when the COVID-19 happened and struck this province, it’s GDP fell down rapidly. However, when the epidemic got controlled the GDP recovered rapidly.

How to read it

The visual patterns are used in the graph

Time , using a quarter, usually three months of a year, which is the x axis’s value to be depicted.
Number of GDP, using a unit of RMB yuan, in a year’s different season between 2018_1 to 2021_3, which uses bar chart to depict.
Year-on-year change: year-on-year GDP growth in different quarters of the same year is depicted using line charts.

Data types are represented in each visual patterns

Position in X-axis: Quarter of year(Datetime)
Position in Y-axis in bar chart: The amount of the GDP(Ordinal)
Position in Y-axis in plot chart:

increase or decrease of GDP(Categorical)
The amount of the rate of GDP year-over-year change rate(Ordinal)

Visual variables

Time:

Position: the closer time point is to the right side, the closer it is to the present.
2. Number of GDP

Summarize

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Replicate the information visualization

Process

Get x and y data

#Get axis data
GDP_2018 = [8188,17958,27634,39366]
GDP_2019 = [9110,19895,30509,45828]
GDP_2020 = [6379,17480,29779,43443]
GDP_2021 = [9872,22777,34731]
GPT_Total = np.array(GDP_2018 + GDP_2019 + GDP_2020 + GDP_2021)
Time=['Q1 2018','Q2 2018','Q3 2018','Q4 2018','Q1 2019','Q2 2019','Q3 2019','Q4 2019','Q1 2020','Q2 2020',
     'Q3 2020','Q4 2020','Q1 2021','Q2 2021','Q3 2021']

Calculate the quarter-on-quarter growth rate by Excel

# Calculate the quarter-on-quarter growth rate by Excel
Increase_Values = [11.24957869,11.24957869,10.78940672,10.40397187,11.24957869,10.78940672,10.40397187,16.41434162,-29.9745885,-12.13947702,-2.392703631,-5.203879436,54.75981095,30.30329207,16.6294038]

Use matplotlib to draw the figure

#Use matplotlib to draw the figure
fig, ax1 = plt.subplots(figsize=(13, 5))

plt.xticks(rotation=45)


ax1.bar(Time, GPT_Total, width=0.85,label="GDP",color='#37A2DA')
ax1.set_ylabel("GDP(Unit: 100 million yuan)")
plt.yticks(list(range(0,50001,5000)))


ax2 = ax1.twinx()
ax2.plot(Time, Increase_Values, color="orange", label="Increase")
ax2.set_ylabel("Increase(percent)")
plt.yticks(list(range(-50,51,10)))


plt.title("GDP of Hubei province in each quarter of each year")
fig.legend(loc="upper right", bbox_to_anchor=(1, 1), bbox_transform=ax1.transAxes)
plt.figure(figsize=(720,480))
plt.show()

This is the final result.
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Propose changes that would improve the graph

Pitfalls of the graph

Unwanted biases issues

Plot unrelated things together; spurious correlation
In this project, the Number of GDP and Year-on-year change are in the same graph, this will bring a bias: The fact that year-on-year change is calculated from previous data, rather than adjacent data, which can be misleading.
Hard to compare a year’s change
In this project’s Number of GDP is depicted by quarter of year, when reader want to compare a year’s GDP size change (it is more important for GDP’s change comparation), they cannot directly get effective information.

Readability issues

Indirect look-up
It has no data labels for readers to directly look-up the data.
Rotating text make it hard to read
As there are too many data to present the time on X-axis, the reader has to rotate the image to get specific information, which makes reading very tough.
Unfriendly for color-blind readers
According to our survey, color-blind people find the plot chart they are in through the bar chart with weak contrast, so it is difficult for color-blind people to read.
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How to address the pitfalls

According to the issues mentioned above, we propose some measures to improve the project.

Unwanted biases issues

Readability issues

Implement the changes

Process

Get data of different quarters’ in a year.

#Gain data of different quarter’s in a year.
GDP1 = [8188,9110,6379,9872]
GDP2 = [17958,19895,17480,22777]
GDP3 = [27634,30509,29779,34731]
GDP4 = [39366,45828,43443,34731]

Convert the total GDP as of the quarter into the increment of GDP for the quarter ********

# Convert the total GDP as of the quarter into the increment of GDP for the quarter
L1 = GDP1
L1

L2 = []
for i in range(4):
    L2.append(GDP2[i] - GDP1[i])
L2

L3 = []
for i in range(4):
    L3.append(GDP3[i] - GDP2[i])
L3

L4 = []
for i in range(4):
    L4.append(GDP4[i] - GDP3[i])
L4

L = []
for i in range(4):
    L.append(L1[i])
    L.append(L2[i])
    L.append(L3[i])
    L.append(L4[i])
L

Compute Year-On-Year GDP change rate for graph 2

# Compute year to year GDP change ratio for graph 2
r = []
for i in range(1,len(L) - 1):
    r.append((L[i]- L[i - 1])/L[i - 1])
r

Gain data of time data for x-axis

r_lable = []
r_lable.append('2018_2')
y = 2019
for i in range(len(r) - 1):
    if i % 4 == 2:
        r_lable.append(str(y) + '_1')
        y = y + 1
    else:
        r_lable.append(' ')
r_lable

draw the figure

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig = plt.figure('GDP statistics of Hubei Province for each quater',figsize=(18,18))
fig.tight_layout()
labels = year
x_lables = np.arange(len(year))
width = 0.5
ax1 = fig.add_subplot(311)
ax1.bar(x_lables, L1, width, label='the first quarter')
ax1.bar(x_lables, L2, width, bottom=L1, color = "green", label='the second quarter')
ax1.bar(x_lables, L3, width, bottom=GDP2,label='the third quarter')
ax1.bar(x_lables, L4, width, bottom=GDP3, color = "orange",label='the fourth quarter')
ax1.set_title('GDP statistics of Hubei Province')
ax1.set_ylabel('GDP(100 million yuan)')
ax1.set_xticks(x_lables)
ax1.set_xticklabels(year)
ax1.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(4):
    ax1.annotate('{}'.format(GDP4[i]),
                    xy=(x_lables[i], GDP4[i]),
                    xytext=(0, 1),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L4[i]),
                    xy=(x_lables[i], GDP4[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L3[i]),
                    xy=(x_lables[i], GDP3[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L2[i]),
                    xy=(x_lables[i], GDP2[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    ax1.annotate('{}'.format(L1[i]),
                    xy=(x_lables[i], GDP1[i]),
                    xytext=(0, -20),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
ax2 = fig.add_subplot(312)
x_lables = np.arange(len(r))
ax2.plot(x_lables, r, label="rate")
ax2.set_title('rate of improvement of GDP in Hubei Province')
ax2.set_ylabel('rate(<1)')
ax2.set_xticks(x_lables)
ax2.set_xticklabels(r_lable)
ax2.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(len(r)):
    if r[i] > 0:
        ax2.annotate('{:.2f}'.format(r[i]),
                    xy=(x_lables[i], r[i]),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    else:
        ax2.annotate('{:.2f}'.format(r[i]),
                    xy=(x_lables[i], r[i]),
                    xytext=(0, -10),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
ax3 = fig.add_subplot(313)
x_lables = np.arange(len(r1_lable))
ax3.bar(x_lables, r1, width, label='rate of improvement')
ax3.set_title('rate of improvement of GDP in Hubei Province')
ax3.set_ylabel('rate(<1)')
ax3.set_xticks(x_lables)
ax3.set_xticklabels(r1_lable)
ax3.legend(bbox_to_anchor=(1, 0), loc=3, borderaxespad=0)
for i in range(len(r1)):
    if r[i] > 0:
        ax3.annotate('{:.2f}'.format(r1[i]),
                    xy=(x_lables[i], r1[i]),
                    xytext=(0, 3),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
    else:
        ax3.annotate('{:.2f}'.format(r1[i]),
                    xy=(x_lables[i], r1[i]),
                    xytext=(0, -10),  # 3 points vertical offset
                    textcoords="offset points",
                    ha='center', va='bottom')
fig.tight_layout()
plt.show()

Final Result:
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-69PrKlOH-1638519543486)(5.1.png)]

Compare the previous visualization project

Unwanted biases issues

Readability issues

publishment on Github and CSDN

1.GitHub

2.CSDN

标签：Impact,GDP,About,ax1,year,lables,quarter,change
来源： https://blog.csdn.net/weixin_45936422/article/details/121701482