A sharp decline in the natural rate of population growth: Trends and visualization analysis of natural population decline in South Korea 2022-2052 - A national crisis

South Korea is currently experiencing a significant demographic tipping point, with the country's natural population growth rate continuing to decline, which is leading to a natural population decline. According to the recently released Statistics Korea Future Population Projections (May 2024), Korea's natural population growth rate is expected to decline sharply and become negative from 2022 to 2052. It's safe to say that this is a national crisis.

In this post, we'll use the Analyzing data at the national and local levels and visualizing the results in Python to highlight the severity of population change in South Korea.by region. By looking at overall trends and regional variations, we hope to provide important insights for our readers.

Data brief: The reality of South Korea's changing population

This analysis is based on the Office for National Statistics May 2024 projections (trial version)The data is based on data provided by the National Population Institute, which includes the natural rate of population change for South Korea as a whole and by region from 2022 to 2052.

Total population natural growth rate data (2022-2052)

Natural population growth by region (2022 vs. 2052)

Python visualization results and insights

Overall Trend: Sharply Declining Natural Population Growth Rate

Below is the Natural growth rate of the total population of South Korea from 2022 to 2052 A line graph to visualize the change.

import numpy as np
import matplotlib.pyplot as plt

Prepare the # data
years = [2022, 2025, 2030, 2035, 2040, 2045, 2052]
growth_rates = [-0.22, -0.27, -0.29, -0.37, -0.55, -0.75, -1.11]

Visualize #
plt.figure(figsize=(12, 6))

# Line Graph
plt.plot(years, growth_rates, marker='o', color='blue', label='Population Growth Rate')

Add # key data labels
for x, y in zip(years, growth_rates):
    plt.text(x, y, f'{y:.2f}%', ha='center', va='bottom', fontsize=10, color='blue')

Set the # x and y axes
plt.xticks(years, fontsize=12)
plt.yticks(fontsize=12)
plt.axhline(0, color='gray', linestyle='--', linewidth=0.8) # Add baseline
plt.axvline(2022, color='red', linestyle='--', linewidth=0.8, label='2022 Reference')

# title and label
plt.title('Population Growth Rate Trend (2022-2052)', fontsize=16)
plt.xlabel('Year', fontsize=14)
plt.ylabel('Growth Rate (%)', fontsize=14)
plt.legend(fontsize=12)

Optimize and display the # layout
plt.tight_layout()
plt.show()

Key observations:

1) 2022: The growth rate is already starting negative (-0.22%) and decreasing.
2) 2052: The growth rate is a whopping -1.11%and the decline is even more dramatic.
3) Amount of change: about -0.89%pis not just a number, it signals a serious social problem.

Regional analysis: widening the gap between regions

Here's the Change in natural rate of population growth by region between 2022 and 2052compared to the other.

import numpy as np
import matplotlib.pyplot as plt

# Data preparation
regions = ['Sejong', 'Gyeonggi', 'Ulsan', 'Daejeon', 'Seoul', 'Jeju', 'Incheon', 'Gwangju',
           'Daegu', 'Chungbuk', 'Chungnam', 'Gyeongnam', 'Busan', 'Gangwon', 'Jeonbuk',
           'Gyeongbuk', 'Jeonnam']
growth_2022 = [0.38, -0.03, -0.09, -0.10, -0.13, -0.15, -0.15, -0.15, -0.16, -0.29, -0.34,
               -0.37, -0.40, -0.47, -0.56, -0.57, -0.65, -0.65]
growth_2052 = [-0.38, -0.89, -1.39, -0.93, -0.80, -0.98, -1.06, -1.04, -1.30, -1.28,
               -1.23, -1.43, -1.49, -1.51, -1.71, -1.63, -1.63]

x = np.array(len(regions)) # X-axis positions

# Visualization
fig, ax = plt.subplots(figsize=(14, 6))

# Bar graph creation
bar1 = ax.bar(x - 0.2, growth_2022, width=0.4, label='2022', color='blue')
bar2 = ax.bar(x + 0.2, growth_2052, width=0.4, label='2052', color='white', edgecolor='black')

# Graph styling
ax.axhline(0, color='black', linewidth=0.8, linestyle='--') # Reference line
ax.set_xticks(x)
ax.set_xticklabels(regions, fontsize=10, rotation=45, ha='right')
ax.set_ylabel('Natural Increase Rate (%)', fontsize=12)
ax.set_title('Natural Increase Rate by Region, 2022 vs. 2052', fontsize=14)
ax.legend(fontsize=10)

# Adding data labels
def add_labels(bars, offset):
    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width()/2, height + offset if height >= 0 else height - offset,
                f'{height:.2f}', ha='center', va='bottom' if height >= 0 else 'top', fontsize=9)

add_labels(bar1, 0.03)
add_labels(bar2, 0.03)

# Layout optimization and display
plt.tight_layout()
plt.show()

Key observations:

1. Sejong City: The only positive growth rate (+0.381 TP3T in 2022), but it turns negative (-0.381 TP3T) in 2052.
2. Jeonbuk, Gyeongbuk, Jeonnamhas the largest decrease, with all -1.6% or higher It's down.
3. Metropolitan Area (Seoul, Gyeonggi, Incheon): relatively good, but still turning negative and not avoiding a decline.

The regional disparities are likely to widen in the future, suggesting this is an area for policy intervention, particularly in the Natural population decline in non-metropolitan areas is expected to be more severewhich further emphasizes the importance of regional balanced development policies. This trend is undoubtedly a national crisis facing Korea.

Wrapping up: Data stories in Python code

In this analysis, we used Python to visualize population natural growth rate data. The two graphs presented above clearly show the national trend and regional variation, respectively.

Code breakdown

Overall trend line graph

• plt.figure(figsize=(12, 6)): Sets the size of the graph.
• plt.plot(years, growth_rates, marker='o', ...): Connect the growth rates by year with a line and mark each data point with a circle marker.
• plt.text(x, y, f'{y:.2f}%', ...): Adds the corresponding value as a label to each data point.
• plt.axhline(0, ...) and plt.axvline(2022, ...): Add a baseline and a 2022 reference line.

Bar graphs by region

• fig, ax = plt.subplots(figsize=(14, 6)): Create a subplot and set its size.
• ax.bar(x - 0.2, growth_2022, ...) and ax.bar(x + 0.2, growth_2052, ...): Create a bar graph with data from 2022 and 2052 side by side.
• add_labels function: Adds the corresponding value as a label above each bar.
• ax.set_xticklabels(regions, fontsize=10, rotation=45, ha='right'): Rotate the x-axis labels by 45 degrees to make them more readable.

Conclusions and recommendations

The analysis clearly demonstrated the severity of South Korea's declining natural population growth rate and the problem of natural population decline.

Key takeaway:

1) Overall, the natural rate of population growth continues to decline, which should be recognized as a national crisis.
2) Regional disparities are widening, and policy efforts are urgently needed to develop balance.
3) Data visualization with Python is a great way to easily understand and analyze complex demographics.

Preparing for the future requires not only policies to encourage fertility, but also the establishment of economic and social systems that are suitable for an aging society, and customized policies that take into account regional characteristics. Raising awareness of this serious population issue and working together with governments and civil society to find solutions is crucial for our future.

This severe natural population decline has been attributed to South Korea's Statistics on the number of deathsand Total fertility rate The outlook is even more grim, so we need to take appropriate measures through social unity and consensus as soon as possible, even if it is too late.