Rural population

The rural population is an essential demographic indicator that reflects the number of individuals living in areas that are classified as rural. These regions are typically characterized by lower population density, less access to services, and a greater reliance on agriculture and natural resources. As of 2023, the global rural population stands at approximately 3.45 billion individuals, with the median rural population value reaching 1,762,465. This demographic factor is crucial in understanding global population trends, economic development, and social challenges.

The importance of monitoring the rural population cannot be overstated. It has significant implications for policy formulation, economic development, and environmental management. Rural areas often face unique challenges, such as limited access to education, healthcare, and employment opportunities. Understanding the rural population dynamics helps governments and organizations tailor interventions to improve living standards and foster sustainable development. For instance, targeted investments in rural infrastructure can facilitate economic growth and enhance access to essential services.

Rural population figures are closely related to various other indicators. For example, the rural population is often correlated with levels of poverty, employment rates, and agricultural productivity. High rural populations may indicate a reliance on agriculture, while lower figures can signify urban migration patterns as people seek better opportunities. Furthermore, trends in rural population dynamics can offer insights into food security, as rural communities are often responsible for food production. This relationship highlights the need for integrated approaches when addressing issues of rural development and sustainability.

Several factors influence the rural population. Economic conditions play a significant role, where rural areas with diverse income opportunities may retain more residents. Conversely, economic stagnation can drive people to urban centers in search of better prospects. Environmental issues, such as land degradation and climate change, also pose threats to rural communities, potentially leading to displacement. Social factors like education and healthcare accessibility can greatly impact population retention or migration, as individuals may leave rural areas in pursuit of improved living conditions.

In light of these challenges, several strategies can be adopted to enhance rural population sustainability and resilience. Investing in education and healthcare services in rural communities can empower individuals and improve their quality of life. Providing access to technology and internet services can also revolutionize the way rural populations engage in economic activities, making them more competitive and innovative. Furthermore, promoting local entrepreneurship and supporting small-scale farmers can stimulate the rural economy, reducing the dependency on urban migration.

Innovative agricultural practices can also significantly contribute to rural development. By introducing sustainable practices and modern farming techniques, the productivity of rural lands can be enhanced while ensuring environmental sustainability. Policymakers can collaborate with NGOs and local leaders to create programs that address specific needs and challenges faced by rural populations.

Despite the importance of understanding rural populations, there are inherent flaws in the data collection and analysis processes. Many countries may lack comprehensive and up-to-date demographic data, leading to an underestimation or misrepresentation of the rural population size. The challenges of defining what constitutes a 'rural' area, particularly in developing nations, can also affect data accuracy. Moreover, urbanization trends can shift rapidly, making it difficult for statistical agencies to keep pace with the changing landscape.

In 2023, the top five countries with the largest rural populations include India, China, Pakistan, Indonesia, and Nigeria. India stands out with a staggering rural population of approximately 915 million, vastly overshadowing other nations. Similarly, China follows with around 499 million individuals living in rural areas. These statistics reflect not only the population size but also the cultural and economic significance of rural life in these nations. On the other hand, regions like Bermuda, the Cayman Islands, Gibraltar, Hong Kong SAR China, and Kuwait report a rural population of zero, indicating urban-centric development and population distribution in these areas.

Looking at historical data, the world’s rural population has seen a gradual decline relative to urban areas over the decades. From approximately 2 billion in 1960, the global rural population hit around 3.45 billion in 2023. This sustained growth indicates that although urbanization is a prominent trend, rural areas are still home to a significant and growing number of people. The data also reveals fluctuations over the years, emphasizing various socio-economic developments, policy changes, and global events that may influence population movements.

Understanding the dynamics of the rural population is vital as it provides valuable insights into the challenges and opportunities facing these communities. As nations grapple with urbanization and strive for sustainable development, recognizing the importance of rural populations in achieving a balanced socio-economic framework will be essential for future progress.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'SP.RUR.TOTL'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'SP.RUR.TOTL'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))