Rural population (% of total population)

The rural population percentage of total population is a significant demographic indicator that reveals both the distribution and dynamics of people living in rural areas as opposed to urban settings. As of 2023, the world median stands at 35.53%, which underscores a global trend towards urbanization, highlighting that more than one-third of the world’s population resides in rural zones. Understanding this indicator requires a multifaceted exploration of its implications, importance, relationships with other factors, contributing elements, strategic solutions, and potential flaws.

Rural population percentage is crucial not just for demographers but for policymakers, developmental organizations, and economists as well. This statistic aids in comprehending the social and economic fabric of regions. A higher rural population percentage often reflects agricultural economies, traditional lifestyles, and possibly lower access to modern services such as healthcare and education. Conversely, areas with very low percentages, like Bermuda and the Cayman Islands (both at 0.0%), indicate developed economies with urban-centric infrastructures that often prioritize financial and technological industries over agriculture or rural development.

The connection between rural population percentage and other indicators is deeply intertwined. For instance, regions with a high rural population typically show different rates of literacy, healthcare access, and income levels compared to urban areas. Thus, a higher rural population may correlate with lower overall economic performance, given the focus on agriculture rather than industrial or service-oriented economic drivers. Furthermore, as rural percentages decrease, we can observe shifts in employment patterns, migration trends, and resource allocation, which are crucial for planning sustainable regional development.

Several factors influence the rural population percentage, including economic opportunities, educational facilities, healthcare access, and government policies. Economic opportunities in urban areas generally attract individuals from rural regions seeking better livelihoods. Young people often migrate to cities for education or employment, which can further exacerbate rural depopulation. In many cases, government policies failing to invest in rural infrastructure or promote agrarian development contribute to this decline, leading to a vicious cycle of abandonment in rural areas.

To combat issues related to high rural populations, many countries have implemented strategies that aim to enhance the living conditions in these regions. Initiatives to improve rural education and healthcare, incentivizing agribusiness, and integrating digital technologies into farming practices are vital solutions. Countries with substantial rural populations like Papua New Guinea (86.28%) and Burundi (85.22%) can find multiple intersections in policy that enhance both agricultural productivity and quality of life. Decentralization policies, promoting local governance, can also empower rural areas to create tailored solutions that address their unique challenges and maintain a balanced demographic landscape.

While policies are essential, they often come with flaws. Historically, rural development initiatives can sometimes neglect the unique cultural and social aspects of rural communities, resulting in solutions that do not meet the needs of their residents. In addition, a one-size-fits-all approach may undermine local traditions or practices that have stood for generations, focusing instead on homogenized development that privileges economic interests over cultural preservation.

Analyzing the historical trends reveals that the world rural population has been steadily declining since 1960, when it represented 66.35% of the total population. This pattern reflects not just globalization but also the impact of industrialization and technological advancement, increasingly pushing people toward urban environments. In stark contrast to rural heavyweights, the world has seen many regions, particularly in developed nations, experience much lower rural population percentages, driving substantial urban-centered development.

The top areas reflecting the remarkable rural populations—such as Papua New Guinea, Liechtenstein, and Burundi—bear witness to differing governance structures, resource management, and socio-economic conditions affecting rural livelihoods. Meanwhile, the lowest entries—like Bermuda and Hong Kong—exemplify urban densification driven by economic focus, leading to challenges related to sustainability and quality of life.

In conclusion, understanding the rural population percentage is vital for recognizing how communities evolve, interact, and develop. By exploring the connectivity to other social and economic indicators while noting the underpinnings that affect them, stakeholders can better navigate the intricacies involved in shaping sustainable policies that retain rural cultural identities while fostering necessary growth. The relationship between rural population dynamics and overall socio-economic development underscores a profound narrative of modern civilization that requires continuous analysis and a thoughtful approach.

                    
# 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.ZS'

# 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.ZS'

# 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))