Urban population (% of total population)

The indicator of urban population as a percentage of total population is a critical metric for understanding the dynamics of human settlement patterns across the globe. It reflects the extent to which populations are migrating from rural areas to cities and urban centers. This shift can be attributed to various factors such as economic opportunities, better access to services, and the search for improved living conditions. In recent years, the world has witnessed a significant increase in urbanization, with the median value of urban population reaching approximately 64.47% in 2023. This means that over half of the global population resides in urban areas, marking a paradigm shift in where people choose to live and work.

The importance of urban population statistics cannot be overstated. They not only inform government policy and urban planning but also drive investment and resource allocation. High levels of urbanization can lead to economic growth as cities tend to concentrate resources, talent, and innovations. However, unchecked urbanization may also strain infrastructure, housing, and social services, leading to challenges such as congestion, pollution, and inequity.

Urban population percentage is closely related to several other indicators, including GDP per capita, employment rates, and quality of life statistics. For instance, countries with high urban populations often exhibit higher GDP per capita due to the concentration of economic activities. Additionally, urban environments typically offer better job opportunities, which can lower unemployment rates compared to rural areas where job markets may be less vibrant. Furthermore, the quality of life in urban settings can vary significantly, influenced by factors such as access to healthcare, education, and public services.

Many factors affect urban population rates. One significant determinant is economic development; as nations industrialize, job opportunities in cities frequently attract rural dwellers seeking better livelihoods. Demographic trends also play a role, with factors such as age distribution, birth rates, and migration patterns exhibiting a profound impact on urbanization. For example, younger populations are more likely to migrate to urban areas in search of educational opportunities and employment, thereby increasing urban population percentages.

To address the challenges posed by rapid urbanization, various strategies and solutions can be implemented. Effective urban planning is essential, focusing on creating sustainable cities that can accommodate growing populations without compromising quality of life. Strategies may include developing efficient public transit systems, expanding green spaces, and investing in affordable housing projects. Moreover, policies that promote integrated urban-rural development can mitigate the pressure on urban centers while improving living conditions in rural areas.

However, the pursuit of urbanization is not without its flaws. While urban areas present numerous advantages, they also exacerbate social inequalities and environmental issues. The urban poor often face inadequate access to essential services and housing, leading to the proliferation of informal settlements. Additionally, urban centers are significant contributors to climate change, consuming vast amounts of energy and resources, necessitating a balanced approach that acknowledges both the benefits and drawbacks of urban living.

Examining the 2023 data highlights the diversity of urbanization across different regions. The highest urban population percentages are seen in jurisdictions like Bermuda, Cayman Islands, Gibraltar, Hong Kong SAR China, and Kuwait, each boasting an impressive 100% urban population. This raises questions regarding the implications of such complete urbanization: while these regions enjoy efficient service delivery and a high concentration of opportunities, they may also face challenges related to sustainability and livability.

In stark contrast, countries such as Papua New Guinea (13.72%), Liechtenstein (14.62%), and Burundi (14.78%) demonstrate significantly lower urbanization levels. These figures illustrate how geographical, economic, and historical contexts shape settlement patterns. Low urban population percentages can indicate challenges in infrastructure development, limited economic opportunities, and a reliance on primary industries, which impede the growth of urban centers.

Over the decades, the global trend has leaned toward increasing urbanization, illustrated by historical data from 1960 to 2023. The world witnessed a steady rise in urban population percentages, growing from 33.65% in 1960 to over 57.25% in recent years. This trajectory signifies a shift in global demographics and the resulting implications for economies and ecosystems alike. One notable observation from the historical data is that while urbanization can spur economic growth, it also necessitates robust governance and strategic planning to manage the associated challenges effectively.

As we move forward, understanding the implications of urban population figures will be crucial for policymakers and stakeholders. There must be a focus on developing sustainable urban centers that promote economic opportunities while ensuring equitable access to vital services. Moreover, pro-active measures should be taken to address environmental concerns stemming from urbanization, such as investing in renewable energy, improving waste management systems, and enhancing urban green spaces. By considering these factors and strategies, cities can harness the benefits of increased urban populations while working to alleviate the associated challenges.

                    
# 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.URB.TOTL.IN.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.URB.TOTL.IN.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))