Urban population

The urban population is a critical demographic indicator representing the portion of people living in urban areas as compared to the overall population of a specific region. This metric is essential for understanding societal trends, economic priorities, and the effectiveness of urban planning and development. As of 2023, the world's urban population reached approximately 4.6 billion, reflecting a significant shift towards urbanization over the decades.

Urbanization is not merely a demographic phenomenon; it is inherently linked to various socio-economic indicators. Higher urban population figures often correlate with increased economic opportunities, as cities generally serve as growth engines for nations. They attract industries, services, and jobs, drawing individuals from rural backgrounds seeking better livelihoods. Thus, urban population statistics can provide insight into economic vitality, employment rates, and migration patterns.

The importance of tracking urban population statistics extends beyond immediate economic concerns. Urban areas face unique challenges, such as increased demand for housing, transportation, infrastructure, and services including healthcare and education. A rising urban population can lead to congestion, environmental degradation, and resource depletion if not managed effectively. Policymakers and urban planners rely on accurate urban population data to create strategies that ensure sustainable development and livable cities.

Various factors affect urban population growth. Economic opportunities, educational institutions, and access to essential services are primary drivers for migration from rural to urban areas. Political stability and safety also play a pivotal role, as people tend to move towards regions perceived as secure and prosperous. In addition to these factors, environmental considerations contribute to urbanization; for example, people may relocate to cities that offer better responses to climate change impacts or natural disasters.

The 2023 statistics show that the top five urban populations are concentrated in rapidly developing countries. China boasts the highest urban population, standing at approximately 910 million. This immense figure reflects China's significant urbanization efforts, marked by government policies favoring industrialization and urban development. India follows with around 523 million, demonstrating its growth as a major economic force, despite facing significant urban challenges such as poverty and infrastructure deficits. The United States ranks third, with approximately 279 million, indicating a mature urban system but revealing ongoing issues related to inequality and housing affordability. Brazil and Indonesia, with urban populations of 185 million and 165 million respectively, illustrate the diverse trajectories through which countries can expand their urban demographics.

At the other end of the scale, some regions exhibit minimal urban population figures. Small nations like Liechtenstein, with only about 5,825 people living in urban areas, starkly contrast the sprawling metropolises above. The bottom five areas reflect unique demographic realities that may include geographical constraints, limited economic opportunities, or cultural preferences for rural living. Such disparities underscore the varying paths of urbanization across the globe.

Tracking urban population growth has practical implications for global decision-making. A continuously growing urban population implies the necessity for sustainable urban planning. Strategies include improving public transport, enhancing waste management systems, and building affordable housing. Implementing smart city solutions that leverage technology to optimize resource use can also improve the quality of life in urban settings. Additionally, fostering community participation in urban planning can encourage local solutions tailored to specific challenges faced by cities.

However, should deficiencies arise in interpreting urban population data, it may lead to flawed policies and ineffective strategies. Misalignments between population data and resource allocation can exacerbate urban challenges, resulting in slum development, inadequate public services, and increased inequality. Thus, the accuracy and timeliness of urban population statistics are critical in shaping effective solutions.

The world, from 1960 to 2023, has seen a remarkable increase in urban population—from about 1 billion in 1960 to nearly 4.6 billion in 2023. This rapid urban growth highlights the ongoing migration patterns and demographic shifts that shape global landscapes. The world is witnessing urbanization becoming one of the primary forces driving economic and social change. As such, continual monitoring and analysis of the urban population are indispensable for understanding emerging trends and challenges faced by urban areas across different regions.

In conclusion, the urban population metric is not merely a numerical representation but a crucial reflection of global societal trends, economic potential, and the need for strategic planning. As countries navigate the complexities of rapid urbanization, leveraging this data will be vital in crafting sustainable solutions that allow cities to thrive while addressing the challenges of an ever-growing urban populace.

                    
# 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'

# 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'

# 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))