Urban population growth (annual %)

The indicator 'Urban population growth (annual %)' is a vital measure that captures the dynamics of urbanization across the globe. This statistic represents the percentage increase in the urban population of a given area over a year. Understanding urban population growth is crucial for policymakers, urban planners, and researchers, as it reflects migration patterns, economic opportunities, and the health of urban infrastructure.

As of 2023, the median urban population growth rate worldwide is 1.6%, marking a slight increase from the previous year. The significance of this growth lies in its broad implications for social, economic, and environmental factors. Rapid urbanization can contribute to economic growth, but it also presents challenges such as increased demand for housing, transportation, and public services. In urban areas where growth is unsustainable, issues like overcrowding, pollution, and inadequate access to essential services can arise.

Examining the top five areas experiencing the highest urban population growth, we find Oman at 7.27%, Syria at 6.04%, Chad at 5.83%, South Sudan at 5.77%, and Kuwait at 5.59%. These rates are indicative of various socio-economic factors at play. For instance, Oman’s growth can be attributed to a combination of economic development and a relatively stable environment conducive to internal migration. In contrast, the high growth rates in conflict-affected areas like Syria and South Sudan highlight the effects of displacement, where people migrate to urban centers in search of safety and resources.

On the other hand, the bottom five areas reflect negative urban population growth: Ukraine at -8.17%, the Marshall Islands at -2.73%, Bulgaria at -2.56%, Moldova at -2.38%, and the Northern Mariana Islands at -1.95%. These declining rates reveal the complexities of urbanization. In the case of Ukraine, the negative growth can be largely attributed to the ongoing conflict, which leads to population displacement and emigration. For Bulgaria and other Eastern European nations, aging populations and low birth rates contribute to a shrinking urban populace as younger generations move abroad for better opportunities.

Yearly data analysis shows fluctuations in the urban population growth through the decades. Back in 1961, the global average stood at 2.83%, witnessing a gradual decline to around 1.67% in 2023. This downward trend over the years reflects broader global changes, including economic cycles, migration trends, and urban planning strategies that have either encouraged or stymied growth. The post-World War II baby boom initially fueled urban populations, but later economic downturns, such as the oil crises in the 1970s and shifting employment patterns in the 1980s and beyond, likely influenced urban growth dynamics.

Urban population growth is intricately linked to several other indicators, including GDP growth, employment rates, and infrastructure development. A positive correlation often exists between robust economic growth and increasing urban populations, as individuals move to cities seeking better job prospects. However, this relationship can be tempered by factors such as urban sprawl or insufficient infrastructure, which can lead to increased poverty levels within urban settings. Population density in cities can both stimulate economic activity and strain resources, showcasing the dual-edged nature of urban growth.

Several factors affect urban population growth, including economic opportunities, social stability, and environmental conditions. Economic development can drive urbanization, as cities often provide better access to jobs, education, and healthcare. On the contrary, political instability and conflict can spur unwanted urban growth as people flee rural areas or war-torn regions. Environmental factors, such as natural disasters or climate change, also play critical roles, pushing populations to urban centers that are perceived to offer more stability and resources.

To manage urban population growth effectively, cities and governments can adopt various strategies and solutions. Sustainable urban planning that emphasizes public transportation, green spaces, and affordable housing is vital to accommodate increasing populations without sacrificing quality of life. Policies directing investments towards infrastructure are essential to avert challenges caused by unexpected population surges. Moreover, community engagement can be instrumental in ensuring that urban development meets the needs of both current and future residents.

However, relying solely on these strategies may reveal some flaws. Rapid urban growth can outpace planning efforts, leading to informal settlements and increased inequality. Additionally, public services may struggle to keep up with population demands, potentially widening socio-economic gaps within urban areas. Without careful, proactive management, these challenges can exacerbate the difficulties faced by cities during periods of significant growth.

In conclusion, the urban population growth indicator serves as a key analytical tool for understanding shifting demographics and their ramifications. The increase or decrease in urban populations not only illustrates immediate human migration patterns but also signals potential long-term trends that could alter economic and social landscapes. By comprehensively analyzing these trends, stakeholders can better address the challenges and opportunities that arise in our increasingly urban world.

                    
# 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.GROW'

# 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.GROW'

# 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))