Mortality caused by road traffic injury (per 100,000 population)

The mortality caused by road traffic injury, reported per 100,000 population, is a critical indicator of public health and safety, reflecting the effectiveness of a nation’s transportation infrastructure, law enforcement, and healthcare systems. The understanding of this statistic is paramount in assessing road safety policies, understanding social behavior regarding transportation, and addressing public health challenges related to accidental injuries. Road traffic injuries not only result in loss of life but also contribute significantly to disabilities and economic burdens on healthcare systems and societies. In the year 2019, the world median value for mortality due to road traffic injuries stood at 15.3 per 100,000 population. This figure offers a glimpse into the overall health of a community concerning road safety. However, it is important to look beyond the median to appreciate the disparities among various regions. On one end of the spectrum, regions such as the Dominican Republic reported alarming figures at 64.6, followed closely by Zimbabwe at 41.2, Venezuela at 39.0, Liberia at 38.9, and Eritrea at 37.9. These statistics raise concerns about the traffic policies, road conditions, emergency response services, and general public behavior regarding road use in these areas. Contrastingly, there are regions that exhibit remarkably lower values. For instance, Antigua & Barbuda recorded zero deaths per 100,000 population, and other areas like Micronesia (Federated States of), Maldives, Kiribati, and Iceland followed with minimal fatalities, ranging from 0.2 to 2.0. The stark contrast between the highest and lowest figures underscores the varying levels of road safety and health across the globe, suggesting that effective traffic regulation, education, and infrastructure investment can lead to significantly safer environments. The relationship between road traffic injury mortality and other indicators is multifaceted. Variables such as the level of urbanization, economic development, healthcare accessibility, and law enforcement practices play crucial roles in shaping road safety outcomes. For instance, countries with strong law enforcement and strict adherence to traffic laws tend to have lower mortality rates. Meanwhile, economic factors can also influence both the number of vehicles on the road and the ability to invest in road safety measures like proper signage, regulations for driver education, and quality road maintenance. Several factors affect the mortality caused by road traffic injuries, including the physical condition of the roads, the quality of vehicles, and individual behaviors such as speeding, driving under the influence of alcohol, or not wearing seat belts or helmets. Societal attitudes towards road safety also play a significant role. In regions where road safety is culturally prioritized, individuals are more likely to adhere to traffic laws and engage in safer driving practices. Effective public education campaigns are essential to changing behaviors that result in risky driving practices. Addressing the high mortality rates from road traffic injuries requires a multi-faceted approach. First and foremost, enhancing the infrastructure is vital; this involves building safer roads that accommodate pedestrian traffic, ensuring road signage is clear and visible, and improving lighting in high-risk areas. Technological integrations, such as traffic monitoring systems and intelligent transportation systems that manage and monitor vehicular flow, can contribute significantly to reducing fatalities. Legislation plays a key role in shaping road safety and could include stricter penalties for traffic violations, regulations on car safety standards, and measures to curb driving under the influence of substances. Furthermore, investing in public transportation can reduce the number of vehicles on the roads and thus lower the potential for accidents. Encouraging a culture of responsibility among drivers through education campaigns that advocate for safe driving behavior is equally essential. Despite the efforts and strategies employed to combat road traffic injuries, gaps still exist, which may contribute to persistent high mortality rates. For instance, in many developing countries, resources may be limited; this can lead to insufficient law enforcement or inadequate public health response systems. Cultural norms valuing speed and efficiency over safety can also pose barriers to change. In conclusion, the mortality caused by road traffic injuries is more than just a number; it is a reflection of a society's commitment to ensuring safety for its population. The contrasting figures of high-risk and low-risk areas serve as a reminder of the work still needed in global traffic safety policies. Advances in infrastructure, enforcement of traffic laws, and education on road safety can lead to a drastic decrease in mortality rates, ultimately saving lives and improving the quality of life across communities. The shifting world values from 2000 to 2019 indicate a gradual improvement in overall traffic safety; however, the journey towards eliminating road traffic injuries falls upon collective efforts, encompassing policy-making, education, enforcement, and community involvement, to create a safer environment for all.

                    
# 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 = 'SH.STA.TRAF.P5'

# 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 <- 'SH.STA.TRAF.P5'

# 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))