Secure Internet servers

The indicator of 'Secure Internet Servers' plays a crucial role in the modern digital landscape. With the internet increasingly permeating every aspect of our lives, from business transactions to social interactions, the need for secure communication and data protection has never been more pressing. Secure Internet Servers refer to the servers that provide secure access to websites, utilizing encryption protocols (such as HTTPS) that protect the integrity and confidentiality of data exchanged between users and servers. Their prevalence can be seen as a direct measure of a country's digital security posture, as well as its commitment to safeguarding users' online experiences.

The importance of this indicator cannot be overstated. Secure Internet Servers are foundational to building trust in online platforms. When users know that a website is secured by an SSL certificate, they are more likely to engage with the site, share personal information, or make transactions. A high density of secure servers in a region can thus correlate with higher levels of e-commerce activity, greater user engagement with online services, and overall digital economic growth. Furthermore, in an era of increasing cyber threats, having robust security measures in place is crucial for countries to protect their digital infrastructure and citizens from potential attacks.

In examining the relations to other indicators, Secure Internet Servers can be interconnected with various aspects of digital infrastructure, including internet penetration rates, overall cybersecurity capabilities, and the literacy of the population regarding online security practices. For instance, countries with high internet usage might require a more extensive infrastructure of secure servers to safeguard their user base, thus leading to a positive correlation. Similarly, nations investing in cybersecurity education will likely foster a higher demand for secure servers, as citizens become more aware of the importance of online security.

Several factors affect the number of secure internet servers within a region. First, the level of government regulation and policies regarding internet and cybersecurity can drive investment in secure servers. Countries with stringent data protection laws typically require businesses to adopt secure communication practices, leading to an increase in secure servers. Financial investment in IT infrastructure, developable technology sectors, and a general cultural emphasis on security can further promote the proliferation of these servers. However, the technological capabilities and economic conditions of a country also play a significant role. Countries with advanced technological ecosystems and strong economies, like the United States and Germany, have a higher number of secure servers compared to smaller or less economically developed nations.

The 2023 data highlights a striking growth trend in the number of secure internet servers over the years. The median value for 2023 stands at 2,217, which suggests that while there is substantial routing of resources towards securing internet servers, the available numbers indicate a disparity among different regions. The top five areas are dominated by developed nations: the United States leads by a staggering margin with over 62 million secure servers, followed by Germany with about 11.6 million, Japan with almost 4 million, France with 3.5 million, and the Netherlands with roughly 3.5 million as well. This distribution signals a concentration of secure server resources in the more economically powerful nations, which typically have both the requirement and capacity to invest in such digital infrastructure.

Conversely, the bottom five areas are starkly different, showcasing minimal secure internet server presence. Eritrea, Kiribati, Nauru, Tuvalu, and the Marshall Islands have, respectively, only 1 to 6 servers. This significant gap illustrates not only an alarming disparity in access to secure online platforms but also raises questions regarding cybersecurity readiness and the overall health of internet infrastructure in these regions.

Looking at the global trend from 2018 to 2023, it is observable that there has been a marked increase in secure internet servers, from approximately 46 million in 2018 to over 124 million in previous years, reaffirming the growing prioritization of online security. Nevertheless, the numbers can be seen as fluctuating, with a peak in 2022 and a slight drop in 2023, which might be influenced by various factors such as market saturation or shifts in technology usage patterns.

Strategies for increasing the number of secure internet servers revolve around heightened awareness and investment. Governments and institutions need to establish clearer regulations surrounding data protection, incentivize businesses to adopt secure practices, and invest in educational campaigns to enhance cybersecurity literacy among citizens. For developing nations, international cooperation and support from established cybersecurity firms could be a pivotal component, fostering a more robust approach to secure server adoption.

While the indicators present a generally positive trend, the flaws and challenges in relying solely on secure internet server counts must be acknowledged. The mere presence of these servers does not guarantee effective security; misconfigurations, poor maintenance, and inadequate encryption practices can undermine the security that these servers are supposed to provide. Additionally, the focus on quantitative measures should not overshadow qualitative assessments of user benefits, secure practices, and the overall ecosystem of cybersecurity.

In summary, Secure Internet Servers are a vital indicator of both the security landscape and digital trust for users worldwide. While significant advancements have been made over the years, especially in developed countries, ongoing efforts are required to bridge the gaps seen in less developed regions. With technological evolution and increasing online threats, the commitment to maintaining and expanding secure internet servers will be integral to fostering a safer and more reliable internet 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 = 'IT.NET.SECR'

# 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 <- 'IT.NET.SECR'

# 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))