Country

Indicator

Value type

Values present

Marine protected areas (% of territorial waters)

Source: worldbank.org, 03.09.2025

Year: 2024

Country	Value	Value change, %	Rank
Aruba	0		93
Angola	0		93
Albania	3.4	+21.4%	67
United Arab Emirates	12	+4.35%	38
Argentina	12	+1.69%	38
American Samoa	8.7	0%	47
Antigua & Barbuda	0.7	+133%	86
Australia	45.4	+2.48%	7
Azerbaijan	1	+150%	83
Belgium	38.3	+0.525%	10
Benin	0		93
Bangladesh	8	0%	50
Bulgaria	8.2	+1.23%	49
Bahrain	21.4	+1.42%	24
Bahamas	7.7	-2.53%	53
Bosnia & Herzegovina	0		93
Belize	20.4	+85.5%	25
Bermuda	0		93
Brazil	26.6	-0.746%	17
Barbados	0		93
Brunei	0	-100%	93
Canada	9.2	+1.1%	45
Chile	41.2	-0.483%	8
China	4.8	-12.7%	58
Côte d’Ivoire	0.1	0%	92
Cameroon	11.4	+4.59%	41
Congo - Kinshasa	0.5	+150%	88
Congo - Brazzaville	3.5	+16.7%	66
Colombia	41.1	+140%	9
Comoros	0.4	0%	89
Cape Verde	0.1		92
Costa Rica	28.4	-2.07%	15
Cuba	4.2	+7.69%	63
Curaçao	0		93
Cayman Islands	0.1	0%	92
Cyprus	8.6	0%	48
Germany	45.4	-0.22%	7
Djibouti	0.2	0%	91
Dominica	0.1		92
Denmark	18.6	+1.64%	28
Dominican Republic	13.3	-23.1%	36
Algeria	0.1	0%	92
Ecuador	19.3	+1.05%	26
Egypt	4.6	-8%	60
Eritrea	0		93
Spain	12.8	0%	37
Estonia	19.3	+2.66%	26
Finland	11.8	-1.67%	40
Fiji	0.9	0%	84
France	49.8	0%	5
Faroe Islands	0		93
Micronesia (Federated States of)	0		93
Gabon	26.9	-6.6%	16
United Kingdom	46.8	+6.12%	6
Georgia	0.8	+14.3%	85
Ghana	0.1	0%	92
Gibraltar	0		93
Guinea	0.6	+20%	87
Gambia	0.6	0%	87
Guinea-Bissau	9.1	+1.11%	46
Equatorial Guinea	0.3	+50%	90
Greece	4.7	+4.44%	59
Grenada	0.1	0%	92
Greenland	4.2	-6.67%	63
Guatemala	0.9	+12.5%	84
Guam	0		93
Guyana	0.1		92
Hong Kong SAR China	0		93
Honduras	4.9	+6.52%	57
Croatia	9.3	+3.33%	44
Haiti	1.8	+20%	76
Indonesia	3	-3.23%	69
Isle of Man	10.8		42
India	0.3	+50%	90
Ireland	2.4	+4.35%	72
Iran	0.9	+12.5%	84
Iraq	0.3		90
Iceland	0.5	+25%	88
Israel	0.6		87
Italy	10.7	+0.943%	43
Jamaica	0.7	-12.5%	86
Jordan	2.8	+180%	71
Japan	13.8	-0.719%	35
Kazakhstan	52.5	+3.55%	4
Kenya	0.6	-14.3%	87
Cambodia	1.5	+7.14%	79
Kiribati	11.9	+0.847%	39
St. Kitts & Nevis	4.5	+12.5%	61
South Korea	2.2	-12%	74
Kuwait	3.2	0%	68
Lebanon	0.2	0%	91
Liberia	0.1	0%	92
Libya	0.6	0%	87
St. Lucia	0.3	+50%	90
Sri Lanka	0.1	0%	92
Lithuania	23.2	-9.38%	20
Latvia	16.4	+2.5%	31
Saint Martin (French part)	97	+0.622%	3
Morocco	0.3	-57.1%	90
Monaco	100	+0.301%	1
Madagascar	1.3	+44.4%	80
Maldives	0.1	0%	92
Mexico	22.6	+4.63%	21
Marshall Islands	0.3	0%	90
Malta	7.8	+5.41%	52
Myanmar (Burma)	0.5	0%	88
Montenegro	5.6	+833%	54
Northern Mariana Islands	32.4	+0.621%	13
Mozambique	2.2	+4.76%	74
Mauritania	3.7	-11.9%	64
Mauritius	0		93
Malaysia	4.8	-14.3%	58
Namibia	1.7	0%	77
New Caledonia	100	+3.84%	1
Nigeria	0		93
Nicaragua	3.6	+5.88%	65
Netherlands	33.6	+24.9%	11
Norway	1	-28.6%	83
Nauru	0		93
New Zealand	30.4	0%	14
Oman	16.2	+3,950%	32
Pakistan	1.2	+50%	81
Panama	26.3	-1.87%	18
Peru	7.9	-1.25%	51
Philippines	1.6	-5.88%	78
Palau	98	-2%	2
Papua New Guinea	0.1	0%	92
Poland	24	+6.19%	19
Puerto Rico	1.9	+5.56%	75
North Korea	0		93
Portugal	16.8	-0.592%	30
Palestinian Territories	0		93
French Polynesia	0		93
Qatar	2.3	0%	73
Romania	22.1	-4.33%	22
Russia	2.2	0%	74
Saudi Arabia	3.5	+40%	66
Sudan	17	+6.25%	29
Senegal	2.2	+15.8%	74
Singapore	0		93
Solomon Islands	0.1	0%	92
Sierra Leone	1.7	+6.25%	77
El Salvador	0.8	+14.3%	85
Somalia	0		93
São Tomé & Príncipe	0		93
Suriname	1.5	0%	79
Slovenia	2.9	+26.1%	70
Sweden	16	+1.27%	33
Sint Maarten	5.1	-41.4%	55
Seychelles	32.6	-0.61%	12
Syria	0.2	0%	91
Turks & Caicos Islands	0.2	+100%	91
Togo	0.7	+250%	86
Thailand	5	+4.17%	56
Turkmenistan	4.2	+40%	63
Timor-Leste	0.7	-50%	86
Tonga	0.1	0%	92
Trinidad & Tobago	0.1		92
Tunisia	1.1	+10%	82
Turkey	1.7	-5.56%	77
Tuvalu	0		93
Tanzania	2.3	-23.3%	73
Ukraine	21.6	+135%	23
Uruguay	1.5	+87.5%	79
United States	19	-0.524%	27
St. Vincent & Grenadines	0.2	0%	91
Venezuela	4.4	+2.33%	62
British Virgin Islands	0		93
U.S. Virgin Islands	0.8	-11.1%	85
Vietnam	0.5	-16.7%	88
Vanuatu	0		93
Samoa	0.2	+100%	91
Yemen	0.4	-20%	89
South Africa	15.4	-0.645%	34

Python
R

                    
# 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 = 'ER.MRN.PTMR.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 <- 'ER.MRN.PTMR.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))

Compare

Marine protected areas (% of territorial waters)

Year: 2024

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