The dots connecting hunger and pollution

Samuel Escapa

Countries Analyzed

Afghanistan
Algeria
Angola
Arab World
Argentina
Armenia
Azerbaijan
Bangladesh
Barbados
Belize
Benin
Bolivia
Botswana
Brazil
Brunei
Burkina Faso
Cambodia
Cameroon
Cape Verde
Caribbean small states
Central African Republic
Chad
Chile
China
Colombia
Congo
Costa Rica
Cote d'Ivoire
Cuba
Djibouti
Dominican Republic
Early-demographic dividend
East Asia & Pacific
East Asia & Pacific (IDA & IBRD)
East Asia & Pacific (excluding high income)
Ecuador
Egypt
El Salvador
Ethiopia
Fiji
Gabon
Gambia
Georgia
Ghana
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Heavily indebted poor countries (HIPC)
Honduras
IBRD only
IDA & IBRD total
IDA blend
IDA only
IDA total
India
Indonesia
Iran
Iraq
Jamaica
Jordan
Kazakhstan
Kenya
Kiribati
Kuwait
Kyrgyzstan
Laos
Late-demographic dividend
Latin America & Caribbean
Latin America & Caribbean (IDA & IBRD)
Latin America & Caribbean (excluding high income)
Least developed countries: UN classification
Lebanon
Lesotho
Liberia
Low & middle income
Low income
Lower middle income
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mauritius
Mexico
Middle East & North Africa
Middle East & North Africa (IDA & IBRD)
Middle East & North Africa (excluding high income)
Middle income
Mongolia
Morocco
Mozambique
Myanmar
Namibia
Nepal
Nicaragua
Niger
Nigeria
North Korea
Oman
Other small states
Pacific island small states
Pakistan
Panama
Paraguay
Peru
Philippines
Pre-demographic dividend
Rwanda
Saint Vincent and the Grenadines
Samoa
Sao Tome and Principe
Saudi Arabia
Senegal
Sierra Leone
Small states
Solomon Islands
South Africa
South Asia
South Asia (IDA & IBRD)
South Korea
Sri Lanka
Sub-Saharan Africa
Sub-Saharan Africa (IDA & IBRD)
Sub-Saharan Africa (excluding high income)
Suriname
Swaziland
Tajikistan
Tanzania
Thailand
Timor
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Uganda
United Arab Emirates
Upper middle income
Uruguay
Uzbekistan
Vanuatu
Venezuela
Vietnam
World
Yemen
Zambia
Zimbabwe


Dataset 1: Hunger Index

I'm using the Global Hunger Index Dataset by the World Bank, collected for the Sustainable Development Goal. The dataset has 3740 rows, collecting data since 1991. The total file size is 105kB. Columns are: The columns of this dataset of 1.3MB are the folling:

Entity
Code
Year
Prevalence of undernourishment (World Bank 2017 & UN FAO SOFI (2018)) (%)

This dataset is the calculated GHI for each country. No modification was done to the dataset, other than organizing them for the charts themselves. The data represents the aomunt of people that are undernourished in a country, as well as the mortality rate, and children affected - per year.
This data will be used in comparision with the second data set which includes the pollution index of these countries, where we will display the number of people truly affected by world hunger, as well as how it connects to pollution in these countries.


Dataset 2: Pollution Index

Link to dataset
World Health Organization, WHO Global Urban Ambient Air Pollution Database (update 2016).

The columns of this dataset of 1.3MB are the folling:

  Country (converted to Lat,Lon with Geocoder)
  City/Town
  Year
  Annual mean PM10
  ug/m3 PM10
  Temporal coverage PM10
  note on converted PM10
  Annual mean PM2.5
  ug/m3 PM2.5
  Temporal coverage PM2.5
  note on converted PM2.5
  Number and type of monitoring stations
  Reference for air quality
  Database version (year)
  status
The dataset (1.3MB) contains 2935 rows, with 15 columns.

This data set has data about air quality index, more specifically the concentration of pollutants such as PM10 and PM2.5 in countries all around the world. This data set collects data from cities, so this dataset will be processed by calculating the average index of each country, so that it can be compared with countries in the first dataset.
Understanding further what these pollutants do, they could be linked the higher death rates, which could be linked as well to food malnutrition.

Inspiration:
Hannah Ritchie and Max Roser (2019) - "Air Pollution". Published online at OurWorldInData.org.