Oxford

Predicting human displacement post-disaster through an open software package with historical data

Key information
Country/ies
Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, The, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bosnia and Herzegovina, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo, Rep., Costa Rica, Côte d’Ivoire, Croatia, Cuba, Cyprus, Czechia, Democratic People's Republic of Korea, Democratic Republic of the Congo, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Estonia, Eswatini, Ethiopia, Falkland Islands, Fiji, Finland, Former Yugoslavia, France, Gabon, Gambia, The, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Islamic Rep., Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Kuwait, Kyrgyzstan, Lao People's Democratic Republic, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Republic of, Mexico, Micronesia, Fed. Sts., Monaco, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Republic of Korea, Republic of Moldova, Romania, Russian Federation, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Serbia and Montenegro, Seychelles, Sierra Leone, Singapore, Sint Maarten (Dutch part), Slovak Republic, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, State of Palestine, Sudan, Suriname, Sweden, Switzerland, Syrian Arab Republic, Tajikistan, Tanzania, United Republic of, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Türkiye, Turkmenistan, Tuvalu, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States of America, Uruguay, Uzbekistan, Vanuatu, Venezuela (Bolivarian Republic of), Viet Nam, Yemen, Zambia, Zimbabwe
Region/s
Data sources

Summary

The project consists of developing an open statistical software package using historical data of human displacement post-disasters to forecast future trends after catastrophes, specifically, earthquakes and cyclones. It aims to help governments and international organizations more rapidly allocate resources to facilitate recovery, estimate more accurately the displaced population, predict potential settlement ‘hotspots,’ and estimate the optimal shelter locations for a precise number of people.

 

The software package entails two main two components:

  • a back-end system that combines the data, model, and state-of-the-art statistical methods into a predictive tool, and
  • an interactive front-end system that visualizes the data and predictions.

Results

The tool has predicted past displacement estimates for over one hundred earthquakes in 38 different countries with at least ten times more accuracy than world-leading risk models produced by the United States Geological Survey (USGS) and the Global Disaster Alerting Coordination System (GDACS).

The tool, which combines software with mobile phone data-based displacement estimates provided by organisations such as Flowminder or Facebook Data for Good, has produced a detailed mapping of the returned and displaced populations over time.  This methodology has been implemented already with data-holders such as Flowminder and Meta Data for Good.

The tool is currently being integrated into for the Internal Displacement Monitoring Centre (IDMC)’s risk models and there is discussionsto integrate it into the International Federation of the Red Cross (IFRC)’s risk models.

Last modified
16 May 2022