Methodological guide on the use of mobile phone data: Displacement and Disaster Statistics

The Guideline on the Use of Mobile Phone Data (MPD) for Displacement and Disaster Statistics aims to provide practical guidance on how to collect the statistics on displacement and population movements in disaster context using information from Big Data in compliance with the new United Nations (UN) recommendation on migration Statistics. It reviews the MPD as an alternative (non-traditional) source for generating and/or filling the gap in displacement and disaster statistics.  This Handbook is part of a series of five (5) handbooks developed by the UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD) Task Team on MPD which are designed to guide countries who plan to use MPD for statistics and indicators in displacement and disaster, information society, dynamic population, migration, and tourism.

Preparation of this handbook was led by the subgroup of displacement and disaster statistics that is composed of members from The University of Tokyo/Spatial Data Commons (subgroup lead), Flowminder Foundation, UN Global Pulse Lab Jakarta, Telenor Research, Positium, BPS Indonesia, IOM, ITU with technical support and guidance from the United Nations Statistics Division and the UN-CEBD Task Team on MPD. 

Table of Contents

• Abbreviations
• 1. Introduction
• 2. Background and context
• 3. Scope of displacement and disaster statistics in this guide
• 4. CDR data characteristics
  • 4.1 Advantages
  • 4.2 Limitations and caveats
  • 4.3 Data privacy and protection
• 5. Institutional frameworks and analytical pipelines
  • 5.1 Importance of institutional framework and analytical pipelines
  • 5.2 Establishing institutional frameworks and analytical pipelines
• 6. Key steps taken for computing displacement and disaster statistics
  • 6.1 Quality assurance
  • 6.2 Establishing a baseline for analysis
  • 6.3 Computing displacement and disaster statistics
    • 6.3.1 Detecting displacements and temporal relocations
    • 6.3.2. Measuring the effects of disasters and interventions
• 7. How the statistics informed decision-making
• 8. Alternative data sources
• 9. Conclusion and recommendations
• References
• Appendices: Use cases
  • Appendix 1: Benefits of having an institutional framework and analytical pipeline
    • Appendix 1.1. Turkey (earthquake)
    • Appendix 1.2. Guinea, Liberia, and Sierra Leone (Ebola)
    • Appendix 1.3. Norway (COVID-19)
    • Appendix 1.4. Mozambique (cyclones)
    • Appendix 1.5. Ghana (COVID-19)
    • Appendix 1.6. DRC, Sierra Leone and Curacao (COVID-19)
    • Appendix 1.7. The Gambia (COVID-19)
    • Appendix 1.8. Haiti (earthquake)
  • Appendix 2.1. Detecting displacements and relocations
    • Indonesia (earthquakes, tsunami)
    • Nepal (earthquake)
  • Appendix 2.2. Measuring the effects of disasters and interventions
    • Bangladesh (cyclone)
    • Haiti (cholera)
    • The Gambia (COVID-19)
    • Sierra Leone (COVID-19)