Human Mobility in the context of Environmental and Climate Change: Assessing current and recommended practices for analysis within DTM

Contact
DTM AKO Unit, dtmlondon@iom.int
Language
English
Location
Snapshot Date
Nov 26 2020
Activity
  • Survey
  • Flow Monitoring
  • Mobility Tracking

Summary

The objective of this document is to provide the readers with a better understanding of how DTM can contribute to data gathering and analysis on human mobility in the context of environmental degradation, climate change and disasters, and help address relevant policy and operational questions.

The paper formulates recommendations for DTM practitioners to improve tools and explore new analytical approaches to allow IOM to be at the forefront in this  field, that is and will be increasingly relevant for programme design and implementation, national governance, regional cooperation, and the achievement of global objectives related to climate change, migration and sustainable development.

Through a critical analysis of current DTM practices and drawing lessons from previously undertaken data collection work, the paper assesses DTM current contributions, relevant challenges and opportunities for understanding human mobility in the context of environmental degradation, climate change and disasters. This introspective process serves to highlight opportunities to improve current tools and practices, as outlined in a set of targeted recommendations.

Finally, this paper identifies possible options for more advanced analyses that could be carried out by taking into consideration external sources of data, such as publicly available meteorological, climatic and environmental databases, alongside information collected through DTM. The thematic paper thus seeks to assist practitioners in considering more consistently environmental factors in the context of population movements, and to apply an analytical lens focused on this intersection when interpreting data.

For the purpose of this paper, DTM practices and MECC-specific studies ranging from 2014 to 2019 were analysed.