Machine learning and phone data supporting humanitarian efforts
Over the last decade, the world has seen significant growth in the uptake of mobile phones and data-driven technology, which has opened up possibilities for applying machine learning algorithms and phone data in humanitarian aid efforts. Machine learning is a form of artificial intelligence that enables computers to ‘learn’ by automatically adapting and improving from experience without being explicitly programmed. Phone data records information regarding location, usage, and environment and can provide real-time insights into events and population movements to inform assistance efforts, such as natural disaster relief or refugee assistance programs.
For example, machine learning algorithms have been developed to detect earthquakes using phone data automatically. When an earthquake is detected, the algorithm can compare changes in areas with existing and predicted population data to gauge the magnitude of the disaster and, therefore, the level of aid required. This data can be continuously monitored to adjust aid efforts according to the intensity of the impacts and associated needs, aiding with delivering food and vital supplies.
Real-time location tracking capabilities for vulnerable populations can also be a powerful tool for aid efforts. In refugee camps, mobile phones could be tracked to ensure people remain within established boundaries, alerting aid workers if boundaries are breached to ensure that people do not enter risky locations. Additionally, phones can gather information about areas near a refugee camp and inform aid efforts if connected to the local cellular network. Phone data can also become an invaluable source of data on environmental conditions and populations. It can be incorporated into existing aid programs to enhance them with the most accurate and up-to-date information possible.
Machine learning can also be helpful for data analysis of incident reports and feedback from aid workers, allowing for more sophisticated identification of more challenging humanitarian problems. Crowdsourced data from reports can be supplemented by aid group feedback to automatically monitor ongoing relief efforts and suggest where aid is most needed and when. This is especially helpful for disasters in remote areas, where aid groups can scale up their assistance efforts.
Using machine learning and phone data for humanitarian aid can be extremely valuable and cost-effective, eliminating the need for physical resources. With further refinement and development, the potential for these technologies' contribution to delivering aid is massive. On top of this, machine learning and phone data can be used to empower those affected by humanitarian disasters and better understand the extent of their needs, helping to ensure that future assistance is provided in an as timely and appropriate manner as possible.