Agent-based machine learning for the prediction of refugee camps’ urban growth

Refugee camps are temporary places accommodating people who have been displaced from their homes due to an emergency, where this temporary period may extend up to a lifetime. The literature indicates that the quality of urbanism in refugee camps is typically poor due to the limited time allocated to urban planners for design. Refugee camps have an almost unplanned urban development, which leads to a lack of distribution of land uses and of security. This study aims to provide policymakers with a framework for an assistive tool to predict and intervene when necessary in a continuously changing social context of refugee camps’ urban growth. This framework utilises Agent-based Machine Learning (ABML) to predict the urban growth of the refugee camp, focusing on its physical and social aspects, as well as its specific functions.