Clustering Nigeria’s IDP Camps for Effective Budgeting and Re-Settlement Policies Using an Optimized K-Means Approach

Abstract

ABSTRACT: The increasing number of IDP camps in Nigeria due to insurgency and displacement has continued to pose serious policy as well as budget issues. The challenges and needs of these camps differ and require proper identification and isolation so as to direct limited resources effectively. Using the demographic data published by Displacement Tracking Matrix (DTM) on the IDP camps for 2021, an intelligent and optimized K-means cluster analysis was employed to classify the camps and identify their unique features for the purpose of guiding government policies and budget aimed at improving the standard of living in the camps. In four clusters, the model successfully classified 164 of the camps as having major poverty and health issues, 114 as having overcrowding and health issues, eleven as having overcrowding issues, and thirteen as living a normal life. The results provide a suggestive guide for imminent aid .