Application of CRISP-DM methodology for managing human-wildlife conflicts: an empirical case study in India

Abstract

Human-wildlife conflict (HWC) is a major concern for protected area management. Managing HWC around protected areas requires structured and replicable processes to reduce subjectivity and promote adherence to good governance principles. The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely-used process model for structured decision-making. This study demonstrates the novel application of CRISP-DM to HWC related decision-making. We apply CRISP-DM and conduct hotspot and temporal (monthly) analysis of HWC data from Ramnagar Forest Division, India. Based on the patterns of crop loss, livestock loss, and human loss, we propose conflict-type and species-specific preventive strategies. A qualitative assessment of the initial outcomes of the ongoing implementation finds the preventive strategies to be effective. We suggest a participatory approach, localization of strategy, and need for data management as opportunities for improvement.