English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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