Understanding the spatial patterns of human-wildlifeconflict is essential to inform management decisionsto encourage coexistence, but it is constrained bythe lack of spatially-explicit data. We collectedspatially-implicit data of human-wildlife conflicts from2009–2015 around Daxueshan Nature Reserve,Yunnan, China, and investigated the patterns anddrivers of these conflicts. A questionnaire was alsodesigned to capture local resident attitudes towardinsurance-based compensation for the losses causedby targeted wildlife. We found that the Asiatic blackbear (Ursus thibetanus) was the most conflict-proneanimal around the reserve, followed by the rhesusmacaque (Macaca mulatta) and Southeast Asiansambar (Cervus equinus). Conflicts were unevenlydistributed among seasons, villages, and communities,with several grids identified as conflict hotspots.Poisson models revealed that human-bear conflictswere negatively related to distance to the reserveand proportion of forest, but positively correlatedto the proportion of cropland. Binomial modelsshowed that communities affected by crop depredationwere positively correlated with the proportion ofcropland and negatively correlated with distanceto the reserve, whereas communities affected bylivestock depredation were negatively correlated withthe proportion of cropland. The insurance-basedscheme has compensated over 90% of losses, tothe satisfaction of 90.6% of respondents. Our resultssuggest that human-bear conflict could be potentiallyreduced by eliminating food crops near the reserveboundary and livestock grazing at conflict hotspots.In addition, the insurance-based scheme could bereplicated at a broader scale with improvement in lossassessment.