Facilitating human-wildlife cohabitation through conflict prediction
  With increasing world population and expanded use of forests as cohabited
regions, interactions and conflicts with wildlife are increasing, leading to
large-scale loss of lives (animal and human) and livelihoods (economic). While
community knowledge is valuable, forest officials and conservation
organisations can greatly benefit from predictive analysis of human-wildlife
conflict, leading to targeted interventions that can potentially help save
lives and livelihoods. However, the problem of prediction is a complex
socio-technical problem in the context of limited data in low-resource regions.
  Identifying the "right" features to make accurate predictions of conflicts at
the required spatial granularity using a sparse conflict training dataset} is
the key challenge that we address in this paper. Specifically, we do an
illustrative case study on human-wildlife conflicts in the Bramhapuri Forest
Division in Chandrapur, Maharashtra, India. Most existing work has considered
human-wildlife conflicts in protected areas and to the best of our knowledge,
this is the first effort at prediction of human-wildlife conflicts in
unprotected areas and using those predictions for deploying interventions on
the ground.
