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Conservation planning, with its emphasis on nature reserves, provides a basis for the development of spatial plans, usually at regional scale, that explicitly state objectives and then provide options for achieving them, despite limited financial resources. Conservation planning, however, is still an imperfect science that places more importance on ecological considerations than on social ones. Complementing social considerations with an integrated understanding of the ecology of a region, and obtaining enough social data in a cost-effective manner, are recurrent challenges. Here, we address the potential of systematic planning to improve human–wildlife interactions. Mapping risks and opportunities with behavioural, social and economic data, e.g., would greatly facilitate management decisions. While data collection through conventional field methods is a constraint at large spatial scales, the huge and fast-growing amount of social data in the 'big data' space remains largely unexplored. We describe new, promising approaches for big data visualization and analysis that could be used to inform wildlife managers through easy-to-interpret, data-intensive approaches.
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