Fast and Exact Algorithm for Balanced Spatial Priority Ranking

ABSTRACT Aim Spatial (conservation) prioritization (SCP) can provide decision support for conservation planning and ecologically based land use planning in general. Technically, it involves integrated analysis of spatial data about the distributions of biodiversity features (species, habitat types, ecosystem services, etc.), costs and opportunity costs, threats (a.k.a. pressures; stressors), and land use restrictions. Here we provide a major improvement to a previously published priority ranking algorithm. Innovation We establish mathematical bounds that significantly limit the number of grid cells that need to be evaluated during each iteration of the priority ranking, and describe a novel algorithm for the computation of the spatial priority map. By comparing it to previously published algorithms, we show that the novel algorithm is both exact and very fast and that its performance significantly exceeds the previous methods with large high‐resolution data. Main Conclusions SCP can be computationally very challenging, because high‐resolution national to global analysis implies spatial data with tens of millions to billions of grid cells of data for potentially thousands of biodiversity features. The new algorithm provides exact solutions to problems that are larger than those that have been analysed with approximate solution methods before.