LandCast: Locally Adaptive, Spatially Explicit Population Projections

Organization: Oak Ridge National Laboratory
Year: 2016

LandCast: Locally Adaptive, Spatially Explicit Population Projections is the first flexible, large scale, adaptive spatial population projection algorithm that addresses the local characteristics of unique geographic areas. To account for local factors that affect population change at the national level, population gravity models were incorporated as well as multivariate methods in the construction of LandCast. The LandCast algorithm is designed to represent one of many potential population futures; its implementation can be based on a business-as-usual scenario, or it can be tailored to user-defined scenarios that factor in specific events, policies or conditions affecting the geographic constraints of population growth, thus limiting modeled scenarios only to the user’s innovation. LandCast uses scenario-driven modeling to produce a range of spatially explicit population futures, and as such should be viewed as both a model and a dataset.

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