Pipeline to identify dominant features in spatial data

Dominant-feature identification decomposes spatial data into several additive components to make different features apparent on each component. It recognizes their dominant credibly and assesses feature attributes. This paper describes the pipeline apply this method regular irregular lattice as well geostatistical data. These implementations are all openly available templates for case provided in an associated git repository. As is typically large, we propose efficient approximations suitable such Emphasizing use of these context dominant-feature identification, them from a climate model describing monthly mean diurnal range period between years 2081 2100.