Spatial autocorrelation informed approaches to solving location–allocation problems

Surveying programs of study at institutions higher learning throughout the world reveals that one natural disciplinary coupling is statistics and operations research, although these two specific disciplines currently lack an active synergistic research interface. Similarly, development spatial optimization has occurred in parallel nearly isolation. This paper seeks to alter this situation by initiating transformative work interface subdisciplines, encouraging considerably more future interaction between them. It outlines three ways can contribute exploiting autocorrelation georeferenced data: missing attribute value imputation (analogous kriging); identifying colocations local hot spots medians; and, geographic tessellation stratified random sampling inputs heuristics successfully guide them optimal location solutions. One contention emphasized statistics/optimization furnishes another vehicle for delivering statistical benefits society, which, turn, providing better integration it into novel interdisciplinary contexts.