ABSTRACT
There has been an increasing demand from clients for predictive location models in cultural resource management (CRM). Resource developers regularly require a geographical information systems-based (GIS) model that will optimize time in the field, thereby minimizing expense and unanticipated discoveries during the construction phase. GIS-modeled locational surfaces of landscapes that incorporate numerous environmental and cultural variables are categorized by cumulative numerical values. Higher values are areas of higher site potential, and lower values of lower site potential. The importance of defining and testing areas of both lower and higher site potential is fundamental for guiding survey efforts, i.e. confirming areas with higher values as holding most cultural resources, and confirming areas with lower values as having fewer cultural resources via empirical observation. 1
