Assessing and mapping conflicting recreation values in state park settings using neural networks

As the complexity of land use decision making increases, information about multiple resources, public values and recreation opportunities, experiences, and behavior must be readily available in many formats to make intelligent decisions about resource allocation and management. We combined a photographic survey technique with a GIS and a Kohonen counter-propagation artificial NN to investigate the perceived impact of park management activities on recreation opportunities and experiences. We compared a selected group of public recreation groups and their perceived attitudes and values toward recreation management, and developed maps of conflicting values that reflect similarities and differences among these groups. The study demonstrates the potential of automated techniques such as NNs for storing the knowledge about the public's perceived values of landscape and the conflicting differences between each of these groups.