Landscape perceptual theories developed within the last decade which have been founded on a cognitive structure have clearly provided a useful, yet generalizable basis for improving the mapability of scenic assessment models. With the increase usage of Geographic Information Systems (GIS) and subsequently the development of more sophisticated spatial models, the application of such scenic assessment models are more easily validated and refined. This study looks at the mystery component of the Kaplan's "Information Processing Model" and develops a quantitative procedure for predicting and mapping mystery in the rural Indiana landscape. Ninety (90) color slides of rural landscape scenery were presented to 26 respondents who rated each photograph on a five point scale for mystery. Landscape composition classes were used to discover the interrelationships of the mystery dimensions and the physical landscape variables affecting their perception and were used to develop a model for mapping mystery. This research strongly supports the use of Kaplan's Information Processing Model as a reliable, comprehensive theoretical foundation for improving landscape assessment procedures. In addition, this research demonstrates how the mystery concept can be validated using a representative sample of the public which can be subsequently used for developing measures that both identify and describe physical landscape variables for mapping perceptual values of mystery in the rural Indiana landscape.
ASJC Scopus subject areas
- Geography, Planning and Development
- Ecological Modeling
- Environmental Science(all)
- Urban Studies