Using Neural Nets to Model the Spatial Distribution of Seasonal Homes

Abstract

Seasonal and second homes are important aspects of recreational tourism. Owning a summer cottage, time-share condominium, hunting cabin, or a part-time residence in a location away from home affects development patterns in significant ways. This paper presents the results of using an artificial neural network and a geographic information systems-based approach to identify and quantify the principal predictors of seasonal home distribution within the Lake States region of Minnesota, Wisconsin, and Michigan. Representative variables, such as proximity and spatial configuration of lakes, proximity to the Great Lakes shore, surrounding forest acreage, and public land access have been quantified using geographic information systems at the minor civil division (MCD) scale for the three states. The GIS data have then been fed into artificial neural networks to enable these pattern recognition tools to identify the principal predictors of seasonal home distribution in the Upper Great Lakes States.