Using neural nets to model the spatial distribution of seasonal homes.

Introduction

A variety of factors influence the occurrence of seasonal homes. Coppock (1977) notes that, globally, the main controlling factors of seasonal home distribution appear to be distance from major population centers; the quality and character of a region; the presence of sea, rivers, or lakes; the presence of other recreational resources; the availability of land; and climate. Spotts (1991) notes that seasonal homes air concentrated in areas with many lakes, rivers, forests, and other natural resources. The high density of seasonal homes in some areas coupled with the treed for frequent travel to the destination generally results in a high demand for high-volume travel accessibility (Page and Getz 1997). "Easy travel" was the most important factor in buying a seasonal home (Coppock 1977).

Seasonal homes are found primarily in areas away from heavily populated urban areas (Coppock 1977). Models of second and seasonal home use in Canada (Lundgren 1974) identify seasonal home developments located far from urban centers and slowly being subsumed by growing local urban development over time. Thus, while seasonal homes are built in rural surroundings, they are within a short distance from modern amenities. Also, primarily agricultural regions contain fewer seasonal homes (Chubb and Chubb 1981).

This paper describes a spatial model of seasonal home distribution in the Upper Great Lakes area of the United States. The model utilizes a unique computational tool--artificial neural networks--which is integrated with geographic information science to numerically quantify spatial relationships that are important to the occurrence of seasonal homes in the Upper Great Lakes region. We present an overview of methods, the results of model performance, and a discussion of these results in relation to seasonal home development and the techniques applied to the development of the model.

Other factors account for the location of a seaso...

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