Abstract
AbstractMost data‐gathering tasks for eliciting information about the structure of spatial images tend to impose predetermined arbitrary dimensions or categories on the subjects' responses. This paper, in contrast, makes use of an unstructured task (word association) from which measures of semantic similarity between metropolitan areas in California are generated. The interpretation of the areal groupings derived from the analysis of these data by a clustering method reveals the importance of location and size of urban areas in cognitive organization. When the dimensions underlying perceived similarities between places are extracted by a factor analytic model, climatic and environmental dimensions are found to account for more variance in the word‐association data than do social dimensions.
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