Large Scale Information Surfaces: An Analysis of City Name Recalls in the United States

This paper examines how large scale spatial information surfaces are constructed, and how this construction varies according to an individual's location. Data consisting of U.S. city names recalled in a given timeframe are obtained from subjects in three U.S. cities: Buffalo NY, East Lansing MI, and Seattle WA. These data are used to calibrate a multivariate model, specific to each of the three sites, linking the probability of a city being recalled from memory to distance from the site, population size, its proximity to other cities, and whether the city is a state capital. As well as presenting information on the effect of these variables on spatial information storage and processing, evidence is presented which suggests that individuals tend to process spatial information hierarchically. The results thus provide a useful link between the cognitive aspects of spatial information processing and the mathematical modeling of spatial choices.