Spatial Dependency in Local Resource Distributions

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Abstract

We investigated the spatial patterns of different classes of resources in a familiar local environment. Past psychological research investigating why humans are so prone to misunderstand random data sets has typically focused on empirical resource distributions of equal base rates and squared arrangements—such as a 10 × 10 grid with resource spots that have 50 resources/tokens in it—to compute alternation probabilities that indicate the degree of spatial aggregation, randomness, or dispersion. We propose to incorporate a new statistical methodology from the spatial ecology literature to overcome these 2 limitations. Over recent semesters, we observed and coded various resources near our university campus from both developed and natural domains, such as seats taken at a café and in a restaurant, occupied parking spots, group members of geese and cows groupings, and patterns of wilderness, wild forest, and water in the nearby Adirondack State Park. Our data collection methodology for this exploratory study included the use of resource-specific coding sheets, flying of an aerial drone to obtain video footage of the animal distributions, and extracting patterns of land use from published New York State map data. Our results extend the available statistical tools for randomness research and provide novel evidence that natural resource domains indeed show more aggregated distribution patterns than those from human-developed resource domains. We discuss our results in light of claims that our ancestral human cognitive evolution selected for specific reasoning mechanisms to detect resources that are distributed in clumps or patches in space and time.

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