Leveraging advances in natural language processing to better understand Tobler's first law of geography

Abstract

Tobler's First Law of Geography (TFL) is one of the key reasons why is special. The law, which states that is related to everything else, but near things are more related than distant things, is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.