Many Kinds of Distance

At the heart of this endeavor is a simple but profound question: what does distance mean in the 21st century? At some point in history, geographical proximity may have implied cultural proximity, but this is increasingly being called into question, as can be seen in everything from voting patterns to the distribution of third-wave coffee shops across the United States. We intuitively felt that Bushwick, Brooklyn has more to do with Silver Lake, Los Angeles than any neighborhood in Albany, New York despite the 3000 miles in between. From this starting point we constructed a suite of metrics about neighborhoods encompassing everything from topological analysis of the built environment to, yes, the ratio of third-to-first wave coffee shops. In a sense, we’ve developed a ‘psychograpics of neighborhoods’, going beyond more familiar demographic viewpoints to capture the personality of a place, and what it feels like to be there. We have used these metrics to arrive at a new understanding (several, really) of how neighborhoods relate to one another.

From a machine learning (ML) point of view, having a strong definition of distance between entities — or, better yet, multiple definitions of distance — can be a powerful tool. Amongst other ML algorithms, mathematically defined distance enables the application of clustering algorithms to a set of entities, allowing them to be grouped together in different ways.