Abstract
Abstract This article advances a new approach using hierarchical cluster analysis (HCA) for identifying and delineating spatial agglomerations and applies it to venture-backed startups. HCA identifies nested clusters at varying aggregation levels. We describe two methods for selecting a particular aggregation level and the associated agglomerations. The ‘elbow method’ relies entirely on geographic information. Our preferred method, the ‘regression method’, uses geographic information and venture capital investment data and identifies finer agglomerations, often the size of a small neighborhood. We use heat maps to illustrate how agglomerations evolve and we describe how our methods can help assess agglomeration support policies.
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