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Understanding the impact that a new business has on the local marketecosystem is a challenging task as it is multifaceted in nature. Past work inthis space has examined the collaborative or competitive role of homogeneousvenue types (i.e. the impact of a new bookstore on existing bookstores).However, these prior works have been limited in their scope and explanatorypower. To better measure retail performance in a modern city, a model shouldconsider a number of factors that interact synchronously. This paper is thefirst which considers the multifaceted types of interactions that occur inurban cities when examining the impact of new businesses. We first present amodeling framework which examines the role of new businesses in theirrespective local areas. Using a longitudinal dataset from location technologyplatform Foursquare, we model new venue impact across 26 major citiesworldwide. Representing cities as connected networks of venues, we quantifytheir structure and characterise their dynamics over time. We note a strongcommunity structure emerging in these retail networks, an observation thathighlights the interplay of cooperative and competitive forces that emerge inlocal ecosystems of retail establishments. We next devise a data-driven metricthat captures the first-order correlation on the impact of a new venue onretailers within its vicinity accounting for both homogeneous and heterogeneousinteractions between venue types. Lastly, we build a supervised machinelearning model to predict the impact of a given new venue on its local retailecosystem. Our approach highlights the power of complex network measures inbuilding machine learning prediction models. These models have numerousapplications within the retail sector and can support policymakers, businessowners, and urban planners in the development of models to characterize andpredict changes in urban settings.

Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics