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

TL;DR

This paper introduces a complex network-based modeling framework to analyze and predict how new businesses influence urban retail ecosystems, considering multifaceted interactions across diverse city venues.

Contribution

It presents a novel approach combining complex network metrics and machine learning to assess and forecast the impact of new venues in urban retail environments.

Findings

Strong community structures in retail networks.

A new metric capturing venue impact correlations.

Effective machine learning predictions of venue impact.

Abstract

Understanding the impact that a new business has on the local market ecosystem is a challenging task as it is multifaceted in nature. Past work in this space has examined the collaborative or competitive role of homogeneous venue types (i.e. the impact of a new bookstore on existing bookstores). However, these prior works have been limited in their scope and explanatory power. To better measure retail performance in a modern city, a model should consider a number of factors that interact synchronously. This paper is the first which considers the multifaceted types of interactions that occur in urban cities when examining the impact of new businesses. We first present a modeling framework which examines the role of new businesses in their respective local areas. Using a longitudinal dataset from location technology platform Foursquare, we model new venue impact across 26 major cities worldwide. Representing cities as connected networks of venues, we quantify their structure and characterise their dynamics over time. We note a strong community structure emerging in these retail networks, an observation that highlights the interplay of cooperative and competitive forces that emerge in local ecosystems of retail establishments. We next devise a data-driven metric that captures the first-order correlation on the impact of a new venue on retailers within its vicinity accounting for both homogeneous and heterogeneous interactions between venue types. Lastly, we build a supervised machine learning model to predict the impact of a given new venue on its local retail ecosystem. Our approach highlights the power of complex network measures in building machine learning prediction models. These models have numerous applications within the retail sector and can support policymakers, business owners, and urban planners in the development of models to characterize and predict changes in urban settings.