  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.
