Deriving retail centre locations and catchments from geo-tagged Twitter data

This investigation offers an initial foray into the application of geo-tagged Twitter data for generating insights within two areas of retail geography: establishing retail centre locations and defining catchment areas. Retail related Tweets were identified and their spatial attributes examined with an adaptive kernel density estimation, revealing that retail related Twitter content can successfully locate areas of elevated retail activity, however, these are constrained by biases within the data. Methods must also account for the underlying geographic distribution of Tweets to detect these fluctuations. Additionally, geo-tagged Twitter data can be utilised to examine human mobility patterns in a retail centre context. The catchments constructed from the data highlight the importance of accessibility on flows between locations, which have implications for the likely commuting choices that may be involved in retail centre journey decision-making. These approaches demonstrate the potential applications for less conventional datasets, such as those derived from social media data, to previously under researched areas. (C) 2016 The Authors. Published by Elsevier Ltd.