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

Article history: Received 13 January 2016 Received in revised form 20 August 2016 Accepted 28 September 2016 Available online 20 October 2016 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. Methodsmust 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 underresearched areas. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).