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Deriving retail centre locations and catchments from geo-tagged Twitter data
AbstractThis 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.
HighlightsNovel methods to establish the value of social media data for deriving retail locations and catchment areas.Geo-tagged interactions with retailers are good indicator of locations with elevated levels of retailing activity.Retail centre Tweet catchments highlight the importance of transport networks on flows between locations.The first demonstration of the value of social media to the geography of retail.
Deriving retail centre locations and catchments from geo-tagged Twitter data
AbstractThis 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.
HighlightsNovel methods to establish the value of social media data for deriving retail locations and catchment areas.Geo-tagged interactions with retailers are good indicator of locations with elevated levels of retailing activity.Retail centre Tweet catchments highlight the importance of transport networks on flows between locations.The first demonstration of the value of social media to the geography of retail.
Deriving retail centre locations and catchments from geo-tagged Twitter data
Lloyd, Alyson (author) / Cheshire, James (author)
Computers, Environments and Urban Systems ; 61 ; 108-118
2016-09-28
11 pages
Article (Journal)
Electronic Resource
English
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