With as many as 4 million passenger journeys within the London Underground system every weekday, the advertisement spaces across the stations hold considerable potential. However, the planning of specific advertisements across time and space is difficult to optimize as little is known about passers-by. Therefore, in order to generate detailed and quantifiable spatio-temporal information which is particular to each station area, we have explored local social media data. This research demonstrates how local interests can be mined from geotagged Tweets by using Latent Dirichlet Allocation, an unsupervised topic modelling method. The relative popularity of each of the key topics is then explored spatially and temporally between the station areas. Overall, this research demonstrates the value of using Geographical Information System and text-mining techniques to generate valuable spatio-temporal information on popular interests from Twitter data.

Place is a concept that is fundamental to how we orientate and communicate space in our everyday lives. Crowd sourced social media data present a valuable opportunity to develop bottom-up inferences of places that are integral to social activities and settings. Conventional location-led approaches use a pre-defined spatial unit to associate data and space with places, which cannot capture the richness of urban places, i.e. spatial extents and their dynamic functions. This paper develops a name-led framework to overcome these limitations in using social media data to study urban places. The framework first derives place names from georeferenced Twitter data combining text mining and spatial point pattern analysis, then estimates the spatial extents by spatial clustering, and further extracts their dynamic functions with time, which makes up a complete place profile. The framework is tested on a case study in Camden borough of London and the results are evaluated through comparisons to the Foursquare Point of Interest (POI) data. This name-lead approach enables the shift from space-based analysis to place-based analysis of urban space.

No abstract