Discovery of spatio-temporal patterns from location-based social networks. Issue 1 (3rd March 2016)

• Record Type: Journal Article
• Authors: Béjar, J.; Álvarez, S.; García, D.; Gómez, I.; Oliva, L.; Tejeda, A.; Vázquez-Salceda, J.
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 28:Issue 1/2(2016)
• Page Start: 313
• Page End: 329
• Publication Date: 2016-03-03
• Subjects: spatio-temporal data -- user profiling -- data mining -- frequent itemsets -- clustering -- location-based social networks; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1024492
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract : Location-based social networks (LBSNs) such as Twitter or Instagram are a good source for user spatio-temporal behaviour. These networks collect data from users in such a way that they can be seen as a set of collective and distributed sensors of a geographical area. A low rate sampling of user's location information can be obtained during large intervals of time that can be used to discover complex patterns, including mobility profiles, points of interest or unusual events. These patterns can be used as the elements of a knowledge base for different applications in different domains such as mobility route planning, touristic recommendation systems or city planning. The aim of this paper is twofold, first to analyse the frequent spatio-temporal patterns that users share when living and visiting a city. This behaviour is studied by means of frequent itemsets algorithms in order to establish some associations among visits that can be interpreted as interesting routes or spatio-temporal connections. Second, to analyse how the spatio-temporal behaviour of a large number of users can be segmented in different profiles. These behavioural profiles are obtained by means of clustering algorithms that show the different patterns of behaviour of visitors and citizens. The data analysed were obtained from the public data feeds of Twitter and Instagram within an area surrounding the cities of Barcelona and Milan for a period of several months. The analysis of these data shows that these kinds of algorithms can be successfully applied to data from any city (or general area) to discover useful patterns that can be interpreted on terms of singular places and areas and their temporal relationships.