Publications

@article{campo005,

title = {A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics},

author = {Rebeca Diaz-Redondo and Carlos García-Rubio and Ana Fernandez-Vilas Celeste Campo-Vázquez and Alicia Rodriguez-Carrion},

url = {http://hdl.handle.net/10016/33771},

doi = {https://doi.org/10.1016/j.future.2020.03.038},

issn = {0167-739X},

year  = {2020},

date = {2020-08-10},

urldate = {2020-08-10},

journal = {Future generation computer systems},

volume = {109},

pages = {83-94},

abstract = {Undoubtedly, Location-based Social Networks (LBSNs) provide an interesting source of geo-located data that we have previously used to obtain patterns of the dynamics of crowds throughout urban areas. According to our previous results, activity in LBSNs reflects the real activity in the city. Therefore, unexpected behaviors in the social media activity are a trustful evidence of unexpected changes of the activity in the city. In this paper we introduce a hybrid solution to early detect these changes based on applying a combination of two approaches, the use of entropy analysis and clustering techniques, on the data gathered from LBSNs. In particular, we have performed our experiments over a data set collected from Instagram for seven months in New York City, obtaining promising results.},

keywords = {crowd dynamics, density-based clustering, emadrid, entropy analysis, instagram, location-based social network},

pubstate = {published},

tppubtype = {article}

}

@article{campo006,

title = {Using entropy of social media location data for the detection of crowd dynamics anomalies},

author = {Carlos García-Rubio and Rebeca Diaz-Redondo and Celeste Campo-Vázquez and Ana Fernandez-Vilas },

url = {http://hdl.handle.net/10016/33760},

doi = {https://doi.org/10.3390/electronics7120380},

issn = {2079-9292},

year  = {2018},

date = {2018-12-03},

urldate = {2018-12-03},

journal = {Electronics},

volume = {7},

issue = {12},

pages = {380},

abstract = {Evidence of something unusual happening in urban areas can be collected from different data sources, such as police officers, cameras, or specialized physical infrastructures. In this paper, we propose using geotagged posts on location-based social networks (LBSNs) to detect crowd dynamics anomalies automatically as evidence of a potential unusual event. To this end, we use the Instagram API media/search endpoint to collect the location of the pictures posted by Instagram users in a given area periodically. The collected locations are summarized by their centroid. The novelty of our work relies on using the entropy of the sequence of centroid locations in order to detect abnormal patterns in the city. The proposal is tested on a data set collected from Instagram during seven months in New York City and validated with another data set from Manchester. The results have also been compared with an alternative approach, a training phase plus a ranking of outliers. The main conclusion is that the entropy algorithm succeeds inn finding abnormal events without the need for a training phase, being able to dynamically adapt to changes in crowd behavior.},

keywords = {anomaly detection, city behavior, data mining algorithms, location-based social network},

pubstate = {published},

tppubtype = {article}

}