RT Journal Article
T1 A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics
A1 Díaz Redondo, Rebeca Pilar
A1 García Rubio, Carlos
A1 Fernández Vilas, Ana
A1 Campo Vázquez, María Celeste
A1 Rodríguez Carrión, Alicia
K1 1203.99 Otras
K1 1209.03 Análisis de Datos
AB 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.
PB Future Generation Computer Systems
SN 0167739X
YR 2020
FD 2020-08
LK http://hdl.handle.net/11093/3894
UL http://hdl.handle.net/11093/3894
LA eng
NO Future Generation Computer Systems, 109, 83-94 (2020)
NO Ministerio de Economía y Competitividad | Ref. TEC2014-54335-C4-2-R
DS Investigo
RD 11-dic-2024