Using Entropy of Social Media Location Data for the Detection of Crowd Dynamics Anomalies

Garcia-Rubio, Carlos; Redondo, Rebeca P. Díaz; Campo, Celeste; Vilas, Ana Fernández

doi:10.3390/electronics7120380

Cited by 12 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our recent work, 32 we use entropy as such fast indicator. To apply 2PD-CC to the general case of unexpected events, first, we need to obtain the data representing the distribution of the people (according to their posts) all over the considered city, ie, the regular pattern in terms of centroids and collectives.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…In order to deal with these two issues, we need to summarize the data through some indicator, which would be fast to retrieve as well as expressive enough so as to reflect anomalies. In our recent work, we use entropy as such fast indicator. Once the potential anomaly has been identified by an entropy change, 2PD‐CC can be applied over a smaller area and, consequently, over a denser set of posts retrieved through the streaming API in that area.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

“…Furthermore, taking into account the cost of obtaining a regular pattern and monitoring the entire city, we are working in integrating an entropy-based detection prestep, which will allow to apply the methodology in a more localized ways according to our first results. 32…”

Section: Conclusion and Further Workmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of crowds' movement using Twitter

Vilas

Redondo

Khalifa

2019

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

Over the last decade, the infrastructure supporting the smart city has lived together with and was surpassed by the rise of social media. The tremendous growth of both mobile devices and social media users has unearthed a new kind of services in the so-called location-based social networks (LBSNs). In this new scenario, the term crowdsensing refers to sharing data collected by sensing humans with the aim of measuring phenomena of common interest. Crowd-sourced location data provide the ability to study, for the first time, the movement of individuals in urban environments. In this paper, we address the problem of monitoring crowds, whereabouts and movement, which can assist decision making in education, emergency training, urban planning, traffic engineering, etc. Precisely, two-phase density-based analysis for collectives and crowds (2PD-CC) is a novel methodology over public data in LBSN, which combines density-based clustering, outlier detection a topic modeling over a region under study to detect, predict, and explain abnormal group behavior. In order to validate the methodology and its potential application to full-scale problems, an experiment over Twitter data was performed in Madrid city.

show abstract

Section: Discussion and Limitationsmentioning

confidence: 99%

Section: Discussion and Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of crowds' movement using Twitter

Vilas

Redondo

Khalifa

2019

Computational Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, [44] introduced the concept of entropy and its practical interpretation. The proposed approach exploits the entropy behavior to minimize both drawbacks: the "anomalous data increase the entropy values, so no previous patterns are needed", and the "entropy levels are continuously adapted as long as new geolocated data are extracted from social media".…”

Section: Discussionmentioning

confidence: 99%

Urban Crowd Detection Using SOM, DBSCAN and LBSN Data Entropy: A Twitter Experiment in New York and Madrid

2019

View full text Add to dashboard Cite

The surfer and the physical location are two important concepts associated with each other in the social network-based localization service. This work consists of studying urban behavior based on location-based social networks (LBSN) data; we focus especially on the detection of abnormal events. The proposed crowd detection system uses the geolocated social network provided by the Twitterapplication programming interface (API) to automatically detect the abnormal events. The methodology we propose consists of using an unsupervised competitive learning algorithm (self-organizing map (SOM)) and a density-based clustering method (density-based spatial clustering of applications with noise (DBCSAN)) to identify and detect crowds. The second stage is to build the entropy model to determine whether the detected crowds fit into the daily pattern with reference to a spatio-temporal entropy model, or whether they should be considered as evidence that something unusual occurs in the city because of their number, size, location and time of day. To detect an abnormal event in the city, it is sufficient to determine the real entropy model and to compare it with the reference model. For the normal day, the reference model is constructed offline for each time interval. The obtained results confirm the effectiveness of our method used in the first stage (SOM and DBSCAN stage) to detect and identify clusters dynamically, and imitating human activity. These findings also clearly confirm the detection of special days in New York City (NYC), which proves the performance of our proposed model.

show abstract

“…It provides a set of domain ontologies to formalize comfort-related metrics and to exploit the automatic reasoning capabilities provided by Semantic Web technologies while gathering data through a network of sensors to ensure guests are provided with tailored comfort profiles during their stays in the hotel [15]. Automatic detection of unusual crowd dynamics using geotagged posts on location-based social networks (LBSNs) is proposed in [16]. Authors use Instagram API media/search endpoint to collect the location of the pictures posted by Instagram users in a given area periodically.…”

Section: The Present Issuementioning

confidence: 99%