# A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics

arXiv: https://arxiv.org/abs/2312.08092 · HTML: https://mlsanity.com/paper/2312.08092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 45 references that are in the map.
- First posted on arXiv 13 Dec 2023.

## Builds on (most cited first, 2 of 2)

- [Estimating Renyi Entropy of Discrete Distributions](https://mlsanity.com/paper/1408.1000/) (2014)
- [A Scalable Framework for Spatiotemporal Analysis of Location-based Social Media Data](https://mlsanity.com/paper/1409.2826/) (2014)

## Abstract (arXiv)

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.