Identifying urban crowds using geo-located Social media data: a Twitter experiment in New York City

Abstract

The massive growth of GPS equipped smartphones coupled with the increasing importance of Social Media has led to the emergence of new services over LBSNs (Location-based Social Networks) where both, opinions and location, are shared. This proactive attitude allow us to consider citizens as sensors in motion whose information supports our approach: monitoring multitudes or crowds all around the city. More specifically, our proposal is mining geotagged data from LBSNs in order to analyze crowds according to different parameters as size, duration, composition, motivation, cohesion and proximity. This analysis is gathered under a methodology for crowd detection in cities that combines social data mining, density-based clustering and outlier detection into a solution that can operate on-the-fly.

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

“…With the advent of smartphones, it is very interesting to propose a crowd detection system using the geolocated social network. The idea of density-based clustering for urban crowd detection has recently been applied to social network analysis [21,23,[41][42][43]. Geo-tagged tweets allow one to detect real-world events from social network data.…”

“…Among the most relevant spatial analyses carried out on Foursquare data, we also mention Kelley's research [20]. Ben Khalifa [21] suggested an analysis of geolocated social media data to identify urban crowds in New York City. "This analysis is gathered under a methodology for crowd detection in cities that combines social data mining, density-based clustering and outlier detection into a solution that can operate on-the-fly" According to Gao [22], the availability of big geo-social data on LBSNs provides an unheated opportunity to "study human mobile behavior through data analysis in a spatial-temporal-social context, enabling a variety of LBS, from mobile marketing to disaster relief".…”

“…Several works have followed the same procedure. In [21], the authors used the Streaming API, to obtained four datasets: one for the special day (from 15:00 of 31 December 2013 to 15:00 of 1 January 2014) and another for a normal day (from 15:00 of 24 February 2014 to 15:00 25 February 2014) in both cities (NYC and Madrid). In [23], the authors used a data set including both normal days and special days, due to festivities like Christmas or natural phenomena like the weekend when Storm Jonas hit the United States, which can be used to test the outlier detection because these dates are supposed to have an uncommon behaviour (higher densities in Christmas, lower during the Storm).…”

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

“…These data are stored and arranged in the database based on keywords (e.g., with Hashtags). These data have been applied to identify crowd events [76], [77], [78], [79], [91], transportation planning and management [21], and crowd sentiment analysis [21], [12], [23].…”

“…With the increased sharing of location and opinion of citizens on the social network, the Location-based Social Networks (LBSN) has gained interest by the urban planners to plan upcoming events. Khalifa et al combined density-based clustering, social data mining, and outlier detection to detect crowds in cities at realtime [78]. This method has been validated by Twitter data of New York City on a reference (e.g., any) day and on study day (e.g., New Years Eve) when crowd events are expected.…”