Real-Time Social Event Analytics

Francesco Calabrese∗, Giusy Di Lorenzo∗, Gavin McArdle∗†, Fabio Pinelli∗, Erik Van Lierde‡∗IBM Research - Ireland, Dublin, Ireland

{fcalabre, giusydil, gavinm, fabiopin}@ie.ibm.com†National Centre for Geocomputation, Maynooth University, Maynooth, Co Kildare, Ireland

gavin.mcardle@nuim.ie‡Mobistar, Brussels, Belgium

erik.vanlierde@mail.mobistar.be

Managing public safety at large events is important. Crowdcontrol and traffic management are particularly relevant fornon-ticketed events in public spaces. In such cases, it canbe difficult for organisers to anticipate the number of peoplewho will attend and to validate the event’s success [1]. Giventhe ubiquitous nature of mobile phones, Call Detail Records(CDRs), which are the logs of user transactions with a mobilephone service provider, have been widely used to study urbanprocesses [2], [3], [4]. By building on the work of [5] and[6] our research explores the use of real-time CDR dataas a proxy to estimate the density of crowds in differentareas of a city while events are taking place. The researchhas also been extended to estimate the density of vehicleson the main access routes to a city. This has led to thedevelopment of an application entitled Social Event Analytics(SEA) which provides both real-time and historic informationabout crowd and vehicle densities. The application can beused by authorities and event organisers to manage the eventand gauge its success. The application was used in January2015 for monitoring city wide events in Mons, Belgium whichmarked the launch of Mons as the European City of Culturefor 2015. Using SEA local police simultaneously monitoredthe density of vehicles on the road network and the crowddensity in different areas of the city. Below, we briefly describethe new real-time data analysis which we carried out for thisspecific case in which over 20 million CDRs were analysedeach day. The results are useful for authorities but will alsohelp to further our knowledge of human processes in urbanenvironments.

We use data from a Belgium telecommunication operatorwho provided details about the distribution and azimuth oftheir cell towers in the city of Mons. Using this informationthe city is divided into cells using a Voronoi tessellation [7].In total, 319 distinct cells were created (figure 1). Theseformed the basic unit on which crowd and vehicle densityare analysed. Prior to the opening ceremony, the police andorganisers provided details of specific public squares in the citywhere events were taking place along with a list of importantaccess routes to the city and our analysis focuses on thesespatial features.

Anonymous CDR data for individuals connected to celltowers in Mons and the surrounding area are supplied directlyby the telecommunication operator. Each row of CDR dataconsists of an anonymous user ID, a time-stamp, the ID ofthe cell tower, the home country of the device and the typeof record (call, SMS, data). Typically, data connections are an

Fig. 1. 319 Voronoi cells were computed from the cell tower distributionand azimuth data provided by the telecommunication operator in Mons.

always on service and lead to generation of comprehensiveCDRs. In this case the data are recorded at a rate of morethan 250 records every second. By analysing the cell IDs,it is possible to determine the number of users connected toeach cell tower and determine the Voronoi cell they are in.By combining this data with the known area of each Voronoicell and the known customer penetration rate of the specificmobile operator, the density of crowds in any given Voronoicell is estimated. For the public squares which are of particularinterest to the police, we also produce a count of the number ofindividuals in these spaces. Furthermore, an estimation of thenumber of people in the whole city is calculated by summingthe amount people connected to the relevant cell towers.

While the police are interested in public safety and crowdcontrol, organisers are also interested in assessing the successof the event and the marketing campaigns used to attractpeople. In addition to the numbers attending the event, theorganisers also wanted to know where people were travellingfrom in order to attend the events in the city. Therefore, thenumber of users by nationality are calculated using the homecountry of the device which is available directly from eachCDR. In order to obtain the home city of Belgian users, further

Fig. 3. A screen capture from the SEA application showing the estimated crowd density for the Voronoi cells in Mons. Additional bar charts show the estimatedvolume of people at several locations in the city and the home country of visitors.

Fig. 2. 86 road segments were computed based on their intersection withthe Voronoi cells.

analysis of the complete CDR data for Belgium was carriedout off line. Several weeks of CDR data were analysed in orderto determine the city where each device generally connected

to network during the night time hours between Midnightand 6 AM. This is similar to approaches for calculating thesignificant places people visit [8], [9].

In addition to crowd estimation, the density of vehiclestravelling on the main access roads to the city is estimatedusing a minimal computational approach. Each road of interestis segmented so each segment is contained within a singleVoronoi cell. This produces 86 road segments (figure 2). Whilethe density of crowds in these cells can be estimated usingthe techniques described above, we are interested in thosetravelling through the cell. Historic CDR data was analysed todetermine users who regularly spend time in that cell. Thesetypically represent people who live or work in the area. Theseusers are removed from the analysis of the density for that cellwhich allows us to estimate the density of the major roads inthe cell. When two or more major roads are covered by a singleVoronoi cell, it is necessary to estimate the density of theseroads by averaging the known densities of the road segmentson either side of the particular cell.

The analysis above was used to produce an application,called SEA, which monitors vehicle and crowd density innear real-time. The data is supplied by the telecommunicationoperator every 15 minutes and contains the CDR data forthe previous 15 minutes. Based on the analysis, dynamicvisualisations are produced to present the results to the policeand organisers. The main visualisation, as seen in figure 1

Fig. 4. A screen capture from the SEA application showing the estimated traffic density on several access routes for Mons.

consists of a map with the Voronoi cell structure superimposed.The cells are coloured based on the crowd density. The Voronoicells containing the squares where events are taking place arefurther highlighted. A bar chart shows the count of the crowdfor each of these squares. An additional bar chart shows thehome country of international visitors along with an estimateof the total number of people in the city. A separate interactivemap shown in figure 4 presents the roads of interest. Eachroad segment is coloured based on the estimated traffic density.Roads which share cells are coloured with 50% transparencyto signify an average of the surrounding road segments wasused to calculate its density.

The real-time application was used by the police andorganisers to monitor the events during the opening ceremonyof Mons as the European City of Culture 2015. Preliminaryfeedback has been positive and we are awaiting the results ofa more detailed evaluation. We will also use the CDR data todetermine the amount of time individuals spend at events andto understand the fine grained mobility occurring in the city.These will serve as indicators regarding the success of eventswhile also furthering our understanding of urban processes.

ACKNOWLEDGMENTS

This publication has emanated from research conductedwith the financial support of Science Foundation Ireland underGrant ”SFI 13/IF/I2783”. The authors would like to thankMobistar for providing access to the anonymized data.

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