text: Pok 'emon Go has received unprecedented media coverage for a location-based game that uses augmented reality techniques. The game has been commonly associated with greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pok 'emon Go on people's mobility patterns in a city is still largely unknown. In this paper we perform a natural experiment using data from mobile networks to evaluate the effect of Pok 'emon Go on the pulse of a big city: Santiago of Chile. We found a significant effect of Pok 'emon Go on the floating population of Santiago: up to 13.8 more people being outside at certain times, even if they do not seem to go out of their usual way. These effects at specific times were found by performing several regressions using count models over snapshots of the cell phone network. The effect is significant after controlling for land use, daily patterns, and points of interest in the city. Particularly, we found that, in business days, there is more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play to places nearby their homes. Even if the statistical effects of the game do not reflect the massive reach portrayed by the media, it still allowed the streets to become a new place for people to spend time in. This is important, because results like these are expected to inform long-term infrastructure investments by city officials, jointly with public policies aimed at, for example, stimulating pedestrian traffic or suggesting alternative routes. Our work supports the notion that location-based games like Pok 'emon Go have benefits for the life in the city.
  @cite_35: Several pervasive and mobile computing systems would benefit from being notified about special events happening in the city. In this paper we present a methodology to discover events from human mobility patterns as recorded by cell network usage. Experiments conducted over an extensive dataset from the main Italian telecom operator show that the proposed approach is effective and can be applied to a number of different scenarios. These results can have a strong impact on pervasive applications including online map services, automatic gazetteers, urban planning, and disaster response.
  @cite_28: This paper deals with the analysis of crowd mobility during special events. We analyze nearly 1 million cell-phone traces and associate their destinations with social events. We show that the origins of people attending an event are strongly correlated to the type of event, with implications in city management, since the knowledge of additive flows can be a critical information on which to take decisions about events management and congestion mitigation.
  @cite_52: The recent development of telecommunication networks is producing an unprecedented wealth of information and, as a consequence, an increasing interest in analyzing such data both from telecoms and from other stakeholders' points of view. In particular, mobile phone datasets offer access to insights into urban dynamics and human activities at an unprecedented scale and level of detail, representing a huge opportunity for research and real-world applications. This article surveys the new ideas and techniques related to the use of telecommunication data for urban sensing. We outline the data that can be collected from telecommunication networks as well as their strengths and weaknesses with a particular focus on urban sensing. We survey existing filtering and processing techniques to extract insights from this data and summarize them to provide recommendations on which datasets and techniques to use for specific urban sensing applications. Finally, we discuss a number of challenges and open research areas currently being faced in this field. We strongly believe the material and recommendations presented here will become increasingly important as mobile phone network datasets are becoming more accessible to the research community.
  @cite_44: 
  @cite_15: Several attempts have already been made to use telecommunications networks for urban research, but the datasets employed have typically been neither dynamic nor fine grained. Against this research backdrop the mobile phone network offers a compelling compromise between these extremes: it is both highly mobile and yet still localisable in space. Moreover, the mobile phone’s enormous and enthusiastic adoption across most socioeconomic strata makes it a uniquely useful tool for conducting large-scale, representative behavioural research. In this paper we attempt to connect telecoms usage data from Telecom Italia Mobile (TIM) to a geography of human activity derived from data on commercial premises advertised through Pagine Gialle, the Italian ‘Yellow Pages’. We then employ eigendecomposition—a process similar to factoring but suitable for this complex dataset—to identify and extract recurring patterns of mobile phone usage. The resulting eigenplaces support the computational and comparative analysis of space through the lens of telecommuniations usage and enhance our understanding of the city as a ‘space of flows’.
  @cite_34: 
summary: According to a recent survey of urban sensing research @cite_52 , local event analysis is a key area of mobile phone data analysis. Local events are usually defined as unusual gatherings or movements of massive amounts of people ( , protests, emergencies, sports, natural events, etc.) @cite_28 @cite_35 @cite_34 @cite_44 . Hence, the unit of analysis is a single event with time and space constraints. Our method, instead, works at the city level with less prescribed time and location from those above. One thing in common between our work and the cited references is that all analysis have been performed ex-post, which makes it possible to create spatio-temporal signatures of places @cite_15 . Even though these approaches allow us to analyze and understand the city, they do not allow measurement of city-scale phenomena due to their assumptions of locality.