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.
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.
@cite_35 @cite_28 @cite_52 @cite_44 @cite_15 @cite_34
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Abstract Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method. Methodology approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area. Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility. Originality value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring. Detecting local events (e.g., protest, disaster) at their onsets is an important task for a wide spectrum of applications, ranging from disaster control to crime monitoring and place recommendation. Recent years have witnessed growing interest in leveraging geo-tagged tweet streams for online local event detection. Nevertheless, the accuracies of existing methods still remain unsatisfactory for building reliable local event detection systems. We propose TrioVecEvent, a method that leverages multimodal embeddings to achieve accurate online local event detection. The effectiveness of TrioVecEvent is underpinned by its two-step detection scheme. First, it ensures a high coverage of the underlying local events by dividing the tweets in the query window into coherent geo-topic clusters. To generate quality geo-topic clusters, we capture short-text semantics by learning multimodal embeddings of the location, time, and text, and then perform online clustering with a novel Bayesian mixture model. Second, TrioVecEvent considers the geo-topic clusters as candidate events and extracts a set of features for classifying the candidates. Leveraging the multimodal embeddings as background knowledge, we introduce discriminative features that can well characterize local events, which enables pinpointing true local events from the candidate pool with a small amount of training data. We have used crowdsourcing to evaluate TrioVecEvent, and found that it improves the performance of the state-of-the-art method by a large margin. The unprecedented amount of data from mobile phones creates new possibilities to analyze various aspects of human behavior. Over the last few years, much effort has been devoted to studying the mobility patterns of humans. In this paper we will focus on unusually large gatherings of people, i.e. unusual social events. We introduce the methodology of detecting such social events in massive mobile phone data, based on a Bayesian location inference framework. More specifically, we also develop a framework for deciding who is attending an event. We demonstrate the method on a few examples. Finally, we discuss some possible future approaches for event detection, and some possible analyses of the detected social events. Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data. This study proposes a street-level space‐time hotspot detection method to analyse crime incidents recorded at the street-address level and provides description of the micro-level variation of crime incidents over space and time. It expands the notion of search-window techniques widely used in crime science by developing a method that can account for the spatial‐temporal distribution of crime incidents measured in network distance. The study first describes the methodological framework by presenting the concept of a new type of search window and how it is used in the process of statistical testing for detecting crime hotspots. This is followed by analyses using 1 a simulated distribution of points along the street network, and 2 a set of real street-crime incident data. The simulation study demonstrates that the proposed method is effective in identifying space‐time hotspots, which include those that are not detected by a non-temporal method. The empirical analysis of the drug markets and assaults in downtown Buffalo, New York, revealed a detailed space‐time signature of each type of crime, highlighting the recurrent nature of drug dealing at specific locations as well as the sporadic tendency of assault incidents. Spatial event detection is an important and challenging problem. Unlike traditional event detection that focuses on the timing of global urgent event, the task of spatial event detection is to detect the spatial regions (e.g. clusters of neighboring cities) where urgent events occur. In this paper, we focus on the problem of spatial event detection using textual information in social media. We observe that, when a spatial event occurs, the topics relevant to the event are often discussed more coherently in cities near the event location than those far away. In order to capture this pattern, we propose a new method called Graph Topic Scan Statistic (Graph-TSS) that corresponds to a generalized log-likelihood ratio test based on topic modeling. We first demonstrate that the detection of spatial event regions under Graph-TSS is NP-hard due to a reduction from classical node-weighted prize-collecting Steiner tree problem (NW-PCST). We then design an efficient algorithm that approximately maximizes the graph topic scan statistic over spatial regions of arbitrary form. As a case study, we consider three applications using Twitter data, including Argentina civil unrest event detection, Chile earthquake detection, and United States influenza disease outbreak detection. Empirical evidence demonstrates that the proposed Graph-TSS performs superior over state-of-the-art methods on both running time and accuracy. The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate realtime traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this talk, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges. We describe a platform for smart, city-wide crowd management based on participatory mobile phone sensing and location situation specific information delivery. The platform supports quick and flexible deployments of end-to-end applications for specific events or spaces that include four key functionalities: (1) Mobile phone based delivery of event space specific information to the users, (2) participatory sensor data collection (from app users) and flexible analysis, (3) location and situation specific message multicast instructing people in different areas to act differently in case of an emergency and (4) post mortem event analysis. This paper describes the requirements that were derived through a series of test deployments, the system architecture, the implementation and the experiences made during real life, large scale deployments. Thus, until today it has been deployed at 14 events in three European countries (UK, Netherlands, Switzerland) and was used by well over 100,000 people. This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London, and Hong Kong. By comparing whole cities’ typical patterns, we provide insights on how cultural, technological, and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity. We propose a new type of crowd analysis, focused on the spectator crowd, that is, people "interested in watching something specific that they came to see" [1]. This scenario applies on stadiums, amphitheaters etc., and shares some aspects with classical crowd monitoring: actually, many people are simultaneously observed, so that per-person analysis is hard; however, here the dynamics of humans is more constrained, due to the architectural environment in which they are situated; specifically, people are expected to stay in a fixed location most of the time, limiting their activities to applaud, support heckle the players or discuss with the neighbors. In this paper, we start facing this challenge by considering hockey matches, locating a videocamera 25-30 meters far from the bleachers, pointing at the crowd: in this scenario, aggregations of spectators that exhibit similar behavior are detected, and the behavior is classified into a set of predefined classes, highlighting the overall excitement. To these aims, in a first step we focus on individual frames, clustering local flow measures into spatial regions. The clustering is then extended by adding the temporal axis into the analysis, looking for non-randomic spatio-temporal clusters; for this purpose, the Lempel-Ziv complexity is considered. This way, choral activities can emerge, indicating for example fan groups belonging to different teams. After this, with the adoption of entropic measures, the degree of excitement of such groups can be quantified. We propose a visual analytics procedure for analyzing movement data, i.e., recorded tracks of moving objects. It is oriented to a class of problems where it is required to determine significant places on the basis of certain types of events occurring repeatedly in movement data. The procedure consists of four major steps: (1) event extraction from trajectories; (2) event clustering and extraction of relevant places; (3) spatio-temporal aggregation of events or trajectories; (4) analysis of the aggregated data. All steps are scalable with respect to the amount of the data under analysis. We demonstrate the use of the procedure by example of two real-world problems requiring analysis at different spatial scales. The future-related information mining task for online web resources such as news articles and blogs has been getting more attention due to its potential usefulness in supporting individual's decision making in a world where massive new data are generated daily. Instead of building a data-driven model to predict the future, one extracts future events from these massive data with high probability that they occur at a future time and a specific geographic location. Such spatiotemporal future events can be utilized by a recommender system on a location-aware device to provide localized future event suggestions. In this paper, we describe a systematic approach for mining future spatiotemporal events from web; in particular, news articles. In our application context, a valid event is defined both spatially and temporally. The mining procedure consists of two main steps: recognition and matching. For the recognition step, we identify and resolve toponyms (geographic location) and future temporal patterns. In the matching step, we perform spatiotemporal disambiguation, de-duplication, and pairing. To provide more useful future event guidance, we attach to each event a sentiment linguistic variable: positive, negative, or neutral, so that one may use these extracted event information for recommendation purposes in the form of "avoid Event A" or "avoid geographic location L at time T" or "attend Event B" based on the event sentiment. The identified future event consists of its geographic location, temporal pattern, sentiment variable, news title, key phrase, and news article URL. Experimental results on 3652 news articles from 21 online new sources collected over a 2-week period in the Greater Washington area are used to illustrate some of the critical steps in our mining procedure. Can we automatically identify relevant places and events happening in the city from the analysis of mobile network use? In this paper we present a methodology to discover events from human mobility patterns as recorded by mobile 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 a wide range of pervasive applications ranging from location-based services to urban planning. 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. In this paper, we model multi-agent events in terms of a temporally varying sequence of sub-events, and propose a novel approach for learning, detecting and representing events in videos. The proposed approach has three main steps. First, in order to learn the event structure from training videos, we automatically encode the sub-event dependency graph, which is the learnt event model that depicts the conditional dependency between sub-events. Second, we pose the problem of event detection in novel videos as clustering the maximally correlated sub-events using normalized cuts. The principal assumption made in this work is that the events are composed of a highly correlated chain of sub-events that have high weights (association) within the cluster and relatively low weights (disassociation) between the clusters. The event detection does not require prior knowledge of the number of agents involved in an event and does not make any assumptions about the length of an event. Third, we recognize the fact that any abstract event model should extend to representations related to human understanding of events. Therefore, we propose an extension of CASE representation of natural languages that allows a plausible means of interface between users and the computer. We show results of learning, detection, and representation of events for videos in the meeting, surveillance, and railroad monitoring domains. Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations. We propose a novel approach to understanding activities from their partial observations monitored through multiple non-overlapping cameras separated by unknown time gaps. In our approach, each camera view is first decomposed automatically into regions based on the correlation of object dynamics across different spatial locations in all camera views. A new Cross Canonical Correlation Analysis (xCCA) is then formulated to discover and quantify the time delayed correlations of regional activities observed within and across multiple camera views in a single common reference space. We show that learning the time delayed activity correlations offers important contextual information for (i) spatial and temporal topology inference of a camera network; (ii) robust person re-identification and (iii) global activity interpretation and video temporal segmentation. Crucially, in contrast to conventional methods, our approach does not rely on either intra-camera or inter-camera object tracking; it thus can be applied to low-quality surveillance videos featured with severe inter-object occlusions. The effectiveness and robustness of our approach are demonstrated through experiments on 330 hours of videos captured from 17 cameras installed at two busy underground stations with complex and diverse scenes. Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems. Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, e.g., a "crime" event can cause a "investigation" event, which can lead to an "arrest" event. However, most current approaches address event extraction with highly local models that extract each event and argument independently. We propose a simple approach for the extraction of such structures by taking the tree of event-argument relations and using it directly as the representation in a reranking dependency parser. This provides a simple framework that captures global properties of both nested and flat event structures. We explore a rich feature space that models both the events to be parsed and context from the original supporting text. Our approach obtains competitive results in the extraction of biomedical events from the BioNLP'09 shared task with a F1 score of 53.5 in development and 48.6 in testing. Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events. To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.