Geo-located Twitter as the proxy for global mobility patterns

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.Comment: 17 pages, 13 figure

Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization

Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80\% of public tweets.Comment: 9 pages, 8 figures, accepted to IEEE BigData 2014, Compton, Ryan, David Jurgens, and David Allen. "Geotagging one hundred million twitter accounts with total variation minimization." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 201

Nuevas fuentes de información geográfica en turismo: las oportunidades de sightsmap.com

La producción de información geográfica ha cobrado un ritmo antes insospechado, gracias a instituciones y empresas, pero especialmente a las acciones voluntarias (Web 2.0). Uno de los campos en los que se abren mayores posibilidades para el uso de la nueva información geolocalizada es el turismo, ya que sus actividades son fácilmente monitorizables. Una de las fuentes más conocidas, Panoramio exhibe fotografías de lugares tomadas y georreferenciadas por usuarios. Esta comunicación presenta las oportunidades que tienen, para el análisis del turismo, la información de Panoramio y la explotación que hace de ella la herramienta Sightsmaps, a diferentes escalas

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactions

Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running efficient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavior. Since a city size is known to have a major, yet often nonlinear, impact on the human activity, it also becomes important to develop scale-free metrics that capture qualitative city properties, beyond the effects of scale. Recent availability of extensive datasets created by human activity involving digital technologies creates new opportunities in this area. In this paper we propose a novel approach of city scoring and classification based on quantitative scale-free metrics related to economic activity of city residents, as well as domestic and foreign visitors. It is demonstrated on the example of Spain, but the proposed methodology is of a general character. We employ a new source of large-scale ubiquitous data, which consists of anonymized countrywide records of bank card transactions collected by one of the largest Spanish banks. Different aspects of the classification reveal important properties of Spanish cities, which significantly complement the pattern that might be discovered with the official socioeconomic statistics.Comment: 10 pages, 7 figures, to be published in the proceedings of ASE BigDataScience 2014 conferenc

Quantifying, Comparing Human Mobility Perturbation during Hurricane Sandy, Typhoon Wipha, Typhoon Haiyan

AbstractClimate change has intensified tropical cyclones, resulting in several recent catastrophic hurricanes and typhoons. Such disasters impose threats on populous coastal urban areas, and therefore, understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Despite its critical roles, limited research has focused on tropical cyclones and their influence on human mobility. Here, we studied how severe tropical storms could influence human mobility patterns in coastal urban populations using individuals’ movement data collected from Twitter. We selected three significant tropical storms, including Hurricane Sandy, Typhoon Wipha, and Typhoon Haiyan. We analyzed the human movement data before, during, and after each event, comparing the perturbed movement data to movement data from steady states. We also used different statistical analysis approaches to quantify the strength and duration of human mobility perturbation. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions; however, the nature-derived Lévy Walk model still predominantly governs human mobility. Also, human mobility exhibits a surprisingly mild and brief perturbation for Hurricane Sandy and Typhoon Wipha, while the duration of disturbance was much longer for Typhoon Haiyan. Our finding that the Lévy Walk model can still predict human mobility suggests that bio-inspired examinations of human mobility patterns may uncover solutions to improve disaster evacuation, response and relief plans

From mobile phone data to the spatial structure of cities

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 metropolitan areas. 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.Comment: 14 pages, 15 figure