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AbstractAcross disciplines, scholars strive to better understand individuals' milieus—the people, places, and institutions individuals encounter in their daily lives. In particular, political scientists argue that racial and ethnic context shapes attitudes about candidates, policies, and fellow citizens. Yet, the current standard of measuring milieus is to place survey respondents in a geographic container and then to ascribe all that container's characteristics to the individual's milieu. Using a new dataset of over 2.6 million GPS records from over 400 individuals, we compare conventional static measures of racial and ethnic context to dynamic, precise measures of milieus. We demonstrate how low-level static measures tend to overstate how extreme individuals' racial and ethnic contexts are and offer suggestions for future researchers.

The currently deployed contact-tracing mobile apps have failed as an efficient solution in the context of the COVID-19 pandemic. None of them have managed to attract the number of active users required to achieve efficient operation. This urges the research community to re-open the debate and explore new avenues to lead to efficient contact-tracing solutions. In this paper, we contribute to this debate with an alternative contact-tracing solution that leverages the already available geolocation information owned by BigTech companies that have large penetration rates in most of the countries adopting contact-tracing mobile apps. Our solution provides sufficient privacy guarantees to protect the identity of infected users as well as to preclude Health Authorities from obtaining the contact graph from individuals. ; The research leading to these results received funding from the European Union's Horizon 2020 innovation action programme under the grant agreement No 871370 (PIMCITY project); the Ministerio de Economía, Industria y Competitividad, Spain, and the European Social Fund(EU), under the Ramón y Cajal programme (Grant RyC-2015-17732); the Ministerio de Educación, Cultura y Deporte, Spain, through the FPU programme (Grant FPU16/05852); the Ministerio de Ciencia e Innovación under the project ACHILLES (Grant PID2019-104207RB-I00); the Community of Madrid synergic project EMPATIA-CM (Grant Y2018/TCS-5046); and the Fundación BBVA under the project AERIS; and the NSERC Discovery Grant 2016-04521.

In the upcoming decades, traffic and travel times are expected to skyrocket, following tremendous population growth in urban territories. The increasing congestion on transport networks threatens cities efficiency at several levels such as citizens well-being, health, economy, tourism and pollution. Thus, local and national authorities are urged to promote urban planning innovation by adopting supportive policies leading to effective and radical measures. Prior to decision making processes, it is crucial to estimate, analyze and understand daily urban mobility. Traditionally, the information on population movements has been gathered through national and local reports such as census and surveys. Still, such materials are constrained by their important cost, inducing extremely low-update frequency and lack of temporal variability. On the meantime, information and communications technologies are providing an unprecedented quantity of up-to-date mobility data, across all categories of population. In particular, most individuals carry their mobile phone everywhere through their daily trips and activities. In this thesis, we estimate urban mobility by mining mobile network data, which are collected in real-time by mobile phone providers at no extra-cost. Processing the raw data is non-trivial as one must deal with temporal sparsity, coarse spatial precision and complex spatial noise. The thesis addresses two problematics through a weakly supervised learning scheme (i.e., using few labeled data) combining several mobility data sources. First, we estimate population densities and number of visitors over time, at fine spatio-temporal resolutions. Second, we derive Origin-Destination matrices representing total travel flows over time, per transport modes. All estimates are exhaustively validated against external mobility data, with high correlations and small errors. Overall, the proposed models are robust to noise and sparse data yet the performance highly depends on the choice of the spatial resolution. In addition, reaching optimal model performance requires extra-calibration specific to the case study region and to the transportation mode. This step is necessary to account for the bias induced by the joined effect of heterogeneous urban density and user behavior. Our work is the first successful attempt to characterize total road and rail passenger flows over time, at the intra-region level.Although additional in-depth validation is required to strengthen this statement, our findings highlight the huge potential of mobile network data mining for urban planning applications ; Dans les prochaines décennies, la circulation et les temps de trajets augmenteront drastiquement en raison du fort taux d'accroissement de la population urbaine. L'augmentation grandissante de la congestion sur les réseaux de transports menace le bon fonctionnement des villes à plusieurs niveaux, tels que le bien-être des citoyens, la santé, l'économie, le tourisme ou la pollution.Ainsi, il est urgent, pour les autorités locales et nationales, de promouvoir l'innovation pour la planification urbaine, à l'aide d'une politique de soutien à l'innovation et de prises de mesures radicales.Pour guider les processus de décisions, il est crucial d'estimer, analyser et comprendre la mobilité urbaine au quotidien.Traditionnellement, les informations sur les déplacements des populations était collectées via des rapports nationaux et locaux, tels que les recensements et les enquêtes. Toutefois, ces derniers ont un coût important, induisant une très faible fréquence de mise-à-jour, ainsi qu'une temporalité restreinte des données.En parallèle, les technologies de l'information et de la communication fournissent une quantité de données de mobilité sans précédent, au jour le jour, toutes catégories de population confondues. En particulier, les téléphones portables accompagnent désormais la majorité des citoyens lors de leurs déplacements et activités du quotidien. Dans cette thèse, nous estimons la mobilité urbaine par l'exploration des données du réseau mobile, qui sont collectées en temps réel, sans coût additionnel, par les opérateurs télécoms. Le traitement des données brutes est non-trivial en raison de leur nature sporadique et de la faible précision spatiale couplée à un bruit complexe.La thèse adresse deux problématiques via un schéma d'apprentissage faiblement supervisé (i.e., utilisant très peu de données labellisées) combinant plusieurs sources de données de mobilité. Dans un premier temps, nous estimons les densités de population et le nombre de visiteurs au cours du temps, à une échelle spatio-temporelle relativement fine.Dans un second temps, nous construisons les matrices Origine-Destination qui représentent les flux totaux de déplacements au cours du temps, pour différents modes de transports.Ces estimations sont validées par une comparaison avec des données de mobilité externes, avec lesquelles de fortes corrélations et de faibles erreurs sont obtenues.Les modèles proposés sont robustes au bruit et à la faible fréquence des données, bien que la performance des modèles soit fortement dépendante de l'échelle spatiale.Pour atteindre une performance optimale, la calibration des modèles doit également prendre en compte la zone d'étude et le mode de transport. Cette étape est nécessaire pour réduire les biais générés par une densité urbaine hétérogène et les différents comportements utilisateur.Ces travaux sont les premiers à estimer les flux totaux de voyageurs routiers et ferrés dans le temps, à l'échelle intra-régionale.Bien qu'une validation plus approfondie des modèles soit requise pour les renforcer, nos résultats mettent en évidence l'énorme potentiel de la science des données de réseaux mobiles appliquées à la planification urbaine

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

Data geolocation in the cloud is becoming an increasingly pressing problem, aggravated by incompatible legislation in different jurisdictions and compliance requirements of data owners. In this work we present a mechanism allowing cloud users to control the geographical location of their data, stored or processed in plaintext on the premises of Infrastructure-as-a-Service cloud providers. We use trusted computing principles and remote attestation to establish platform state. We enable cloud users to confine plaintext data exclusively to the jurisdictions they specify, by sealing decryption keys used to obtain plaintext data to the combination of cloud host geolocation and platform state. We provide a detailed description of the implementation as well as performance measurements on an open source cloud infrastructure platform using commodity hardware. ; InfraCloud

In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors' percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Includes bibliographical references ; Security Information and Event Management (SIEM) technology supports security threat detection and response through real-time and historical analysis of security events from a range of data sources. Through the retrieval of mass feedback from many components and security systems within a computing environment, SIEMs are able to correlate and analyse events with a view to incident detection. The hypothesis of this study is that existing Security Information and Event Management techniques and solutions can be complemented by location-based information provided by feeder systems. In addition, and associated with the introduction of location information, it is hypothesised that privacy-enforcing procedures on geolocation data in SIEMs and meta- systems alike are necessary and enforceable. The method for the study was to augment a SIEM, established for the collection of events in an enterprise service management environment, with geo-location data. Through introducing the location dimension, it was possible to expand the correlation rules of the SIEM with location attributes and to see how this improved security confidence. An important co-consideration is the effect on privacy, where location information of an individual or system is propagated to a SIEM. With a theoretical consideration of the current privacy directives and regulations (specifically as promulgated in the European Union), privacy supporting techniques are introduced to diminish the accuracy of the location information - while still enabling enhanced security analysis. In the context of a European Union FP7 project relating to next generation SIEMs, the results of this work have been implemented based on systems, data, techniques and resilient features of the MASSIF project. In particular, AlienVault has been used as a platform for augmentation of a SIEM and an event set of several million events, collected over a three month period, have formed the basis for the implementation and experimentation. A "brute-force attack" misuse case scenario was selected to highlight the benefits of geolocation information as an enhancement to SIEM detection (and false-positive prevention). With respect to privacy, a privacy model is introduced for SIEM frameworks. This model utilises existing privacy legislation, that is most stringent in terms of privacy, as a basis. An analysis of the implementation and testing is conducted, focusing equally on data security and privacy, that is, assessing location-based information in enhancing SIEM capability in advanced security detection, and, determining if privacy-enforcing procedures on geolocation in SIEMs and other meta-systems are achievable and enforceable. Opportunities for geolocation enhancing various security techniques are considered, specifically for solving misuse cases identified as existing problems in enterprise environments. In summary, the research shows that additional security confidence and insight can be achieved through the augmentation of SIEM event information with geo-location information. Through the use of spatial cloaking it is also possible to incorporate location information without com- promising individual privacy. Overall the research reveals that there are significant benefits for SIEMs to make use of geo-location in their analysis calculations, and that this can be effectively conducted in ways which are acceptable to privacy considerations when considered against prevailing privacy legislation and guidelines.