676 research outputs found

    PERSPECTIVES ON LOCATION PRIVACY AND MOBILITY PREDICTION WHEN USING LOCATION-BASED SERVICES

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    Truth Discovery in Crowdsourced Detection of Spatial Events

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    ACKNOWLEDGMENTS This research is based upon work supported in part by the US ARL and UK Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the US ARL, the US Government, the UK Ministry of Defense or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.Peer reviewedPostprin

    The geography of city liveliness and consumption: evidence from location-based big data

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    Understanding the complexity in the connection between city liveliness and spatial configurationsfor consumptive amenities has been an important but understudied research field in fast urbanising countries like China. This paper presents the first step towards filling this gap though location-based big data perspectives. City liveliness is measured by aggregated spacetime human activity intensities using mobile phone positioning data.Consumptive amenities are identified by point-of-interest data from Chinese Yelp website (dian ping). The results provide the insights into the geographic contextual uncertainties of consumptive amenities in shaping the rise and fall in the vibrancy of city liveliness

    Hidden location prediction using check-in patterns in location based social networks

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    Check-in facility in a Location Based Social Network (LBSN) enables people to share location information as well as real life activities. Analysing these historical series of check-ins to predict the future locations to be visited has been very popular in the research community. However, it has been found that people do not intend to share the privately visited locations and activities in a LBSN. Research into extrapolating unchecked locations from historical data is limited. Knowledge of hidden locations can have a wide range of benefits to society. It may help the investigating agencies in identifying possible places visited by a suspect, a marketing company in selecting potential customers for targeted marketing, for medical representatives in identifying areas for disease prevention and containment, etc. In this paper, we propose an Associative Location Prediction Model (ALPM), which infers privately visited unchecked locations from a published user trajectory. The proposed ALPM explores the association between a user's checked-in data, the Hidden Markov Model and proximal locations around a published check-in for predicting the unchecked or hidden locations. We evaluate ALPM on real-world Gowalla LBSN dataset for the users residing in Beijing, China. Experimental results show that the proposed model outperforms the existing state of the art work in literature

    Spatio-temporal statistical models with application to atmospheric processes

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    This dissertation is concerned with spatio-temporal processes in the Atmospheric Sciences;In the first chapter, a comprehensive overview of spatio-temporal methods from the atmospheric science literature is presented. Focus is on Empirical Orthogonal Function (EOF), Principal Interaction Pattern (PIP), Principal Oscillation Pattern (POP), and spatio-temporal Canonical Correlation Analysis (CCA) methods. Previously unexamined issues related to measurement error, continuous space, and Bayesian ideas are considered;In the second chapter, harmonic analysis is used to make diagnostic inference about the spatial variation of the semiannual oscillation (SAO) in the Northern Hemisphere (NH) 500-hPa height field. The SAO is explained by the spatial and temporal asymmetries in the annual variation of stationary eddies. The SAO in the NH extratropics is a result of east-west land-sea contrasts, analogous to the well-known Southern Hemisphere (SH) SAO, which is explained by north-south land-sea contrasts;The third chapter examines the seasonal variability of mixed Rossby-gravity waves (MRGWs) in the lower stratosphere over the tropical western Pacific. Thirty-one years of lower stratospheric wind observations from four tropical Pacific stations are examined with seasonally varying cross-spectral analysis, which suggests significant twice-yearly peaks in the v-wind power and the mean squared coherence between the u- and v-winds, with peaks occurring in the winter-early spring and in summer-early fall. Horizontal momentum flux convergence is found with these waves, with the sign of the convergence opposite during the two seasonal maxima. Cyclic spectral analyses show that the frequency of the maximum v-wind power in the MRGW frequency band shifts seasonally;In the fourth chapter, a spatio-temporal statistical model is proposed that assumes a first-order Markov dynamic process combined with a spatially descriptive colored noise process. With a measurement error equation, a spatio-temporal Kalman filter gives predictions in time and at any spatial location. The model prediction equation includes a simple kriging analog as a special case. The model predicts well with simulated spatio-temporal data, and is superior to simple kriging applied independently at each time. Predictions of precipitation over the data-sparse South China Sea captures the dynamic variation of the spatial precipitation
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