11,401 research outputs found
Patterns, Entropy, and Predictability of Human Mobility and Life
Cellular phones are now offering an ubiquitous means for scientists to observe life: how people act, move and respond to external influences. They can be utilized as measurement devices of individual persons and for groups of people of the social context and the related interactions. The picture of human life that emerges shows complexity, which is manifested in such data in properties of the spatiotemporal tracks of individuals. We extract from smartphone-based data for a set of persons important locations such as “home”, “work” and so forth over fixed length time-slots covering the days in the data-set (see also [1], [2]). This set of typical places is heavy-tailed, a power-law distribution with an exponent close to −1.7. To analyze the regularities and stochastic features present, the days are classified for each person into regular, personal patterns. To this are superimposed fluctuations for each day. This randomness is measured by “life” entropy, computed both before and after finding the clustering so as to subtract the contribution of a number of patterns. The main issue that we then address is how predictable individuals are in their mobility. The patterns and entropy are reflected in the predictability of the mobility of the life both individually and on average. We explore the simple approaches to guess the location from the typical behavior, and of exploiting the transition probabilities with time from location or activity A to B. The patterns allow an enhanced predictability, at least up to a few hours into the future from the current location. Such fixed habits are most clearly visible in the working-day length.Peer reviewe
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
Topic modelling for routine discovery from egocentric photo-streams
Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals' lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed
What Did You Do Today? Discovering Daily Routines from Large-Scale Mobile Data
We present a framework built from two Hierarchical Bayesian topic models to discover human location-driven routines from mobile phones. The framework uses location-driven bag representations of people's daily activities obtained from celltower connections. Using 68 000+ hours of real-life human data from the Reality Mining dataset, we successfully discover various types of routines. The first studied model, Latent Dirichlet Allocation (LDA), automatically discovers characteristic routines for all individuals in the study, including ``going to work at 10am", ``leaving work at night", or ``staying home for the entire evening". In contrast, the second methodology with the Author Topic model (ATM) finds routines characteristic of a selected groups of users, such as ``being at home in the mornings and evenings while being out in the afternoon", and ranks users by their probability of conforming to certain daily routines
- …