Food safety continues to threaten public health. Machine learning holds potential in
leveraging large, emerging data sets to improve the safety of the food supply and mitigate …

The emergence of big data brings new opportunities for us to understand our socioeconomic
environments. We use the term social sensing for such individual-level big geospatial data …

Large-scale data sets of human behavior have the potential to fundamentally transform the
way we fight diseases, design cities, or perform research. Metadata, however, contain …

Mobile phones and other geolocated devices have produced unprecedented volumes of
data on human movement. Analysis of pooled individual human trajectories using big data …

In this paper, we present a novel approach to predict crime in a geographic space from
multiple data sources, in particular mobile phone and demographic data. The main …

Research has proven that stress reduces quality of life and causes many diseases. For this
reason, several researchers devised stress detection systems based on physiological …

We study six months of human mobility data, including WiFi and GPS traces recorded with
high temporal resolution, and find that time series of WiFi scans contain a strong latent …

Advances in information and communication technology and the collection of big data at
small spatiotemporal scales have opened up new opportunities to reveal spatiotemporal …

Predictive models for human mobility have important applications in many fields including
traffic control, ubiquitous computing, and contextual advertisement. The predictive …

Zipf-like distributions characterize a wide set of phenomena in physics, biology, economics,
and social sciences. In human activities, Zipf's law describes, for example, the frequency of …