Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatiotemporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both...

Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling tools for the identification of compl...

from genetic homogeneity). CORT showed the same pattern of dichotomy; of particular note, but contrary to our a priori prediction, blood CORT levels were higher in auklets that bred on a colony in which early chlorophyll-a bloom in local waters suggested good feeding conditions, than in auklets that bred on a colony with later local chlorophylla bloom. Also contrary to prediction, feather CORT ...

Socio-economic maps contain important information regarding the population of a country. Computing these maps is critical given that policy makers often times make important decisions based upon such information. However, the compilation of socio-economic maps requires extensive resources and becomes highly expensive. On the other hand, the ubiquitous presence of cell phones, is generating larg...

Traffic scene understanding is an important part of future advanced driver assistance system (ADAS). In this paper we present a probabilistic approach for this problem. We use Hidden Markov Models (HMMs) for modeling the spatiotemporal dependencies of traffic situations. The parameters of the individual models are learned from a data base, generated by a professional driving simulation software...

Compressing sequences of complex time-varying surfaces as generated by medical instrumentations or complex physical simulations can be extremely challenging: repeated topology changes during the surface evolution render most of the previous techniques for compression of time-varying surfaces inefficient or impractical. In order to provide a viable solution, we propose a new approach based upon ...

This study focuses on predicting harmful algal bloom (HAB) events in Lake Okeechobee, a shallow lake Florida. A spatio-temporal deep learning model is employed to predict the levels of cyanobacteria Microcystis aeruginosa (M. aeruginosa) present for single-day and 14-day prediction horizon. Dat...