Inferring Unusual Crowd Events From Mobile Phone Call Detail Records

The pervasiveness and availability of mobile phone data offer the opportunity of discovering usable knowledge about crowd behaviors in urban environments. Cities can leverage such knowledge in order to provide better services (e.g., public transport planning, optimized resource allocation) and safer cities. Call Detail Record (CDR) data represents a practical data source to detect and monitor unusual events considering the high level of mobile phone penetration, compared with GPS equipped and open devices. In this paper, we provide a methodology that is able to detect unusual events from CDR data that typically has low accuracy in terms of space and time resolution. Moreover, we introduce a concept of unusual event that involves a large amount of people who expose an unusual mobility behavior. Our careful consideration of the issues that come from coarse-grained CDR data ultimately leads to a completely general framework that can detect unusual crowd events from CDR data effectively and efficiently. Through extensive experiments on real-world CDR data for a large city in Africa, we demonstrate that our method can detect unusual events with 16% higher recall and over 10 times higher precision, compared to state-of-the-art methods. We implement a visual analytics prototype system to help end users analyze detected unusual crowd events to best suit different application scenarios. To the best of our knowledge, this is the first work on the detection of unusual events from CDR data with considerations of its temporal and spatial sparseness and distinction between user unusual activities and daily routines.Comment: 18 pages, 6 figure

Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets

Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products

ON CORRELATING BIRD MIGRATION TRAJECTORY WITH CLIMATE CHANGES

Climate changes are expected to affect bird migration in several aspects including timing changes, breeding and migration orientation. The correlation analysis of several climate conditions (e.g. temperature, wind, humidity, etc) and bird migration trajectory is the key for explaining bird behavior during migration. Moreover, the resulting correlation can be used for predicting new bird behavior according to climate changes. In this paper we propose an integrated solution for correlating bird migration trajectory with climate conditions. This solution is composed by two orthogonal and complementary methods. The first method concerns discovering regions where birds are used to stop during their migration. The second method is based on a machine learning algorithm for classifying bird stops according to climate conditions. A real bird migration scenario was used for assessing the accuracy of the integrated solution

Mining sensor datasets with spatiotemporal neighborhoods

Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on real-world datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods