Statistics for Spatio-Temporal Data by Noel Cressie, Christopher K. Wikle

Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle ebook
Page: 624
Publisher: Wiley
Format: epub
ISBN: 0471692743, 9780471692744

In this case, he and de Montjoye were able to use those tools to uncover a simple mathematical relationship between the resolution of spatiotemporal data and the likelihood of identifying a member of a data set. Previously, researchers have examined several summary statistics (e.g. We extend the spatio-temporal data mining framework that we have developed earlier to analyze and manage such data [5]. In regard to these works, there is the increasing use of GIS combined with spatial statistics, which is a documented pattern throughout the social sciences (Goodchild and Janelle, 2004). In this field, current research progresses focus on analyzing traffic flows of individual links or local Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. Abstract: Statistical traffic data analysis is a hot topic in traffic management and control. A GIS was built within ArcGIS 9.2 (Environmental Research Systems Institute, Redlands, CA, USA) and statistical analyses were performed using Stata 11 (Stata Corporation, College Station, Texas). Hidalgo's group specializes in applying the tools of statistical physics to a wide range of subjects, from communications networks to genetics to economics. Pertinent to the current examination, we are interested in the ability to link publicly available crime data and tracking the 'mobility' of this data over a given period of time. Here we introduce a novel approach to aggregating, and . Risk maps have been defined in [47] as “outcomes of models of disease transmission based on spatial and temporal data”, incorporating “to varying degrees, epidemiological, entomological, climatic and environmental information”, and they have been applied to numerous diseases for . Department name when degree awarded. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. This framework is designed to analyze spatio-temporal data produced in several scientific domains. Radius of gyration, root mean square deviation (RMSD)) to identify similar 3D conformations in folding trajectories.