Spatio-Temporal Constraints on Social Networks Workshop, University of California, Santa Barbara, Center for Spatial Studies, 13-14 December 2010
“My interest in this field of intellectual endeavour is situated at the intersection of geography and media and cultural studies. In particular, I am interested in the ways in which digital social networks and other convergent media, including Google Earth, Facebook and Twitter, are shaping everyday experiences of space and place in dynamic and complex ways and the potential of these technologies for forging social transformation and new modes of cultural citizenship, particularly among marginalized populations in the global South and elsewhere.”
This book on DVD simplifies the application of spatial statistics in GIS using case study data. The author explains the relationship between uncertainty in the data and uncertainty in the model, discusses sources of uncertainty and error, examines commonly used statistical distributions, and introduces methods for sensitivity and uncertainty analysis. The book also looks at the differences between continuous, regional, and discrete data and at the advantages and disadvantages of deterministic and statistical models.
About the Author: Konstantin Krivoruchko is a senior research associate on the Esri software development team who played a central role in developing ArcGIS Geostatistical Analyst. Prior to joining Esri in 1998, he was director of the GIS laboratory at the Sakharov Institute of Radioecology in Minsk, Belarus, where he developed GIS and spatial statistics curriculum, supervised PhD and graduate school candidate research pertaining to GIS applications and spatial statistical data analysis.
ISBN: 9781589481619 2011 894 pages $39.99
International Journal of Communications, Network and System Sciences, Vol. 4 No. 1, January 2011
Evens Jean, Ingmar Rauschert, Robert T. Collins, Ali R. Hurson, Sahra Sedigh, Yu Jiao
“Once deployed, sensor networks are capable of providing a comprehensive view of their environment. However, since the current sensor network paradigm promotes isolated networks that are statically tasked, the full power of the harnessed data has yet to be exploited. In recent years, users have become mobile entities that require constant access to data for efficient and autonomous processing. Under the current limitations of sensor networks, users would be restricted using only a subset of the vast amount of data being collected; depending on the networks they are able to access. Through reliance on isolated networks, proliferation of sensor nodes can easily occur in any area that has high appeals to users. Furthermore, support for dynamic tasking of nodes and efficient processing of data is contrary to the general view of sensor networks as subject to severe resource constraints. Addressing the aforementioned challenges requires the deployment of a system that allows users to take full advantage of data collected in the area of interest to their tasks. Such a system must enable interoperability of surrounding networks, support dynamic tasking, and swiftly react to stimuli. In light of these observations, we introduce a hardware-overlay system designed to allow users to efficiently collect and utilize data from various heterogeneous sensor networks. The hardware-overlay takes advantage of FPGA devices and the mobile agent paradigm in order to efficiently collect and process data from cooperating networks. The computational and power efficiency of the prototyped system are herein demonstrated. Furthermore, as a proof-of-concept, we present the implementation of a distributed and autonomous visual object tracker implemented atop the Reconfigurable and Interoperable Sensor Network (RISN) showcasing the network’s ability to support ad-hoc agent networks dedicated to user’s tasks.”