URISA is pleased to announce that it is accepting applications for its Vanguard Cabinet through December 5, 2011. The Vanguard Cabinet (VC) is a URISA initiative to engage young GIS practitioners, increase their numbers in the organization, and better understand the concerns facing these future leaders of the GIS community. The VC is an advisory board made up of URISA young professionals who represent the young membership of the organization. The Cabinet’s mission is to collaborate with URISA’s Board of Directors and Committees in creating and promoting programs and policies of benefit to young professionals. Comprised entirely of passionate young members selected from different geospatial disciplines, the Cabinet aims to position URISA as the center of opportunities for creative young professionals who are committed to improving URISA and the geospatial profession via innovation, collaboration, networking, and professional development.
URISA members who are 35 and under are encouraged to apply for membership to the Cabinet. Each year, five young professionals will be selected by the Vanguard Steering Committee, and each will serve a two year appointment on the Cabinet. In addition, each Cabinet member will participate on a URISA Committee to bring their unique perspective to the activities of that Committee. In recognition of their service, each Cabinet member will be offered a free GIS-Pro conference registration while serving on the Cabinet. In addition, one Cabinet member will be chosen as GIS Young Professional of the Year and will be awarded free tuition to the URISA Leadership Academy (www.urisa.org/ULA).
If you want to connect and network with young, ambitious GIS practitioners who are committed to improving their profession and their communities, then the Vanguard Cabinet is the organization for you. Details and an application form are available online at http://www.urisa.org/about/vanguard.
[Source: URISA press release]
Computers & Geosciences, Available online 29 October 2011
Kristin Stock, Tim Stojanovic, Femke Reitsma, Yang Ou, Mohamed Bishr, Jens Ortmann, and Anne Robertson
- This paper describes an information model for a geospatial knowledge infrastructure.
- It uses ontologies to represent domain and scientific knowledge semantics.
- This semantic information can be used to enable intelligent discovery of resources.
- The model was successfully implemented in a working knowledge infrastructure.
- The evaluation identified some issues in creating the ontologies.
“A geospatial knowledge infrastructure consists of a set of interoperable components, including software, information, hardware, procedures and standards, that work together to support advanced discovery and creation of geoscientific resources, including publications, data sets and web services. The focus of the work presented is the development of such an infrastructure for resource discovery. Advanced resource discovery is intended to support scientists in finding resources that meet their needs, and focuses on representing the semantic details of the scientific resources, including the detailed aspects of the science that led to the resource being created.
“This paper describes an information model for a geospatial knowledge infrastructure that uses ontologies to represent these semantic details, including knowledge about domain concepts, the scientific elements of the resource (analysis methods, theories and scientific processes) and web services. This semantic information can be used to enable more intelligent search over scientific resources, and to support new ways to infer and visualise scientific knowledge.
“The work describes the requirements for semantic support of a knowledge infrastructure, and analyses the different options for information storage based on the twin goals of semantic richness and syntactic interoperability to allow communication between different infrastructures. Such interoperability is achieved by the use of open standards, and the architecture of the knowledge infrastructure adopts such standards, particularly from the geospatial community. The paper then describes an information model that uses a range of different types of ontologies, explaining those ontologies and their content. The information model was successfully implemented in a working geospatial knowledge infrastructure, but the evaluation identified some issues in creating the ontologies.”
GIScience & Remote Sensing, Volume 48, Number 2 / April-June 2011
George Ch. Miliaresis and Andreas Tsatsaris
“The regional temporal and spatial multi-temporal land surface temperature (LST) MODIS dataset and elevation data are used to compute the day and night temperature variation in Greece in 2008. Clustering was applied and eight cluster centroids captured the temporal pattern of near-diurnal temperature (01:30 a.m. and 01:30 p.m.) variability while elevation statistics were computed per cluster. The spatial distribution of the clusters indicate that mean elevation, elevation variability, proximity to the sea, and the major inland water bodies were the key factors controlling the near-diurnal LST variability in Greece.”
Emerging Themes in Epidemiology, 8:7, 04 November 2011
Colantonio A, Moldofsky B, Escobar M, Vernich L, Chipman M, and McLellan B
“Background: The aim of this study is to show how geographical information systems (GIS) can be used to track and compare hospitalization rates for traumatic brain injury (TBI) over time and across a large geographical area using population based data.
“Results & Discussion: Data on TBI hospitalizations, and geographic and demographic variables, came from the Ontario Trauma Registry Minimum Data Set for the fiscal years 1993-1994 and 2001-2002. Various visualization techniques, exploratory data analysis and spatial analysis were employed to map and analyze these data. Both the raw and standardized rates by age/gender of the geographical unit were studied.
Example of mapping of TBI rates and cluster analyses.
“Data analyses revealed persistent high rates of hospitalization for TBI resulting from any injury mechanism between two time periods in specific geographic locations.
“Conclusions: This study shows how geographic information systems can be successfully used to investigate hospitalizaton rates for traumatic brain injury using a range of tools and techniques; findings can be used for local planning of both injury prevention and post discharge services, including rehabilitation.”