URISA is now accepting abstract submissions for the 2011 URISA GIS in Public Health Conference. The conference will take place in Atlanta, Georgia, 27-30 June 2011. The conference was established to provide an open and participatory forum for advancing the effective use of spatial information and geographic information system technologies across the domains of public health, health care, and community health preparedness.
The educational program is developed through a review of submissions received through the Call for Presentations. The range of focal areas for the URISA GIS in Public Health Conference is broad, reflecting the varied areas of interest those engaged in both research and practice in public health. The committee welcomes the submission of individual papers, sessions, and posters in any area that meets the general criteria:
- has a spatial component,
- is related to public health, and
- holds interest due to its novelty or information content.
Individuals who submit abstracts are asked to indicate the Thematic Area(s); Methods and Data Sources; and Programs, Policy and Practice Area(s) which apply to their topic.
Abstract submissions will be accepted until 11 January 2011. The link to the Call for Presentations is: http://www.urisa.org/2011health_call
[Source: URISA press release]
Letter to the editor on The Economist, 07 October 2010
“SIR – Your special report on forests (“Seeing the wood”, September 25th) succinctly captured the fact that monetary flow through the Reduced Emissions from Deforestation and Forest Degradation (REDD) mechanism to forest communities is slowed by the problems of unclear land ownership. A key factor in the success of REDD is the application of geospatial technology in the form of geographical information systems (GIS) to collate, map and report forest carbon emission information to investors and international regulatory agencies.
“GIS is the same technology that under-pins the determination of property lines and land tenure, as well as the mapping of land-use patterns in general. Thus investments in REDD, by providing support for implementing GIS for forestry, have a dual benefit. They not only allow those countries to meet the requirements to validate REDD payments but also help them to establish the technical basis for economic development. It isn’t the solution, but it is a start.
“D. James Baker
Global Carbon Measurement Programme
The William J. Clinton Foundation
Fisheries Research, Available online 30 September 2010
Charles F. Adams, Bradley P. Harris, Michael C. Marino II, and Kevin D.E. Stokesbury
“Spatially explicit management strategies require the identification of appropriate spatial scales for the observation, analysis and management of fisheries. Although the mesoscale (km) is the domain of traditional fisheries stock units, there have been few attempts to describe mesoscale aggregations of scallops, typically referred to as beds. We quantified the average bed diameter of sea scallops (Placopecten magellanicus) using geostatistics. Data were collected between 1999–2007 in the Northern Edge (NE) of Closed Area II and the Nantucket Lightship (NL) Closed Area on Georges Bank. Average bed diameter in the NE varied between 6.5–8.6 km with classical variograms, and 7.6–9.8 km with robust variograms. Average bed diameter in the NL varied between 3.0–10.1 km with classical variograms, and 4.0–13.22 km with robust variograms. There was more spatial structure in the NE. The spatial structure of the NL was less clearly defined and/or more variable. Kriged maps indicate the presence of multiple beds in both areas. Densities of ca. 1.24 scallops/m2 appeared to correspond well with the average bed diameters given by variograms. These results can be used as guidelines for the observation and analysis of the sea scallop resource in the NE and NL.”
IEEE International Geoscience and Remote Sensing Symposium, 12-17 July 2009
Gokaraju, B. Durbha, S.S. King, R.L., and Younan, N.H.
“Harmful Algal Blooms (HABs) pose an enormous threat to the U.S. marine habitation and economy in the coastal waters. Federal and state coastal administrators have been working in devising a state-of-the-art monitoring and forecasting system for these HAB events. These modernized HAB systems provide useful and forewarning information to a varied user community. However, the lack of standardization in the data exchange mechanism with the current available systems causes an impediment to the wide area coastal observation and management. Hence, there is a need for the system to adapt the services oriented architecture and the OGC (Open Geospatial Consortium) sensor web enablement framework. We propose a HAB monitoring system by adopting the standardized OGC sensor web and using machine learning approaches for the detection of HAB events in the region of Gulf of Mexico. Various feature extraction techniques have been used in obtaining features of both HAB and Non-HAB data. Kernel based Support vector machines have been used as a classifier in the detection of HAB’s. The performance of this approach is analyzed by accuracy measures like Kappa Coefficient, N-fold cross validation average and Confusion Matrix on a considerable test data.”