The Open Geospatial Consortium, Inc. (OGC) and the International Environmental Modeling & Software Society (iEMSs) Secretariat have signed a Memorandum of Understanding (MOU) to collaborate in standards development, education and outreach to enable and promote the use of interoperable Web based geospatial technologies in environmental modeling and software tools.
“The time has come for environmental models to interconnect flexibly with one another and with services and data from other disciplines,” explained Phillip C. Dibner, a consulting scientist and engineer, and chair of the OGC Earth System Science Domain Working Group. “Typically, this is difficult and cumbersome, but it becomes much more feasible when models implement open interface and encoding standards…”
Alexey Voinov, President of iEMSs, said, “Models are more than data and more than software. To connect various models in a meaningful and efficient way is a challenge since we need to connect paradigms, scales, algorithms, etc. Yet it is essential since there is growing interest in integrated modeling as a way to study complex systems. OGC has had much success in developing interfaces and encoding standards for data. We hope that together we can learn how to better connect models.”
The iEMSs (pronounced “eye-em-es”) (http://www.iemss.org) is a not-for-profit organization uniting people and organizations dealing with environmental modeling, software and related topics. The iEMSs seeks to develop and use environmental modeling and software tools to advance science and improve decision making with respect to resource and environmental issues. This places an emphasis on interdisciplinary and the development of generic frameworks and methodologies which integrate environmental models and software tools.
About the Open Geospatial Consortium (OGC®)
The OGC® is an international consortium of more than 385 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OpenGIS® Standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. OGC Standards empower technology developers to make geospatial information and services accessible and useful with any application that needs to be geospatially enabled. Visit the OGC website at http://www.opengeospatial.org.
[Source: OGC press release]
“The Wilderness Society seeks a Landscape Ecologist to join our Center for Landscape Analysis. This is an exceptional opportunity for a conservation science professional to apply his or her spatial and ecological analysis expertise to land conservation and management challenges and to work in the nexus between science and public policy. We seek a leader who can apply skills in geographic information systems (GIS), ecology, and natural resource management. These skills will be used to design new spatially-explicit projects, conduct project work, and communicate scientific results to further our wildland management and conservation goals.”
National Center for Ecological Analysis and Synthesis (NCEAS), UC Santa Barbara
A postdoctoral researcher is sought to work closely with Dr. Ben Halpern at the National Center for Ecological Analysis and Synthesis (NCEAS) as part of a large, multi-institution collaboration to develop and implement an Ocean Health Index (OHI; see below). There is guaranteed funding for 18 months and a commitment to find funding for an additional 18 months.
With support from a research assistant, the post-doctoral researcher will develop and test analytical approaches for (1) scaling and comparing indices relevant to ocean health across different regions and geographic scales, and (2) combining and/or reducing indices while preserving overall information about ocean health. S/he will also collaborate with the other post-doctoral researcher based at NCEAS to (3) conduct an extensive literature review to assess the state of indicator science (in general and in specific proposed locations) and (4) mine historical, empirical, and theoretical data sets to determine the quality and quantity of available data in specific locations. As part of the larger OHI team, s/he will communicate with the other project researchers to coordinate research projects and participate in project meetings, where appropriate.
Candidates must have a Ph.D. in Ecology, Environmental Studies, Statistics, Oceanography, Geography or related fields and demonstrated experience with multivariate analyses. A strong quantitative background (e.g., experience with the statistical package R and/or SAS, collection and management of large databases, processing satellite data, GIS data analysis, and synthetic research) is a plus, and topical experience (e.g., familiarity with ecosystem modeling programs such as EcoSim and Atlantis, indicator science, and/or food web and ecosystem ecology) would be ideal. Must work well in a collaborative research environment.
Timeframe: 1.5 years guaranteed with additional 1.5 years likely, starting as soon as possible
To apply,: submit CV and cover letter to Ben Halpern (Halpern@nceas.ucsb.edu) by 08 February 2010.
Spatial Distribution of African Animal Trypanosomiasis in Suba and Teso Districts in Western Kenya
Samuel Thumbi, Joseph Jung’a, Reuben Mosi, Francis McOdimba
BMC Research Notes 2010, 3:6
“Studies on the epidemiology of African Animal Trypanosomiasis (AAT) rarely consider the spatial dimension of disease prevalence. This problem is confounded by use of parasitological diagnostic methods of low sensitivity in field surveys.
“Here we report a study combining highly sensitive and species specific molecular diagnostic methods, and Geographical information system (GIS) for spatial analysis of trypanosome infection patterns, to better understand its epidemiology. Blood samples from 44 and 59 animals randomly selected from Teso and Suba districts respectively were screened for trypanosomes using PCR diagnostic assays.”
Agricultural Water Management, Vol. 97 Issue 2 (February 2010), pages 240-246
“A groundwater monitoring network can provide quantity and quality data necessary to make informed decisions regarding the state of the environment. A properly designed monitoring system provides a representative understanding of the state of the monitored area. The selection of the optimum number of monitoring sites and their spatial distribution is a major challenge for the hydrogeologist. On the one hand, improper distribution of monitoring sites or insufficient number of sites will not provide a representative view of the state of the environment. On the other hand, if the sampled sites are too many, the information obtained is redundant and the monitoring network is costly and inefficient. A new methodology combining vulnerability mapping and geostatistics is proposed to help define the most efficient groundwater quality monitoring network on a regional scale. Vulnerability mapping identifies areas with high pollution potential, and in turn, prioritises for monitoring. A geostatistics methodology is then used to interpret the obtained data and to examine the spatial distribution of monitored parameters at different sites. The accuracy of spatial mapping reflects the effectiveness of the distribution of the monitoring sites. The methodology was applied to assess the nitrate monitoring network in the Heretaunga basin, Hawke’s Bay, New Zealand. The DRASTIC approach was used to prepare a vulnerability map for the area of study, and kriging variance was used to check the spatial distribution of the sites. Based on this study, it was found that some areas with high vulnerability are not covered within the existing network indicating the number of monitoring sites and their distribution is not efficient. Some sites should be dropped and some others need to be added to the existing network.”
Handbook of Applied Spatial Analysis
Software Tools, Methods and Applications
Fischer, Manfred M.; Getis, Arthur (Eds.)
2010, XVI, 811 p. 190 illus., Hardcover
Chapter A.1: Spatial Statistics in ArcGIS, by Lauren M. Scott and Mark V. Janikas
“Tools to perform spatial analysis have been extended over the years to include geostatistical techniques (Smith et al. 2006), raster analysis (Tomlin 1990), analytical methods for business (Pick 2008), 3D analysis (Abdul-Rahman et al. 2006), network analytics (Okabe et al. 2006), space-time dynamics (Peuquet 2002), and techniques specific to a variety of industries (e.g., Miller and Shaw 2001). In 2004, a new set of spatial statistics tools designed to describe feature patterns was added to ArcGIS 9. This chapter focuses on the methods and models found in the Spatial Statistics toolbox.
“Spatial statistics comprises a set of techniques for describing and modeling spatial data. In many ways they extend what the mind and eyes do, intuitively, to assess spatial patterns, distributions, trends, processes and relationships. Unlike traditional (non-spatial) statistical techniques, spatial statistical techniques actually use space – area, length, proximity, orientation, or spatial relationships – directly in their mathematics (Scott and Getis 2008).”
…from V1 Magazine…
“V1: You’re a high-energy individual that has applied every waking hour for more than 40 years toward the design and application of technology to help manage the earth. Are your concerns for our planet a strong motivator for you?
“Dangermond: This purpose has always been the reason for ESRI, and why all of us here work so hard. I think in our own small way ESRI, through the incredible work of our users, has been able to make a difference. However, given the immensity of the problem there is so much more to be done, and we need to keep driving our vision of integrating geographic thinking into virtually all human activities.”