Day: September 3, 2010

Supporting VGI with Standardized Sensor Web and Web-based Geoprocessing

/ Matt Artz

Position paper to be presented at the Role of VGI in Advancing Science workshop, GIScience 2010, Zurich, Switzerland

Theodor Foerster, SWSL

“Volunteered Geographic Information (VGI) is about user-generated geographic content created with low-cost tools and shared on the Web. It appears currently to be a hot topic in research and we believe that it is relevant for our research regarding Sensor Web and web-based Geoprocessing. We are looking forward to sharing our experience about Sensor Web and web-based Geoprocessing with the VGI community. Additionally, we are curious about the input especially regarding motivating and creating incentives for people to contribute VGI.”

Delphi and Olympia: The Spatial Politics of Panhellenism in the Archaic and Classical Periods

/ Matt Artz

Cambridge University Press, 2010

Michael Scott

“Michael Scott provides a synthetic overview of the built structures and monumental dedications at Delphi and Olympia from 650 to 300 BC. The book complements C. Morgan’s Athletes and Oracles (1990), which focuses on Delphi and Olympia in the tenth to seventh centuries BC. Scott argues that Olympia and Delphi have been studied primarily for their athletics and oracle respectively, while the material culture and spatial politics of these sanctuaries has been underappreciated. The book has an introduction (essentially a literature review and a description of Scott’s method), a chapter on the regulation of dedications at Delphi and Olympia (precious little is known), five chapters tracing the chronological development of the sanctuaries, a chapter that compares the two sanctuaries, and a chapter that critiques the contemporary concept of Panhellenism. Overall, the book is a notable success. The archaeological bibliography on these two sanctuaries is extraordinarily large and written primarily in French and German; Scott’s synthesis and analysis of this vast amount of research provides a particularly helpful English-language overview of the sanctuaries’ material culture, while offering several novel, illuminating ideas along the way.”

Geostatistics in Subsurface Flow & Transport Modeling: Select Topics & Future Research

/ Matt Artz

Wyoming Geographic Information Science Center

Friday, 10 September 2010, 12:00 – 1:00 p.m.

Ye Zhang

“Geostatistics studies the spatial correlation of subsurface fluid flow properties, notably permeability and porosity of porous rocks. It is an important tool used in a variety of subsurface modeling applications, e.g., parameter estimation, reservoir modeling, history matching, sample network design, etc. This talk will present two research projects where geostatistics is an integral aspect of the study. One project uses geostatistics to estimate effective parameters for groundwater flow and solute transport modeling. The second project uses geostatistics to create a deepwater oil reservoir model to identify important uncertainty parameters to predictions. Ongoing and future research is also presented.”

Geostatistics for Assessing the Efficiency of a Distributed Physically-based Water Quality Model: Application to Nitrate in the Seine River

/ Matt Artz

Hydrological Processes, published online 01 September 2010

Edwige Polus, Nicolas Flipo, Chantal de Fouquet, Michel Poulin

“This article shows how geostatistics can be used to reduce distributed physically based model (DPBM) uncertainties when assessing nitrate concentrations along a 250-km stretch of the Seine River. First of all, co-kriging is used to build a set of boundary conditions (BCs) (inlet concentrations from major tributary rivers) consistent with validation data. It partially addresses the issues of errors in data used as model input and errors in data to be compared with model outputs. Then the analysis of temporal variograms (simple variograms of observed and simulated concentrations, and their cross-variogram) reveals a clear mismatch between simulated and observed values that was not detected by classical objective functions [root mean squared error (RMSE), etc.]. Variograms appear to include three components representing three different time scales: sub-weekly, monthly and annual scales. Mismatches between simulated and observed values are analysed as (i) wrong quantification of inputs to the river (especially during the rainy period: combined sewer overflow (CSO), waste water treatment plant (WWTP) and tributary contributions to the nitrate fluxes in the Seine River) and (ii) wrong description of physical processes within the river. Finally, the modelling of simple and cross-variograms appears to be a sensitive analysis tool which can be used to describe and reduce modelling uncertainties.”

Spatio-temporal Modelling of Disease Mapping of Rates

/ Matt Artz

Canadian Journal of Statistics, Article first published online 20 August 2010

Mahmoud Torabi and Rhonda J. Rosychuk

“This paper studies generalized linear mixed models (GLMMs) for the analysis of geographic and temporal variability of disease rates. This class of models adopts spatially correlated random effects and random temporal components. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and autoregressive smoothing over the temporal dimension are developed. The model also accommodates the interaction between space and time. However, the effect of seasonal factors has not been previously addressed and in some applications (e.g., health conditions), these effects may not be negligible. The authors incorporate the seasonal effects of month and possibly year as part of the proposed model and estimate model parameters through generalized estimating equations. The model provides smoothed maps of disease risk and eliminates the instability of estimates in low-population areas while maintaining geographic resolution. They illustrate the approach using a monthly data set of the number of asthma presentations made by children to Emergency Departments (EDs) in the province of Alberta, Canada, during the period 2001–2004. The Canadian Journal of Statistics © 2010 Statistical Society of Canada”