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Spatio-Temporal Statistics in Geodesign
 
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Distinguished professor Dr. Noel Cressie from the University of Wollongong brings his award-winning studies in spatial statistics to geodesign in this thought-provoking keynote. Cressie shows how introducing conditional probability to geodesign allows spatio-temporal statistics to handle uncertainties in data.
Views: 1096 Esri Geodesign
Discussion: Spatio-Temporal Statistics
 
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Noel Cressie, Jack Dangermond, and Carl Steinitz join Tom Fisher to discuss creating policy through geodesign at both large and small scales using the Cressie's principles of conditional probability.
Views: 330 Esri Geodesign
Data Issues: Multiple Testing, Bias, Confounding, Missing...
 
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Dr. Lance Waller from Emory University presents a lecture titled "Data Issues: Multiple Testing, Bias, Confounding, & Missing Data." View Slides https://drive.google.com/open?id=0B4IAKVDZz_JUczRSd0NucjlhT00 Lecture Abstract Once data are scraped, wrangled, linked, merged, and analyzed, what information do they reveal and can we trust the resulting conclusions? In this presentation, we define and review data issues relating to the analysis and interpretation of observational data from the field of epidemiology and consider implications for data science, especially regarding the goal of moving from big data to knowledge. Specifically, we explore concepts of bias, confounding, effect modification, and missing/mismeasured data as applied to data science. We provide an analytic context based on sampling concepts and explore relevant literature and tools from epidemiology, biostatistics, computer science, and data science. As with many issues in data science, the full applicability of the concepts is very much a work in progress and present multiple opportunities for future development. About the Speaker Lance A. Waller, Ph.D. is Rollins Professor and Chair of the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Committee on Applied and Theoretical Statistics. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Join our weekly meetings from your computer, tablet or smartphone. Visit our website to view our schedule and join our next live webinar! http://www.bigdatau.org/data-science-seminars
Space, Time and Gradients: Why we need them in statistical modeling for public health data
 
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Sudipto Banerjee - Space, Time and Gradients Why we need them in statistical modeling for public health data Advances in Geographical Information Systems (GIS) and related software have led to a burgeoning of spatial-temporal databases. Statisticians and spatial analysts today routinely encounter situations where they seek to model relationships among variables across space and time. In recent times interest has turned to inferring about rates of change of health outcomes over space and time. Why are such questions relevant and how should we estimate them? One example considers analyzing monthly hospitalization rates aggregated over the counties in California where hospital management seeks to carry out inference on gradients of the temporal process, while at the same time accounting for spatial similarities across neighboring regions. Another example (an extension) is to analyze spatial-temporal gradients for environmental pollutants to understand the nature of dispersal of pollutants. Here, we are interested in directional rates of change over space at any given time, temporal gradients at any given location and even "mixed" gradients, e.g., how the temporal rate of change varies over space. We will work within a fully Bayesian inferential paradigm without unnecessary, and potentially inflexible, parametric modeling assumptions and obtain the full posterior predictive distribution for these gradients using process-based models. (Co-authors: Harrison Quick and Bradley P. Carlin)
Views: 339 uclachipts
Kriging
 
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In statistics, originally in geostatistics, Kriging or Gaussian process regression is a method of interpolation for which the interpolated values are modeled by a Gaussian process governed by prior covariances, as opposed to a piecewise-polynomial spline chosen to optimize smoothness of the fitted values. Under suitable assumptions on the priors, Kriging gives the best linear unbiased prediction of the intermediate values. Interpolating methods based on other criteria such as smoothness need not yield the most likely intermediate values. The method is widely used in the domain of spatial analysis and computer experiments. The technique is also known as Kolmogorov Wiener prediction. The theoretical basis for the method was developed by the French mathematician Georges Matheron based on the Master's thesis of Danie G. Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa. Krige sought to estimate the most likely distribution of gold based on samples from a few boreholes. The English verb is to krige and the most common noun is kriging; both are often pronounced with a hard "g", following the pronunciation of the name "Krige". This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 10797 Audiopedia