Spatial Statistics 2022: Climate and the Environment
19 – 22 July 2022 | University of Colorado Boulder, USA
Welcome to the 6th Spatial Statistics conference, which will be held at the University of Colorado Boulder, USA, from 19 – 22 July 2022 under the theme Climate and the Environment.
The conference will provide a forum to debate and discuss how to use spatially referenced data to advance our understanding and provide support for decision making in the domain of Earth system dynamics.
Our physical environment is dynamic, continuously evolving at many scales of time and space. Understanding the Earth’s climate system has become even more critical in recent decades with the realization that many parts of society and ecological systems are vulnerable to rapid change. The mechanisms for these changes need to be better understood because of the great consequences they have, for society and the environment well into this century and beyond.
Climate is the result of many diverse processes such as: local rainfall and temperature, land use and vegetation, or the global jetstream and ocean currents. Weather that historically would be considered extreme is now more common, and the vulnerability of economies and infrastructure, particularly for developing countries, to large weather events make seasonal forecasting critical. Effects on climate can come from variations in the Sun’s radiation, to human activities in transport and industry, deforestation and urban concentrations. The effects can be diverse: different patterns may emerge in epidemics, stresses can develop on local ecosystems, or sea levels can rise for coastal areas. These impacts have complex dynamics and feedbacks, have many uncertain components, and so require solid, statistically sound predictions for a wide variety of stakeholders. The field of spatial statistics has developed in recent years to address many of these challenging problems connected to the Earth system. This includes increasing attention on deep learning methods, applications of Bayesian methodology for large data volumes, extreme value theory, and the synthesis of spatial and temporal models for representing climate processes. The need for well grounded spatial and spatio-temporal statistics is huge, being the leading discipline to interpret observational data and also attach measures of uncertainty to conclusions and predictions.
This conference will focus on climate change dynamics, their causes, their effects and their future. The conference theme will be the perspective of the Earth as a unified system with connections and feedbacks between physical and biological spheres and also human activities.
Crucial developments in the methodology are in new scalable methods, spatio-temporal statistics, prediction and statistical aspects of modeling, like spatial and spatio-temporal extremes, attribution and forecasting.
Professor Alfred Stein
University of Twente, The Netherlands
Prof. dr. ir. Alfred Stein (1958) is professor in Spatial Statistics and Image Analysis. He received his MSc in mathematics and information science, with a specialization in applied statistics from Eindhoven University of Technology. He obtained a PhD in 1991 at Wageningen University on spatial statistics.
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He started his career at the soil science and geology department of Wageningen university. In 1995 he was appointed a visiting professor at the Faculty ITC, in the soils department. In 1999 this changed to the department of spatial data acquisition.
In 2000 he was appointed a professor at the chair of mathematical and statistical models in Wageningen university (0.2) and in 2002 he became a 0.8 professor at the new department of Earth Observation Science at ITC, which he has headed for more than 10 years. In 2008 he became vice-rector research of the institute, a position that he had for four years. This was followed in 2012 by a position as portfolio holder education of the management team of the faculty.
His research interests focus on statistical aspects of spatial and spatio-temporal data, like monitoring data, in the widest sense. Optimal sampling, image analysis, spatial statistics, use of prior information, but also issues of data quality, fuzzy techniques, random sets, all in a Bayesian setting.
Christopher K. Wikle
University of Missouri (MU), USA
Christopher K. Wikle is Curators’ Distinguished Professor and Chair of Statistics at the University of Missouri (MU), with additional appointments in Soil, Environmental and Atmospheric Sciences and the Truman School of Public Affairs.
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He received a PhD co-major in Statistics and Atmospheric Science in 1996 from Iowa State University. He was research fellow at the National Center for Atmospheric Research from 1996-1998, after which he joined the MU Department of Statistics. His research interests are in spatio-temporal statistics applied to environmental, ecological, geophysical, agricultural and federal survey applications, with particular interest in dynamics. His work has been concerned with formulating computationally efficient deep hierarchical Bayesian models motivated by scientific principles, with more recent work at the interface of deep neural models in machine learning.
Awards include elected Fellow of the American Statistical Association (ASA), Institute of Mathematical Statistics (IMS), elected Fellow of the International Statistical Institute (ISI), Distinguished Alumni Award from the College of Liberal Arts and Sciences at Iowa State University, ASA Environmental (ENVR) Section Distinguished Achievement Award, co-awardee 2017 ASA Statistical Partnership Among Academe, Industry, and Government (SPAIG) Award, the MU Chancellor’s Award for Outstanding Research and Creative Activity in the Physical and Mathematical Sciences, the Outstanding Graduate Faculty Award, and Outstanding Undergraduate Research Mentor Award. His book Statistics for Spatio-Temporal Data (co-authored with Noel Cressie) was the 2011 PROSE Award winner for excellence in the Mathematics Category by the Association of American Publishers and the 2013 DeGroot Prize winner from the International Society for Bayesian Analysis. His latest book, Spatio-Temporal Statistics with R, with Andrew Zammit-Mangion and Noel Cressie, was published in 2019 and is free to download at spacetimewithR.org. This book won the 2019 Taylor and Francis award for Outstanding Reference/Monograph in the Science and Medicine category. Dr. Wikle is Associate Editor for several journals and is one of six inaugural members of the Statistics Board of Reviewing Editors for Science.
We are accepting oral and poster abstracts on the topics listed below. They should be submitted using the online abstract submission system. Deadline 28 January 2022
- Space-time statistics, e.g. geostatistics, point patterns, estimation methods, large dimensions
- Spatial deep learning
- Inverse modeling
- Modeling of extremes
- Stochastic geometry, tesselation, point processes, random sets
- Causal statistical modeling
- Trajectory/movement modeling
- Climate system modeling and observations
- Health e.g. epidemiology, geohealth and global health
- Spatially-Explicit Ecological Models
- Plant and animal epidemiology
- Quantifying the spatial extent of hazards and risk
- Crime and poverty mapping
- Space/time econometrics
- Interface of Neural Computing and Spatial/Spatio-Temporal Statistics
- Inferring Movement and Behavior from Telemetry