Inferential Challenges for Large Spatio-Temporal Data Structures (17w5153)
The Banff International Research Station will host the "Inferential Challenges for Large Spatio-Temporal Data Structures" workshop from December 3rd to December 8th, 2017. As government agencies and other organizations are producing increasingly vast quantities of data, methods are needed for effective and efficient modelling and analysis. Not all of the data are uniformly high quality; in many instances, aggregation has occurred,leading to errors in some situations, or to mismatches in the geography. For example,health regions do not have the same geographic boundaries as census regions, so correlating health outcomes with demographic factors is not a straightforward exercise. The proposed event concerns the development and application of models for complex data arising in a number of fields, including healthcare, ecology, wildfire science, fisheries, and climate. One outcome of such models is the improved ability to map disease risk, highlighting geographic, climatic and other environmental factors which may be at play. In developing the models, there is a need for intensive computation, not only because of the scope and complexity of the data involved, but also because of the complexity of the models being developed. The workshop will feature a number of computational approaches and approximations which can give the modeller, whether in healthcare or in environmental assessment, the ability to construct useful statistical output which can guide management decisions. The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disc iplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineeri ng Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).
