Spatio-temporal modelling, network design, health effects, and dimensional analysis

时空建模、网络设计、健康影响和维度分析

• 批准号: RGPIN-2022-03576
• 负责人: Zidek, James
• 金额: $ 1.31万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765729
• 关键词: Spatio; temporal; modelling; network; design

The burgeoning World population, newly predicted under present conditions to continue growing to as much as 15billion by the year 2100, is leading to increasing concerns about elevated levels of environment risk. More incidents e.g. high temperature episodes and the increased consumption of fossil fuels e.g. coal are expected. This dark future portends an increased need to effectively monitor environmental processes over space and time along with accurate methods for predicting these processes where they cannot be monitored in space and in the future, to support the management and mitigation of these risks through effective policy and regulation. Statisticians have a major role to play in this future, particularly in quantifying the uncertainties that abound. The proposed research will continue the applicant's long-term research program on the optimum design of spatio - temporal process monitoring networks and the use of the resulting data for modelling these processes for spatial prediction and future processes. We continue work on algorithms for overcoming the computational challenges associated with selecting optimal locations while ensuring that they are picked in an unbiased. But we will also provide extended theory for addressing the problems that arise with existing monitoring networks that have not bee selected in an unbiased way. In particular, since existing network stations are sometimes sited at preferentially selected geographical locations, we will continue seeking methods to reduce errors in statistical estimates induced by locating stations in that way. The research will also explore a promising objective method for siting them, one that insures they are well distributed over space. The remainder of the research concerns the use of data from these networks, possibly with adjustments for the effects of preferential siting, to create good spatio - temporal models. We will continue current research and seek models for temperature fields that extends their geographical domains of applicability - previous work focused the Pacific Northwest region of North America but now we will be moving into the United Kingdom to work with the UK Met Office on monitoring temperatures, since in the age of climate change, high temperatures are a health hazard. More specifically we will model exposures to extreme temperatures that take account of human behaviour, i.e. by modelling such exposures in micro-environments like car cabins or homes. But climate change is affecting not just the human species. Other research on marine mammals will continue since their numbers in some regions are being depleted for reasons not yet understood. New research on modelling their paths as a basis for determining their energy needs and how well these will be met, given the impact of warming on the food supply for these creatures.

在目前的条件下，最近预测到2100年，世界人口将继续增长到150亿，这导致人们对环境风险水平上升的担忧日益加剧。预计还会发生更多事件，例如高温事件和煤炭等化石燃料消费的增加。这一黑暗的未来预示着越来越需要有效地监测空间和时间上的环境过程，以及在空间和未来无法监测的情况下预测这些过程的准确方法，以支持通过有效的政策和监管来管理和缓解这些风险。在这个未来，统计学家将发挥重要作用，特别是在量化充斥着不确定性的方面。拟议的研究将继续申请人的长期研究方案，即时空过程监测网络的最佳设计和利用所产生的数据为空间预测和未来过程模拟这些过程。我们继续致力于算法方面的工作，以克服与选择最佳地点相关的计算挑战，同时确保在无偏见的情况下选择它们。但我们也将提供扩展理论，以解决现有监测网络出现的问题，这些网络没有以公正的方式进行选择。特别是，由于现有的网络站点有时位于优先选择的地理位置，我们将继续寻找方法，以减少以这种方式定位站点而导致的统计估计误差。这项研究还将探索一种有前景的客观方法来定位它们，一种确保它们在空间中均匀分布的方法。研究的其余部分涉及使用来自这些网络的数据，可能会对优先选址的影响进行调整，以创建良好的时空模型。我们将继续目前的研究，寻找扩大其适用地理范围的温度场模型-以前的工作重点是北美的太平洋西北部地区，但现在我们将进入英国，与英国气象局合作监测温度，因为在气候变化的时代，高温是一种健康危害。更具体地说，我们将模拟极端温度下的暴露，考虑到人类的行为，即通过模拟汽车驾驶舱或家庭等微环境中的暴露。但气候变化影响的不仅仅是人类物种。对海洋哺乳动物的其他研究将继续进行，因为在一些地区，由于尚不清楚的原因，它们的数量正在枯竭。考虑到气候变暖对这些生物的食物供应的影响，对它们的路径进行了新的研究，以此作为确定它们的能源需求以及满足这些需求的程度的基础。