Stochastic Models and Statistical Methodology for Marked Spatio-Temporal Point Processes with Applications to Wildland Fire Management

目标：1)开发影响野火科学和管理的统计工具、方法和模型；2)用现代统计方法训练HQP，强调协作、跨学科、基于团队的数据科学；3)向科学界传播统计学、环境学、野火科学和管理方面的进展；4)通过提供概念框架、软件和决策支持系统工具，将进步转移到火灾管理。方法：我的研究动机是对大型、复杂的标记时空点过程和火灾天气数据集的科学研究。主题包括：***数据可视化：将开发用于时空点模式及其标记的可视化工具来探索关键问题。(例如：燃烧是如何降低着火风险的？这种风险又是如何消散的？火的寿命在空间和时间上是如何变化的？点火的聚类是由于热点还是随机聚类？如何在决策支持工具中最好地可视化基于模型的信息？)***火灾发生：将开发存在/不存在、计数和大型逃逸火灾的精细尺度火灾发生时空预测模型。新的设计方案将创建更相关的暴露度量，并导致增益统计效率的提高。方法学将被创建，以开发大空间范围的模型。检测效率的变化将被量化，并用于在监测气候变化影响时减少混杂效应。***火灾持续时间：将开发单个火灾生命周期模型和大型景观火灾生存时间的空间格局模型。这些将描述火灾生命周期的关键时期（例如，从发现到报告的时间，初始攻击逃逸时间，抑制时间）。将为历史火灾编制具有时变协变量的大型数据集，并为火灾管理创建预测未来时空寿命的新模型。***随机模型：将开发时空点过程聚类模型来模拟火灾到达。标记将与这些和/或其他点火模型耦合，以模拟火灾制度特征，例如燃烧面积或火灾负荷（景观上活跃的火灾数量）在时空上的变化。***决策支持系统：上述主题的组件将被整合到基于模型的决策支持工具中，这将更好地为消防管理提供信息。***影响：提议的组成部分将导致重大进展，进一步科学地了解野火。学生将在一个跨学科、协作的团队环境中接受现代统计学方面的培训。这项研究将影响火灾科学和火灾管理、统计和环境计量、运筹学，并产生影响保险和自然资源部门的技术。*****

OBJECTIVES: 1) Develop statistical tools, methodology and models that impact wildland fire science and management; 2) Train HQP in modern statistical methods emphasizing collaborative, interdisciplinary, team-based data science; 3) Disseminate advancements in statistics, environmetrics, wildland fire science and management to the scientific community; 4) Transfer advancements to fire management by providing conceptual frameworks, software and decision support systems tools. ***APPROACH: My research is motivated by the scientific study of large, complex marked spatio-temporal point-process and fire-weather data sets. Topics include:***Data Visualization: Visualization tools for spatio-temporal point patterns and their marks will be developed to explore key questions. (Eg: How does a burn reduce ignition risk and how does this dissipate? How do fire lifetimes vary spatially and temporally? Is the clustering of ignitions due to hot spots or stochastic clustering? How can one best visualize model-based information in decision support tools?)***Fire Occurrence: Fine-scale spatio-temporal fire occurrence prediction models for the presence/absence, for counts, and for large escaped fires will be developed. New design schemes will create more relevant measures of exposure and lead to gains in gain statistical efficiency. Methodology will be created to develop models over large spatial extents. Changes to detection effectiveness will be quantified and used to reduce confounding effects when monitoring impacts of climate change.***Fire Duration: Models for individual fire lifetimes and for spatial patterns in fire survival times over large landscapes will be developed. These will characterize the key epochs of a fire's lifetime (eg, time from detection to report, initial attack getaway time, suppression time). A large data set with time-varying covariates for historical fires will be compiled and novel models that forecast future lifetimes over space-time will be created for fire management.***Stochastic Models: Spatio-temporal point process cluster models will be developed to model fire arrivals. Marks will be coupled to these and/or the other ignition models to model fire regime characteristics, such as area burned or the fire-load (the number of fires active on the landscape) over space-time. ***Decision Support Systems: Components from the above set of topics will be incorporated into model-based decision support tools which will better inform fire management. ***IMPACTS: The proposed components will lead to significant advances which further the scientific understanding of wildland fires. Students will be trained in modern statistics in an interdisciplinary, collaborative team setting. This research will impact fire science and fire management, statistics and environmetrics, operations research, and produce technology that impacts insurance and natural resources sectors.*****