Spatial pattern comparison metrics for detecting environmental change

用于检测环境变化的空间模式比较指标

• 批准号: RGPIN-2016-06140
• 负责人: Robertson, Colin
• 金额: $ 1.75万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679990
• 关键词: Spatial; pattern; comparison; metrics; detecting

A recent report by the United Nations Initiative on Global Geospatial Information Management highlighted exponential growth in the creation of geographic data as a key trend that is expected to continue over the next 5-10 years. Coupled with this is increasing transformation of the natural world, through both natural and anthropogenic processes. There is therefore expanding need for human and technical capacity to manage, process, and analyze new sources of geospatial information about the earth's surface, such as high-resolution satellite imagery. A key task in handling geographic information is the ability to make inferences about spatial patterns, and increasingly, to compare spatial patterns. This might be in evaluating the fit of a spatially explicit model about the spread of an invasive species, or evaluating whether a landscape has lost significant connectivity after resource extraction has occured. While methods for detecting patterns in spatial data have blossomed, there are decidedly few tools available for comparing spatial patterns about the environment. Given the vast landscape of geographical information sources, there is a growing need for pattern comparison algorithms. ******I propose a research program that directly confronts this challenge through the discovery of metrics and algorithms that support the comparison of spatial patterns. I aim to develop these approaches for a variety of spatial representations, and include both static and dynamic spatial patterns. Streaming spatial data sources are dynamic in nature, and pose a significant research challenge for pattern analysis and interpretation. I will develop new methods robust to data velocity, volume, and nature. Case studies will be used to demonstrate and evaluate the metrics for environmental change detection. As part of my research program, I will train high quality personnel who can meet the industry's growing demand for spatial data scientists

联合国全球地理空间信息管理倡议最近的一份报告强调，地理数据创建的指数增长是一个关键趋势，预计将在未来 5-10 年持续下去。与此同时，自然世界通过自然和人为过程不断发生转变。因此，对管理、处理和分析地球表面地理空间信息新来源（例如高分辨率卫星图像）的人力和技术能力的需求不断扩大。处理地理信息的一项关键任务是能够推断空间模式，并且越来越多地比较空间模式。这可能是在评估关于入侵物种传播的空间明确模型的拟合度，或者评估在资源开采发生后景观是否失去了显着的连通性。虽然检测空间数据模式的方法已经蓬勃发展，但可用于比较环境空间模式的工具却很少。鉴于地理信息源的广阔前景，对模式比较算法的需求日益增长。 ******我提出了一个研究计划，通过发现支持空间模式比较的指标和算法来直接应对这一挑战。我的目标是为各种空间表示开发这些方法，包括静态和动态空间模式。流空间数据源本质上是动态的，对模式分析和解释提出了重大的研究挑战。我将开发对数据速度、容量和性质而言稳健的新方法。案例研究将用于演示和评估环境变化检测的指标。作为我的研究计划的一部分，我将培养高素质的人才，以满足行业对空间数据科学家不断增长的需求