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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=663491
• 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计划的最新报告强调了创建地理数据的指数增长，这是预计在未来5 - 10年内将继续进行的关键趋势。再加上这是通过自然和人为过程的自然世界的变化。因此，对人力和技术能力的需求不断扩大，以管理，处理和分析有关地球表面的地理空间信息的新来源，例如高分辨率卫星图像。处理地理信息的一个关键任务是对空间模式进行推断，并越来越多地比较空间模式。这可能是在评估有关入侵物种传播的空间显式模型的拟合度，或者评估在资源提取发生后景观是否失去了显着连接性。尽管用于检测空间数据中模式的方法已经开花，但几乎没有可用于比较环境的空间模式的工具。鉴于地理信息源的广阔景观，对模式比较算法的需求日益增长。 ******我提出了一个研究计划，该计划通过发现支持空间模式比较的指标和算法直接面临这一挑战。我的目标是为各种空间表示开发这些方法，并包括静态和动态空间模式。流媒体空间数据源本质上是动态的，对模式分析和解释构成了重大的研究挑战。我将为数据速度，音量和性质开发新的方法。案例研究将用于证明和评估环境变化检测的指标。作为我的研究计划的一部分，我将培训能够满足该行业对空间数据科学家需求不断增长的高质量人员