Spatial pattern comparison metrics for detecting environmental change

联合国全球地理空间信息管理倡议最近的一份报告强调，地理数据的创建呈指数级增长，这是一个关键趋势，预计将在今后5至10年继续下去。与此同时，自然世界通过自然和人为过程发生的变化越来越多。因此，越来越需要人力和技术能力来管理、处理和分析有关地球表面的地理空间信息的新来源，如高分辨率卫星图像。处理地理信息的一项关键任务是能够对空间模式进行推断，并越来越多地比较空间模式。这可能是在评估一个空间明确的模型的入侵物种的传播，或评估是否已经失去了重要的资源提取后，景观连接的适合。虽然用于检测空间数据中的模式的方法已经蓬勃发展，但用于比较环境空间模式的工具却非常少。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度量和算法来直接面对这一挑战。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。我将开发出对数据速度、数据量和数据性质具有鲁棒性的新方法。案例研究将用于演示和评估环境变化检测的指标。作为我研究计划的一部分，我将培养高素质的人才，他们能够满足业界对空间数据科学家日益增长的需求

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