Data Complexity and Spatial Scaling: Prediction Accuracy and Implications for Emerging Landscape Paradigms

这项研究将为不同地理数据集的合并和聚合以及如何在这些过程中将其数据质量损失降至最低提供新的知识。当数据集在不同的空间尺度上合并时，通常被称为可修改面积单位问题(MAUP)的统计偏差会发生，并受到数据集的组成和配置的影响。研究人员将研究如何克服这些偏差，以便使数据集能够准确地缩放到不同的分辨率。通过开发使用成分和构型异质性测量的方法，研究人员将提供关于如何评估数据集的潜力的新见解，该数据集将被精确缩放到更粗和更细的分辨率，以促进整合。研究人员将提高人们对恰当和正确使用生态、社会和地理数据集进行综合分析的准确性问题的认识，她将提供解决方案，以促进更好的跨学科整合，以及通过学习模块让年轻科学家接触这些问题的方法。该项目将通过借鉴地理学和景观生态学的平行理论来推动空间科学的发展。这项研究将试图回答三个核心问题：(1)数据复杂性和空间异质性如何影响与MAUP相关的统计偏差？(2)数据简化/复杂性标准能否改善预测？(3)不同的景观范式对异质性变化的反应是否不同？研究人员将使用多种方法来回答这些问题，包括景观生态领域地表范式的创新，遥感光谱分解的进展，以及空间格局度量的尺度相关性。研究结果将导致确定多种分辨率的不同形式的数据简化和复杂性聚合方法；开发一种预测缩小尺度精度的快速评估方法；并为使用新出现的连续地表景观分析范例建立统计基础。这些成果有可能改变环境、社会和经济学科的科学家如何处理各种类型的地理空间数据的聚合和缩放。

This research will contribute new knowledge regarding the merger and aggregation of diverse geographic datasets and how their data quality loss can be minimized during these processes. As datasets are merged across varying spatial scales, statistical biases, commonly known as the Modifiable Areal Unit Problem (MAUP) occur and are impacted by the composition and configuration of the datasets. The investigator will study how these biases can be overcome in order to allow datasets to be accurately scaled to different resolutions. By developing methods that use measures of compositional and configurational heterogeneity, the researcher will provide new insights regarding how to assess the potential of a dataset to be accurately scaled to coarser and finer resolutions in order to foster integration. The investigator will raise awareness of accuracy issues surrounding appropriate and correct use of ecological, social, and geographical datasets for integrated analysis, and she will offer solutions to promote better interdisciplinary integration as well as methods for exposing young scientists to these issues through learning modules.This project will advance the spatial sciences by drawing on parallel theories in geography and landscape ecology. The research will seek to answer three core questions: (1) How do data complexity and spatial heterogeneity impact statistical biases associated with MAUP? (2) Can standards for data reduction/complexity improve prediction? (3) Do various landscape paradigms respond differently to changing heterogeneity? The investigator will use multiple methods, including innovations in surface paradigms in the field of landscape ecology, advances in remote sensing spectral unmixing, and scale-dependency of spatial pattern metrics to answer these questions. The research findings will result in identification of different forms of data reduction and complexity aggregation methods across multiple resolutions; development of a rapid assessment method for predicting downscaling accuracy; and establishment of a statistical basis for using the emerging continuous surface paradigm of landscape analyses. These outcomes have the potential to transform how scientists across environmental, social, and economic disciplines approach aggregation and scaling of various types of geospatial data.