Analyzing the Effects of Spatial Autocorrelation in Geospatial Databases

国家科学基础地理空间科学(GSS)进展这个研究项目将通过分析空间自相关对地理空间数据建模和分析的潜在影响，更好地理解自然资源的空间格局。研究人员将通过量化空间自相关性对广泛的环境预测-响应变量的影响，为改进地理空间建模程序提供新的见解。通过对多种自然资源与不同生态系统中的一系列环境因素进行分析和建模，研究人员将在多个尺度上确定土壤、水、生物多样性和环境因素之间的相互依存关系。项目成果将为政府机构和政策制定者提供新的信息和方法，以改进对高需求但稀缺的自然资源的预测和管理。这一空间方法框架的结果将有助于在全球范围内研究其他生态系统，并可能成为建立跨不同生态系统的综合管理建议平台的跳板。为了对空间现象进行建模和预测，空间科学家依赖于包含物理和生物资源信息的多个地理编码数据集。然而，由于生物物理变量之间的相互依存关系，确定与这种现象相关的各种空间过程和预测-响应变量之间的关系是具有挑战性的。研究人员将把他们的分析重点放在空间自相关对自然资源跨景观分布的建模和解释方面的潜在影响。他们将分析代表各种地理和生物物理梯度的多个全球空间数据集，并将制定一个框架，在多个尺度上更好地理解不同但相关的地理现象。空间特征向量映射和环境建模将用于分析预测变量和响应变量之间的复杂关系。空间自相关影响的量化将有助于理解各种自然资源和地理现象的潜在网络和空间过程。

NATIONAL SCIENCE FOUNDATIONGEOGRAPHY SPATIAL SCIENCES (GSS) PROGRAMABSTRACTThis research project will provide a better understanding of the spatial patterns of natural resources by analyzing the potential effects of spatial autocorrelation on the modeling and analyses of geospatial data. The investigators will provide new insights for improving geospatial modeling procedures by quantifying the influences of spatial autocorrelation on a wide range of environmental predictor-response variables. By analyzing and modeling multiple natural resources as they relate to a range of environmental factors across different ecosystems, the investigators will identify the interdependent relationships among soil, water, biodiversity, and environmental factors at multiple scales. Project findings will inform government agencies and policy makers by providing new information and approaches for improved prediction and management for high-demand but scarce natural resources. Outcomes from this spatial methodological framework will be useful in examining other ecological systems globally and potentially will serve as a springboard for establishing platforms for comprehensive management recommendations across diverse ecosystems.In order to model and predict spatial phenomena, spatial scientists rely on multiple geocoded data sets that contain information about physical and biological resources. Because of the interdependency among biophysical variables however, it is challenging to determine the relationships among the various the spatial processes and the predictor-response variables associated such phenomena. The investigators will focus their analyses on the potential effects of spatial autocorrelation on the modeling and interpretation of the distribution of natural resources across landscapes. They will analyze multiple global spatial data sets representing a variety of geographic and biophysical gradients, and they will develop a framework to better understand the different yet related geographic phenomena at multiple scales. Spatial eigenvector mapping and environmental modeling will be used to analyze the complex relationships among predictor and response variables. The quantification of the influences of spatial autocorrelation will be catalytic for the understanding of potential networks and spatial processes across a broad spectrum of natural resources and geographic phenomena.