A Multiscale Framework for Spatial Modeling in Geography

地理空间建​​模的多尺度框架

• 批准号: 0079077
• 负责人: Eric Kolaczyk
• 金额: $ 24.94万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-01 至 2004-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0079077&HistoricalAwards=false
• 关键词: Multiscale; Framework; Spatial; Modeling; Geography

Scale dependency is an inherent property of geographic phenomena, and the increasingly pressing importance of better understanding the effects of scale has been highlighted in a number of recent public forums. So-called multiscale models can be particularly appropriate for this task, that is, mathematical/statistical models in which the overall structure of an object under study is decomposed according to its component structures at different scales of spatial and/or temporal resolution. In this project, work will focus on the development and implementation of a multiscale statistical modeling framework recently introduced by the principal investigators specifically for geographic data structures. The framework underlying these models is that of a set of hierarchically defined partitions (or aggregations) of a data space. The effects of scale are captured through a fundamental decomposition (or factorization) of the data likelihood, induced by this hierarchy, into individual components of local information at all possible spatial resolutions. Upon combining these multiscale likelihoods with an appropriately defined Bayesian prior probability structure, a powerful inferential framework results. The specific aims of this project are three-fold: (i) further develop and extend the original, general modeling structure, so as to (ii) tailor it to two specific class of problems in geographical analysis, those of remote sensing and census geography, and finally (iii) produce formal tools of statistical inference for characterizing the influence of scale effects in standard tasks such as prediction, classification, knowledge discovery, and decision making.Broadly speaking, this research is aimed at fully developing an inferential statistical framework for the study of scale effects in geographic phenomena, with particular emphasis on problems in remote sensing and census geography. As such, it is expected to have implications in areas such as geographical theory, knowledge discovery and spatial data mining, and theory and methods for database generalization. The resulting framework will be sufficiently flexible and computationally efficient to allow for integration into geographic information systems (GIS), such as those arising in the context of environmental, epidemiological, and agricultural applications.

规模依赖性是地理现象的固有属性，并且在许多最近的公共论坛中强调了更好地理解规模影响的越来越紧迫的重要性。 所谓的多尺度模型可能特别适合此任务，即数学/统计模型，其中根据其在空间和/或时间分辨率的不同尺度上根据其组件结构分解了研究对象的总体结构。 在该项目中，工作将重点关注主要研究人员最近针对地理数据结构的多尺度统计建模框架的开发和实施。 这些模型的框架是数据空间的一组层次定义的分区（或聚集）。 量表的效果是通过该层次结构引起的数据可能性的基本分解（或分解）捕获的，这些分解可能在所有可能的空间分辨率下将局部信息的各个组成部分捕获。 将这些多尺度可能性与适当定义的贝叶斯先前概率结构相结合后，有强大的推论框架结果。 该项目的具体目的是三个方面：（i）进一步发展和扩展原始的一般建模结构，以便（ii）将其量身定制到两种特定类别的问题中，这些问题的地理分析，遥感和人口普查和人口普查的地理位置以及（iii）的形式工具（iii）产生了统计工具，以表征量表的范围，诸如量表的影响，并在诸如级别上的影响，并在诸如范围内的范围内效果，并构成了批判性，并确定了批判性，并确定了这些范围，并构成了批判性，并确定了这些范围，并构成了批准，并确定了批判性的研究，并构成了批判性的效果。研究地理现象中规模效应的统计框架，特别强调了遥感和人口普查地理学问题。 因此，预计它将在地理理论，知识发现和空间数据挖掘以及数据库概括的理论和方法等领域具有影响。 最终的框架将具有足够的灵活性且在计算上有效，以允许将其集成到地理信息系统（GIS）中，例如在环境，流行病学和农业应用的背景下产生的框架。