Uncovering Relationships Between Geo-Statistical and Spatial Autoregressive Modelling

揭示地理统计和空间自回归建模之间的关系

• 批准号: 9507855
• 负责人: Daniel Griffith
• 金额: $ 13.88万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-01 至 1998-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9507855&HistoricalAwards=false
• 关键词: Uncovering; Relationships; Between; Geo; Statistical

The investigator undertakes to complete establishing connections between two separate contemporary areas of statistics, namely geo- and spatial statistics. These connections are developed (1) by analyzing popular scientific models in both of these sub-fields; (2) by evaluating these models within the context of the sizes and shapes of geographic regions to which they are applied; (3) by studying the role geographic direction plays in preserving or altering these connections; and (4) by investigating properties of these models for geographic maps upon which similar or markedly different classificatory color codings tend to cluster. Much of the theoretical research will be pursued with the use of computers. Selected U.S. EPA Environmental Monitoring and Assessment Program (EMAP) data already obtained for the Chesapeake and Delaware estuaries and the northeast forest inventory will be used for empirical analysis and demonstration purposes. The practical importance of this proposed research arises within the U.S. EPA EMAP project, a major national endeavor in which statistics are being applied to environmental issues as well as monitoring and analysis undertakings. Articulation of a common foundation for geo- and spatial statistics will better enable consistent statistical application and analysis of the massive amounts of geo-referenced, or locationally tagged, EMAP data becoming available. These data currently are being collected and geo-referenced in terms of a network of approximately 12,600 hexagons overlayed on the continental United States.

研究者致力于建立两个独立的当代统计领域，即地理统计和空间统计之间的联系。这些联系是(1)通过分析这两个子领域的流行科学模型来发展的；(2)在这些模型所应用的地理区域的大小和形状的背景下评估这些模型；(3)研究地理方向在保持或改变这些联系中的作用；(4)通过研究这些地理地图模型的属性，这些模型倾向于聚集相似或明显不同的分类颜色编码。许多理论研究将借助计算机进行。美国环保局环境监测和评估计划（EMAP）已经获得的切萨皮克和特拉华河口以及东北森林清单数据将用于实证分析和演示目的。这项拟议研究的实际重要性体现在美国环境保护署EMAP项目中，这是一项主要的国家努力，将统计数据应用于环境问题以及监测和分析工作。地理和空间统计的共同基础的衔接将更好地实现对大量地理参考或位置标记的EMAP数据的一致统计应用和分析。目前正在收集这些数据，并根据覆盖在美国大陆上的大约12,600个六边形的网络进行地理参考。