Spatial Association Statistics

空间分析及相关领域的最新进展 地理信息系统等方法， 指出需要更好地了解统计数据， 空间相关性和自相关性。 失败 考虑空间自相关可能导致严重的 模型解释的错误。 空间建模不仅要 考虑到依赖结构和异方差，但 它还必须评估空间尺度的影响。 本研究 着重于一个新的家庭的空间关联统计，G，由 不同空间分布变量的实验 特别是在小样本的情况下。 最终 目标是将G统计纳入地理 信息系统，以探索空间的影响， 依赖性和空间异质性对各种分析 模型 地理学家、地质学家和计量经济学家等， 使用了各种统计技术来衡量 空间关联 该研究项目将增加一个更 空间分布分析的强大工具，因为 在各种空间尺度上的直接应用。 G统计数据纳入地理信息 系统将允许更严格的空间分析 分布在不同的空间聚集水平，因此， 将它们的效用扩展到许多不同的问题。

Recent advances in spatial analysis and related methodologies such as geographical information systems have pointed up the need for a better understanding of the statistical properties of spatial correlation and autocorrelation. Failure to take spatial autocorrelation into account can lead to serious errors in model interpretation. Spatial modeling must not only take account of dependence structure and heteroscedasticity, but it must also assess the effects of spatial scale. This research focuses on a new family of spatial association statistics, G, by experimentation on spatially distributed variables in different situations, especially with small samples. The ultimate objective is to incorporate the G statistics into geographical information systems in order to explore the influence of spatial dependence and spatial heterogeneity on a variety of analytical models. Geographers, geologists and econometricians, among others, have used a variety of statistical techniques for measuring spatial association. This research project will add a more powerful tool for the analysis of spatial distributions, because of its direct application at a variety of spatial scales. Integration of the G statistics into geographical information systems will permit more rigorous analysis of spatial distributions at different levels of spatial aggregation, thus extending their utility to many different problems.