Andersen Mark
Department of Zoology NJ-15, University of Washington, 98195, Seattle, WA, USA.
Oecologia. 1992 Aug;91(1):134-140. doi: 10.1007/BF00317252.
In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.
在本文中，我介绍并讨论了一些用于分析单变量和双变量空间点模式数据的方法。生态学中此类数据的示例包括已绘制地图的野外样地中生物的x - y坐标。我通过对美国华盛顿州圣海伦斯山已绘制地图的野外样地数据进行分析来说明这些方法。我所强调的统计方法是依赖于生物之间距离分析的图形方法。使用蒙特卡罗方法可以轻松地对这类方法进行假设检验，我也会对此进行讨论。对于单变量和双变量分析，我发现诸如K函数图之类的二阶方法通常比一阶方法（即QQ图）更可取。然而，对于多变量分析，这些二阶方法比一阶分析对小样本量更敏感。
