The concept of ‘spatial scale’, or simply ‘scale’ is implicit in any discussion of global versus local models. The raison d’etre of local models is that a global scale (where here ‘global’ simply refers to all locations within a predefined area of interest) might be the incorrect scale at which to undertake any analysis of spatial processes; the alternative being a local scale (where here ‘local’ refers to individual locations). Here we explore two well-known scale issues in the context of local modeling: the modifiable areal unit problem (MAUP) and Simpson’s paradox. In doing so, we highlight that scale effects play two very different roles in any consideration of local versus global modeling. First, we examine the sensitivity of global and local models to the MAUP and show how the effects of the MAUP in global models are a function of the degree to which processes vary over space. This generates a new insight into the MAUP: it results from the properties of processes rather than the properties of data. Then we highlight the extreme differences that can result when calibrating global and local models and how Simpson’s paradox can arise in this context. In the examination of the MAUP, scale is treated as a measure of the degree to which data are aggregated prior to any form of modeling; in the study of Simpson’s paradox, scale refers to the geographical entity for which a model is calibrated.

“空间尺度”或简称“尺度”的概念隐含在任何关于全局与局部模型的讨论中。存在的理由局部模型的一个特点是全球尺度（这里的“全球”只是指预先定义的感兴趣区域内的所有位置）可能是对空间过程进行任何分析的不正确尺度；另一种选择是本地规模（这里的“本地”是指各个位置）。在这里，我们在局部建模的背景下探讨两个众所周知的尺度问题：可修改面积单位问题 (MAUP) 和辛普森悖论。在这样做的过程中，我们强调规模效应在考虑本地与全局建模时扮演着两个非常不同的角色。首先，我们检查了全局和局部模型对 MAUP 的敏感性，并展示了 MAUP 在全局模型中的影响如何随空间变化的程度而变化。这产生了对 MAUP 的新见解：它源于过程而不是数据的属性。然后我们强调在校准全局和局部模型时可能导致的极端差异，以及辛普森悖论如何在这种情况下出现。在 MAUP 的检查中，规模被视为在任何形式的建模之前数据聚合程度的衡量标准；在辛普森悖论的研究中，尺度是指模型被校准的地理实体。