This article attempts to identify and separate the role of spatial “context” in shaping voter preferences from the role of other socioeconomic determinants. It does this by calibrating a multiscale geographically weighted regression (MGWR) model of county-level data on percentages voting for the Democratic Party in the 2016 U.S. presidential election. This model yields information on both the spatially heterogeneous nature of the determinants of voter preferences and the geographical scale over which the effects of these determinants are relatively stable. The article, perhaps for the first time, is able to quantify the relative effects of context versus other effects on voter preferences and is able to demonstrate what would have happened in the 2016 election in two scenarios: (1) if context were irrelevant and (2) if every county had exactly the same population composition. In addition, the article sheds light on the nature of the determinants of voter choice in the 2016 U.S. presidential election and presents strong evidence that these determinants have spatially varying impacts on voter preferences.

本文试图将空间“背景”在塑造选民偏好方面的作用与其他社会经济决定因素的作用区别开来。它通过校准县级数据的多尺度地理加权回归（MGWR）模型来确定2016年美国总统选举中民主党的投票百分比。该模型产生有关选民偏好决定因素的空间异质性以及这些决定因素的影响相对稳定的地理范围的信息。这篇文章也许是第一次，它能够量化背景相对于其他因素对选民偏好的相对影响，并且能够在两种情况下证明2016年大选会发生的情况：（1）如果上下文无关紧要，（2）每个县的人口构成是否完全相同。此外，本文阐明了2016年美国总统大选投票人选择决定因素的性质，并提供了有力证据表明这些决定因素在空间上对选民偏好产生影响。