Psychologists have become increasingly interested in the geographical organization of psychological phenomena. Such studies typically seek to identify geographical variation in psychological characteristics and examine the causes and consequences of that variation. Geo-psychological research offers unique advantages, such as a wide variety of easily obtainable behavioral outcomes. However, studies at the geographically aggregate level also come with unique challenges that require psychologists to work with unfamiliar data formats, sources, measures, and statistical problems. The present article aims to present psychologists with a methodological roadmap that equips them with basic analytical techniques for geographical analysis. Across five sections, we provide a step-by-step tutorial and walk readers through a full geo-psychological research project. We provide guidance for (a) choosing an appropriate geographical level and aggregating individual data, (b) spatializing data and mapping geographical distributions, (c) creating and managing spatial weights matrices, (d) assessing geographical clustering and identifying distributional patterns, and (e) regressing spatial data using spatial regression models. Throughout the tutorial, we alternate between explanatory sections that feature in-depth background information and hands-on sections that use real data to demonstrate the practical implementation of each step in R. The full R code and all data used in this demonstration are available from the OSF project page accompanying this article.

中文翻译：

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心理学家的空间分析：如何使用个人数据进行地理聚合水平的研究。

心理学家对心理现象的地理组织越来越感兴趣。此类研究通常旨在确定心理特征的地理差异，并检查该差异的原因和后果。地理心理学研究具有独特的优势，例如各种容易获得的行为结果。然而，地理总体层面的研究也面临着独特的挑战，要求心理学家处理不熟悉的数据格式、来源、测量和统计问题。本文旨在为心理学家提供一个方法路线图，为他们提供地理分析的基本分析技术。我们通过五个部分提供了分步教程，并引导读者完成完整的地球心理学研究项目。我们提供以下方面的指导：(a) 选择适当的地理级别并聚合单个数据，(b) 空间化数据并绘制地理分布图，(c) 创建和管理空间权重矩阵，(d) 评估地理聚类并识别分布模式，以及（ e) 使用空间回归模型回归空间数据。在整个教程中，我们交替使用具有深入背景信息的解释部分和使用真实数据演示 R 中每个步骤的实际实现的实践部分。完整的 R 代码和本演示中使用的所有数据可从本文附带的 OSF 项目页面。