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Making spatial analysis operational: Commands for generating spatial-effect variables in monadic and dyadic data

机译:使空间分析可操作:用于在二元和二元数据中生成空间效应变量的命令

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摘要

Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent, or actor, and even more true for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents, or actors. Dyadic data offer more complex ways of modeling spatial-effect variables than do monadic data. The commands described in this article facilitate spatial analysis by providing an easy tool for generating, with one command line, spatial-effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.
机译:只要一个分析单元的预期效用受到其他分析单元做出的决策或行为的影响,就会存在空间依赖性。在社会关系和互动中,空间依赖性无处不在。但是,令人惊讶的是,很少有社会科学研究能够说明空间依赖性。这适用于研究人员使用单子数据的环境,其中分析的单位是单个单位,代理或参与者,而对于二进位数据设置的情况更是如此,其中分析的单位是表示相互作用或相互作用的对或对偶。两个个体单位,代理或参与者之间的关系。与二元数据相比,二元数据提供了更复杂的空间效应变量建模方法。本文中介绍的命令通过提供一种简单的工具来帮助进行空间分析,该工具可通过一个命令行生成用于单峰传染以及二进位数据中所有可能的传染形式的空间效应变量。

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