Blaunet: An R-based graphical user interface package to analyze Blau space

• Main Authors: GENKIN, Michael, WANG, Cheng, BERRY, George, BRASHEARS, Matthew E.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Demography, Population, and Ecology; Sociology
• Online Access: https://ink.library.smu.edu.sg/soss_research/2696; https://ink.library.smu.edu.sg/context/soss_research/article/3953/viewcontent/journal.pone.0204990.pdf
• Institution: Singapore Management University

McPherson’s Blau space and affiliation ecology model is a powerful tool for analyzing the ecological competition among social entities, such as organizations, along a combination of sociodemographic characteristics of their members. In this paper we introduce the R-based Graphical User Interface (GUI) package Blaunet, an integrated set of tools to calculate, visualize, and analyze the statuses of individuals and social entities in Blau space, parameterized by multiple sociodemographic traits as dimensions. The package is able to calculate the Blau statuses at the nodal, dyadic, and meso levels based on three types of information: sociodemographic characteristics, group affiliations (e.g., membership in groups/organizations), and network ties. To facilitate this, Blaunet has the following five main capabilities, it can: 1) identify a list of possible salient dimensions; 2) calculate, plot, and analyze niches for social entities by measuring the social distance along the salient dimensions between individuals affiliated with them; 3) generate Blau bubbles for individuals, thereby allowing the study of interpersonal influence of similar others even with limited or no network information; 4) capture niche dynamics cross-sectionally by calculating the intensity of exploitation from the carrying capacity and the membership rate; and 5) analyze the niche movement longitudinally by estimating the predicted niche movement equations. We illustrate these capabilities of Blaunet with example datasets.