Using Data Visualization to Examine an Academic Library Collection

Abstract

The authors generated data visualizations to compare sections of the library book collection, expenditures in those areas, student enrollment in majors and minors, and number of courses. The visualizations resulting from the entered data provide an excellent starting point for conversations about possible imbalances in the collection and point to areas that are either more developed or less developed than is needed to support the major and minor areas of study at the university. The methodology used should offer a template to follow for others wishing to examine their collection and may prove valuable for adjusting expenditures, suggesting service opportunities or for marketing pieces of the collection that had been hidden before graphical analysis. athering and displaying data in visual representations helps inform the brain faster and more effectively than reading textual lines of information. "One picture is worth a thousand words" is the cliché we use to describe this phenomenon. The classic works of both Edward Tufte and of Information Science professor and scientist Katy Börner provide beautiful examples of what excellent design principles applied to information and data may graphically reveal. Visualizations provide "overviews about general patterns and trends" and allow discovery of "hidden structures." 1 Edward Tufte, professor emeritus of Yale University and a well-known advocate and creator of elegant graphical display of complex data, explains that "graphics reveal data." Tufte asserts that the most "effective way to describe, explore, and summarize a set of numbers is to look at pictures of those numbers." 2 Used in libraries, information gathered into graphical impressions can reveal patterns hidden in lines of text. Xu et al. remind us that, "[i]n the context of large-scale and heterogeneous collections, the different layers of information cannot be easily comprehended if presented linearly and sequentially, and there is a risk of getting buried in details or lost in generalities." 3

Supporting Information Visualization Research in an Academic Library

“…Examples include "How Data Visualization Supports Academic Library Assessment: Three Examples from The Ohio State University Libraries using Tableau," 13 Desktop to Support Library Decision-making," 14 and "Using Data Visualization to Examine an Academic Library Collection." 15 To better understand what LIS information visualization research has been done outside of the most influential publications, this paper seeks to summarize all published, LIS-related, information visualization research, highlighting the most popular research topics and the most frequently used visualization methods.…”

Data Visualization for Collection Analysis

“…Murphy (2013) and Datig and Whiting (2018) have found success using Tableau to track data on library programs, spaces, and services in order to inform future decision making. Finch and Flenner (2016) and Walton and Bunderson (2020) used collection analysis and visualization to identify and track budget and collection development trends over time, while Haren (2014), Eaton (2017), and Wissel and DeLuca (2018) all used visualization methods to explore sets and subsets of data within the library's catalog. Even though many of today's ILSs have more sophisticated analytic capabilities than their predecessors, they often cannot provide the context necessary to make the data truly valuable.…”

“…If ease of use and reproducibility are more important, then visualization using simpler tools like Microsoft Excel or Tableau may be a better fit. Both Haren (2014) and Finch and Flenner (2016) found tools like these to be most useful in their collection analysis and mapping due to their flexibility and relatively low learning curve.…”

A case study of Singapore Management University Libraries: Adopting a mixed methods approach towards collection evaluation

“…By providing relevant and sound data, it allows librarians to ascertain which resources should be acquired to optimize the budget, so that effective collections can be built (Finch & Flenner, 2017), thereby eliminating the risk of acquisition decisions being uninformed and subjective (Finch & Flenner, 2017).…”