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This case study demonstrates the application of an unsupervised topic modeling algorithm to 7,773 English-language articles published in the Library Quarterly from 1931 to 2015. The analysis of 85 years of the journal's output follows an exploratory data analysis framework to generate novel hypotheses about the history of LIS using topic modeling, a method for identifying clusters of co-occurring words within large collections of text. The paper closely examines two topics that suggest differences in gender representation in the journal to propose and support a new hypothesis regarding the historical inclusion of gendered objects of study in LIS literature. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to analyze the relationship among measures of resource and service usage and other features of academic libraries in the USA and Canada. Design/methodology/approach – Through the use of a self-organizing map, academic library data were clustered and visualized. Analysis of the library data was conducted through the computation of a "library performance metric" that was applied to the resulting map. Findings – Two areas of high-performing academic libraries emerged on the map. One area included libraries with large numbers of resources, while another area included libraries that had low resources but gave greater numbers of presentations to groups, offered greater numbers of public service hours, and had greater numbers of staffed service points. Research limitations/implications – The metrics chosen as a measure of library performance offer only a partial picture of how libraries are being used. Future research might involve the use of a self-organizing map to cluster library data within certain parameters and the identification of high-performing libraries within these clusters. Practical implications – This study suggests that libraries can improve their performance not only by acquiring greater resources but also by putting greater emphasis on the services that they provide to their users. Originality/value – This paper demonstrates how a self-organizing map can be used in the analysis of large data sets to facilitate library comparisons. [ABSTRACT FROM AUTHOR]

Recent economic growth and in-migration have led to an interesting patchwork of rapidly growing counties in the United States. This paper identifies 299 U.S. counties that were growing at least 70% from 1970 to 1990, a rate greater than one standard deviation above the mean of all counties. Using a k-means cluster algorithm, seven distinct types of rapidly growing counties, with meaningful spatial patterns, were found. Four of these clusters were primarily urban: smaller fast-growing cities in the south, large sunbelt cities, as well as both white and blue-collar suburban counties ringing many of the more dynamic metropolitan regional centers of the country. Also, there were two predominately rural clusters: retirement counties and several counties in the Intermontane West characterized by environmental amenities and isolated growth. Lastly, we observed a distinct cluster of several Hispanic counties in the southwest primarily along the U.S.–Mexico border. We believe these seven clusters present a remarkably clear portrait of the significant forces underlying the rapid growth of counties in the United States. [Copyright &y& Elsevier]