We present the results of a comprehensive multi-pass analysis of visualization paper keywords supplied by authors for their papers published in the IEEE Visualization conference series (now called IEEE VIS) between 1990–2015. From this analysis we derived a set of visualization topics that we discuss in the context of the current taxonomy that is used to categorize papers and assign reviewers in the IEEE VIS reviewing process. We point out missing and overemphasized topics in the current taxonomy and start a discussion on the importance of establishing common visualization terminology. Our analysis of research topics in visualization can, thus, serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an online query tool ( http: keyvis.org ) that allows visualization researchers to easily browse the 3952 keywords used for IEEE VIS papers since 1990 to find related work or make informed keyword choices.
Co-word analysis has been used in different research areas, , polymer chemistry @cite_28 , acid rain research @cite_3 , or education @cite_7 . Others further restricted the scope of the literature to specific countries, such as Hu and Liu al's @cite_38 @cite_20 co-word analysis on library and information science in China. The closest co-word analysis studies to our work are Coulter al's @cite_8 work on the software engineering community, Hoonlor al's @cite_5 general investigation of the computer science literature, and, most recently, Liu al's @cite_23 analysis of the human-computer interaction literature. Liu al examined papers of the ACM CHI conference from 1994--2013, identified research themes and their evolution, and classified individual keywords as popular, core, or backbone topics. We employ similar approaches as used in particular in Liu al's work. Naturally, however, we differ as our focus is on a different research community, visualization, with different keywords, trends, and patterns and and a different historical evolution.
@cite_38 @cite_7 @cite_8 @cite_28 @cite_3 @cite_23 @cite_5 @cite_20
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This study aims to reveal the intellectual structure of Library and Information Science (LIS) in China during the period 2008---2012 utilizing co-word analysis. The status and trends of LIS in China are achieved by measuring the correlation coefficient of selected keywords extracted from relevant journals in the Chinese Journal Full-Text Database. In co-word analysis, multivariate statistical analysis and social network analysis are applied to obtain 13 clusters of keywords, a two-dimensional map, centrality and density of clusters, a strategic diagram and a relation network. Based on these results, the following conclusions can be drawn: (i) LIS in China has some established and well-developed research topics; (ii) a few emerging topics have a great potential for development; and (iii), the research topics in this LIS field are largely decentralized as a whole, where there are many marginal and immature topics. In this paper the adequacy of the co-word method for mapping the structure of scientific inquiry is explored. Co-word analysis of both the keywords and the titles of a set of papers in acidification research' is undertaken and the results are found to be comparable, though the keyword-derived results provide greater detail. This strongly suggests that keyword indexing doest not, as has sometimes been claimed, distort coword findings. It also points to differences between titles (which often emphasize the supposed originality of an article) and keywords (which tend to show the relationship between the paper and other publications). The paper also explores important differences between the methodological assumptions that underlie the Paris Keele co-word clustering algorithms and the factor analysis method for creating clusters. This study employs hierarchical cluster analysis, strategic diagrams and network analysis to map and visualize the intellectual landscape of the CHI conference on Human Computer Interaction through the use of co-word analysis. The study quantifies and describes the thematic evolution of the field based on a total of 3152 CHI articles and their associated 16035 keywords published between 1994 and 2013. The analysis is conducted for two time periods (1994-2003, 2004-2013) and a comparison between them highlights the underlying trends in our community. More significantly, this study identifies the evolution of major themes in the discipline, and highlights individual topics as popular, core, or backbone research topics within HCI. This empirical research demonstrates the effectiveness of content analysis to map the research literature of the software engineering discipline. The results suggest that certain research themes in software engineering have remained constant, but with changing thrusts. Other themes have arisen, matured, and then faded as major research topics, while still others seem transient or immature. Co-word analysis is the specific technique used. This methodology identifies associations among publication descriptors (indexing terms) from the ACM Computing Classification System and produces networks of descriptors that reveal these underlying patterns. This methodology is applicable to other domains with a supporting corpus of textual data. While this study utilizes index terms from a fixed taxonomy, that restriction is not inherent; the descriptors can be generated from the corpus. Hence, co-word analysis and the supporting software tools employed here can provide unique insights into any discipline's evolution.