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Journal of Data and Information Science  2017, Vol. 2 Issue (2): 1-40    DOI: 10.1515/jdis-2017-0006
Expert Review     
Science Mapping: A Systematic Review of the Literature
Chen Chaomei()
College of Computing and Informatics, Drexel University, Philadelphia, PA 19104-2875, USA
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Abstract  

Purpose: We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes: First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.

Design/methodology/approach: We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain’s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.

Findings: The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages: conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.

Research limitations: The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately. More in-depth analyses of each specialty would be more revealing by incorporating additional methods such as citation context analysis and studies of other aspects of scholarly publications.

Practical implications: The underlying analytic process of science mapping serves many practical needs, notably bibliometric mapping, knowledge domain visualization, and visualization of scientific literature. In order to master such a complex process of science mapping, researchers often need to develop a diverse set of skills and knowledge that may span multiple disciplines. The approach demonstrated in this article provides a generic method for conducting a systematic review.

Originality/value: Incorporating the evolutionary stages of a specialty into the visual analytic study of a research domain is innovative. It provides a systematic methodology for researchers to achieve a good understanding of how scientific fields evolve, to recognize potentially insightful patterns from visually encoded signs, and to synthesize various information so as to capture the state of the art of the domain.



Key wordsScience mapping      Knowledge domain visualization      Domain analysis      Systematic review      CiteSpace     
Published: 25 February 2017
Cite this article:

Chen Chaomei. Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2017, 2(2): 1-40.

URL:

http://manu47.magtech.com.cn/Jwk3_jdis/10.1515/jdis-2017-0006     OR     http://manu47.magtech.com.cn/Jwk3_jdis/Y2017/V2/I2/1

Figure 1. Topic search queries used for data collection.
Figure 2. The main user interface of CiteSpace.
Figure 3. The distribution of the bibliographic records in Set #14.
Figure 4. A dual-map overlay of the science mapping literature.
Figure 5. A hierarchy of index terms derived from Set #14.
Figure 6. 49 references with citation bursts of at least 5 years.
Figure 7. A landscape view of the co-citation network, generated by top 100 per slice between 1995 and 2016 (LRF = 3, LBY = 8, and e = 1.0).
Figure 8. A timeline visualization of the largest clusters of the total of 603 clusters.
Cluster Size Mean (year) Silhouette % of the
network
Accumulated %
of the network
% of top 3 LCCs Accumulated %
of LCCs
0 214 2006 0.748 4.5 4.5 8.1 8.1
1 209 1997 0.765 2.3 6.7 4.1 12.2
2 190 2009 0.845 3.3 10.0 6.0 18.2
3 160 2005 0.954 2.9 12.9 5.3 23.5
4 152 1992 0.890 1.7 14.6 3.0 26.5
Table 1 The five largest clusters of co-cited references of the network of 3,145 references. The largest three connected components include 1,729 of the references.
Cluster ID Size Silhouette From To Duration Median Sustainability Activeness Theme
0 214 0.748 1995 2015 21 2006 ++++++ Active Science mapping
1 209 0.765 1990 2006 17 1997 ++ Inactive Domain analysis
2 190 0.845 2000 2015 16 2009 Active Research evaluation
3 160 0.954 1996 2014 19 2005 ++++ Active Information visualization / Visual analytics
4 152 0.890 1988 1999 12 1993 Inactive Applications of ACA
6 125 0.925 1995 2006 12 2001 Inactive Webometrics
8 93 0.882 1994 2010 17 2002 ++ Inactive Bibliometric studies of social work in health
11 48 0.965 1994 2006 13 2000 Inactive Bibliometric studies of management research
12 44 0.966 1990 1999 10 1996 Inactive Graph visualization
16 29 0.977 1999 2007 9 2003 Inactive Bibliometric studies of information systems
28 15 0.995 2004 2013 10 2008 Inactive Global trend; Water resources
Table 2 Temporal properties of major clusters.
Figure 9. A hierarchy of key concepts selected from citing articles of Cluster #0 by log-likelihood ratio test.
Figure 10. High-impact members of Cluster #0
Figure 11. Top 20 most cited references in the largest cluster.
Figure 12. Major citing articles to the largest cluster.
Figure 13. A hierarchy of key concepts in Cluster #1.
Figure 14. Key members of Cluster #1.
Figure 15. Key members of Cluster #1, sorted by sigma.
Figure 16. Citing articles to Cluster #1.
Figure 17. A hierarchy of key concepts in Cluster #2.
Figure 18. High-impact members of Cluster #2.
Figure 19. High-impact members of Cluster #2.
Figure 20. Citing articles of Cluster #2.
Figure 21. A hierarchy of key concepts in Cluster #3.
Figure 22. High-impact members of Cluster #3.
Figure 23. Key members of Cluster #3.
Figure 24. Citing articles of Cluster #3.
Figure 25. Novel co-citations made by 8 papers of White (left) and by 14 papers of Thelwall (right).
Year ?M ?CLw CKL Geometric Mean GC Title Reference
2016 6.0541 0.0152 0.0251 0.1322 5 A review of the literature on citation impact indicators (Waltman, 2016)
2016 0.9235 0.0019 0.3407 0.0842 0 How are they different? A quantitative domain comparison of information visualization and data visualization (2000-2014) (Kim, Zhu, & Chen, 2016)
2016 0.8207 0.0017 0.0640 0.0447 2 A bibliometric analysis of 20 years of research on software product lines (Heradio et al., 2016)
2015 1.7498 0.0073 0.0380 0.0786 0 Global ontology research progress: A bibliometric analysis (Zhu et al., 2015)
2015 1.9873 0.0052 0.0397 0.0743 9 Bibliometric methods in management and organization (Zupic, 2015)
2015 1.9906 0.0029 0.0238 0.0516 13 A review of theory and practice in scientometrics (Mingers & Leydesdorff, 2015)
2014 1.6240 0.0087 0.0434 0.0850 3 Research dynamics: Measuring the continuity and popularity of research topics (Yan, 2014)
2014 1.1837 0.0031 0.0463 0.0554 1 Making a mark: A computational and visual analysis of one researcher’s intellectual domain (Skupin, 2014)
2014 0.4462 0.0024 0.0270 0.0307 12 The knowledge base and research front of information science 2006-2010: An author cocitation and bibliographic coupling analysis (Zhao & Strotmann, 2014)
2013 2.5398 0.0112 0.0643 0.1223 13 Analysis of bibliometric indicators for individual scholars in a large data set (Radicchi & Castellano, 2013)
2013 1.0781 0.0065 0.2180 0.1152 6 A visual analytic study of retracted articles in scientific literature (Chen et al., 2013)
2013 1.7978 0.0064 0.0542 0.0854 24 Quantitative evaluation of alternative field normalization procedures (Li et al., 2013)
2012 3.6274 0.0107 0.0811 0.1466 29 SciMAT: A new science mapping analysis software tool (Cobo et al., 2012)
2012 3.4380 0.0248 0.0259 0.1302 15 A forward diversity index (Carley & Porter, 2012)
2012 1.0719 0.0032 0.0321 0.0479 11 Visualizing and mapping the intellectual structure of information retrieval (Rorissa & Yuan, 2012)
Table 3 Potentially transformative papers published in recent years (2012-2016).
Waltman, 2016), 2) (Zupic, 2015), and 3) (Zhu et al., 2015).">
Figure 26. Three examples of articles with high modularity change rates: 1) (Waltman, 2016), 2) (Zupic, 2015), and 3) (Zhu et al., 2015).
Figure 27. Stars indicate articles that are both cited and citing articles. Dashed lines indicate novel co-citation links. Illustrated based on 15 papers of the author’s own publications.
Figure 28. Citation trajectories of Howard White’s publications and their own locations.
Bar-Ilan, 2008).">
Figure 29. Novel links made by a review paper of informetrics (Bar-Ilan, 2008).
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