2. Materials and Methods
In this paper, we used CiteSpace, a
Java application for visualizing and analyzing trends and patterns in
scientific literatures, for
scientometric analysis. The
paper
data used for the analysis was extracted from the Science Citation
Index-Expanded (SCI-E) of the Web of Science Core Collection (WoSCC).
Specific retrieval strategy is as follows: hydrological connectivity OR
hydrological connection OR hydrologic connection OR hydrologic
connectivity. Also, timespan was set as 1998-2018. This search obtained
3,307 bibliographic records including titles, abstracts, and cited
references and were then exported to CiteSpace for subsequent analysis.
A knowledge map of the publication characteristics, including subject
categories, journals, keywords, authors and references was generated
based on a certain number of papers published in this field using the
functions of Citespace software (co-cited analysis, co-occurrence
analysis and co-operative
analysis), which shows the hot research issues and emerging trends. A
network map contains many nodes and links. Nodes in different kinds of
network map represent different meaning such as author, keyword,
reference, institution, and the larger the nodes, the more the
publications or frequency (Liang, Luo & Zhong, 2018). The connecting
lines between nodes indicate a relationship of co-operation,
co-occurrence or co-citation, and their various colors represent the
year that the two nodes are co-cited (Chen, 2006; Xie, 2015; Min et al.,
2018). Additionally, a node surrounded by a purple ring indicates that
the node has a relatively strong (≥ 0.1) centrality, which indicated an
extensive connection with other nodes. In this research, most of the
analysis was conducted by CiteSpace based on papers published on SCI-E
(Science Citation Index Expanded) over the period of 1998 to 2018.
Contributing co-cited authors and journals were mapped to identify the
evolution of hydrological connectivity. Hot issues and frontier research
were identified based on the frequency of popular keywords, which can be
divided into 3 periods (i.e., 1998 to 2004, 2005 to 2011, and 2012 to
2018). Analysis of highly co-cited references will also be conducted as
an effective supplement to the related research hotspots. In addition,
burst detection of co-cited authors was applied to identify the author
whose research achievements in this research field is significant.
Therefore, the analysis of co-operative and co-occurrence network as
well as co-cited ones with the frequency, centrality and burst will be
conducted to identify research characteristics and emerging trends on
hydrological connectivity.