Browse > Article
http://dx.doi.org/10.22640/lxsiri.2020.50.2.203

A Study on the Spatial Distribution Patterns of Co-authoring Activities in the Korean Cadastral Research Field  

Kim, Yun-Ki (Department of Land Management, Choengju University)
Publication Information
Journal of Cadastre & Land InformatiX / v.50, no.2, 2020 , pp. 203-219 More about this Journal
Abstract
The primary purpose of this study is to identify spatial distribution patterns of co-authoring activities in Korean cadastral science. The analysis showed that a small number of researchers played an essential role in the Korean cadastral co-authorship network. In particular, some authors not only had a significant influence on other nodes in the network but also served as intermediaries between researchers. Moreover, the distance between researchers influenced co-authorship decisions to a limited extent. This study differs considerably from previous studies in that it used spatial analysis techniques to identify spatial distribution patterns of co-authoring activities. However, this research is limited in that it applied only 2019 data to determine the spatial distribution pattern of co-authoring activities. We can overcome this limitation if we analyze the spatial distribution patterns of co-authoring activities using multi-year data in future studies.
Keywords
spatial distribution patterns; cadastral co-authorship network; node; co-authorship decisions; intermediaries; distance;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Anselin L. 1995. Local indicators of spatial association-LISA. Geographical analysis. 27(2): 93-115.   DOI
2 Abbasi A, Altmann J, Hossain L. 2011. Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics. 5(4):594-607.   DOI
3 Barrett S. 2002. Overcoming transactional distance as a barrier to effective communication over the internet. International Education Journal. 3(4):34-42.
4 Bassett DS, Bullmore ED. 2006. Small-world brain networks. The neuroscientist. 12(6):512-523.   DOI
5 Fiaschi D, Gianmoena L, Parenti A. 2014. Local directional moran scatter plot. p.97-112
6 Apolloni A, Jean-Baptiste R, Pablo J. 2013, Collaboration range: Effects of geographical proximity on article impact. The European Physical Journal Special Topics. 222(6): 1467-1478.   DOI
7 Anselin, L. 2003. An introduction to spatial autocorrelation analysis with GeoDa. Spatial Analysis Laboratory, University of Illinois, Champagne-Urbana Illinois.
8 Bornmann L, de Moya Angeon F. 2019. Hot and cold spots in the US research: A spatial analysis of bibliometric data on the institutional level. Journal of Information Science. 45(1):84-91.   DOI
9 Bornmann L, Waltman L. 2011. The detection of "hot regions" in the geography of science―Avisualization approach by using density maps. Journal of informetrics. 5(4): 547-553.   DOI
10 Cohen E, Delling D, Pajor T, Werneck RF. 2014. Computing classic closeness centrality, at scale. In Proceedings of the second ACM conference on Online social networks. p. 37-50.
11 Morel CM, Serruya SJ, Penna GO, Guimaraes R. 2009. Co-authorship network analysis: a powerful tool for strategic planning of research, development and capacity building programs on neglected diseases. PLoS neglected tropical diseases. 3(8):e501.   DOI
12 Frenken K, Hardeman S, Hoekman J. 2009. Spatial scientometrics: Towards a cumulative research program. Journal of informetrics. 3(3):22-232.
13 Hoefer M, Krysta P. 2005. Geometric network design with selfish agents. In International Computing and Combinatorics Conference. p. 167-178.
14 Hou, H., Kretschmer, H., & Liu, Z. (2008). The structure of scientific collaboration networks in Scientometrics. Scientometrics, 75(2), 189-202.   DOI
15 Hudson J. 1996. Trends in multi-authored papers in economics. Journal of Economic Perspectives. 10(3):153-158.   DOI
16 Jones HAC, Noble C, Damsgard B, Pearce GP. 2011. Social network analysis of the behavioural interactions that influence the development of fin damage in Atlantic salmon parr (Salmo salar) held at different stocking densities. Applied Animal Behaviour Science. 133(1-2): 117-126.   DOI
17 Liu X, Bollen J, Nelson ML, Van de Sompel H. 2005. Co-authorship networks in the digital library research community. Information processing &management. 41(6):1462-1480.   DOI
18 Matkan AA, Shahri M, Mirzaie M. 2013. Bivariate Moran's I and LISA to explore the crash risky locations in urban areas. N-Aerus. 14:1-12.
19 Nurhas I, de Fries T, Geisler S, Pawlowski J. 2018. Positive Computing as Paradigm to Overcome Barriers to Global Co-authoring of Open Educational Resources. In 2018 23rd Conference of Open Innovations Association. p. 281-290.
20 Newman ME. 2001. The structure of scientific collaboration networks. Proceedings of the national academy of sciences. 98(2):404-409.   DOI
21 Zhang J, Luo Y. 2017. Degree centrality, betweenness centrality, and closeness centrality in social network. In 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017). Atlantis Press.
22 Rochat Y. 2009. Closeness centrality extended to unconnected graphs: The harmonic centrality index.
23 Yan E, Ding Y. 2009. Applying centrality measures to impact analysis: A coauthorship network analysis. Journal of the American Society for Information Science and Technology. 60(10): 2107-2118.   DOI