手法を比較

	タイムスライス型テーマ進化分析	サイエンスマッピング
分野	科学計量学	計量書誌学
系統	Process / pipeline	Process / pipeline
提唱年	2011–2012	2000s
提唱者	Cobo, López-Herrera, Herrera-Viedma & Herrera	Katy Börner, Chaomei Chen, and others
種類	Longitudinal bibliometric analysis	Method
原典	Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
別名	longitudinal thematic mapping, temporal thematic evolution, time-period thematic analysis, diachronic science mapping	knowledge mapping, domain mapping, research landscape visualization
関連	6	5
概要	Time-sliced thematic evolution analysis is a bibliometric method that divides a corpus of publications into consecutive time windows and tracks how research themes emerge, consolidate, split, merge, or disappear across those periods. By applying co-word analysis and strategic-diagram mapping within each slice and then linking themes across slices, it reveals the intellectual trajectory of a research field over time.	Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.