Comparer des méthodes

	Revue cartographique basée sur les réseaux	Revue cartographique
Domaine	Scientométrie	Scientométrie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s	Late 1990s–2000s; major methodological formalization ~2010s
Auteur d'origine	Petticrew & Roberts (mapping review); network overlay adopted from bibliometric network analysis tradition	Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)
Type	Evidence synthesis method with network analysis overlay	Systematic evidence mapping methodology
Source fondatrice	Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing. ISBN: 978-1405121101	James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.
Alias	network mapping review, citation-network mapping review, network-enhanced evidence mapping, network-informed mapping review	evidence map, systematic map, research map, literature map
Apparentées	6	6
Résumé	A network-based mapping review combines the breadth of a traditional evidence mapping exercise with bibliometric network analysis to chart the structural landscape of a research field. Rather than simply cataloguing studies by topic, this approach constructs citation, co-authorship, or co-word networks to reveal clusters of intellectual activity, influential works, and collaboration patterns — producing both a visual and a descriptive map of the evidence base.	A mapping review (also called a systematic map or evidence map) is a form of systematic review that aims to chart the extent, range, and nature of evidence on a broad topic rather than synthesize findings into a single pooled answer. It categorizes studies by key dimensions — such as intervention type, population, outcome, and study design — and presents the resulting landscape visually and tabularly so that researchers and practitioners can identify clusters of evidence, knowledge gaps, and priorities for future primary research or deeper synthesis.