[PDF] Unfolding the landscape drawing method of Rakuchū Rakugai Zu screen paintings in a GIS environment | Semantic Scholar Skip to search formSkip to main content> Semantic Scholar's Logo Search Sign InCreate Free Account You are currently offline. Some features of the site may not work correctly. DOI:10.3366/ijhac.2009.0008 Corpus ID: 207753897Unfolding the landscape drawing method of Rakuchū Rakugai Zu screen paintings in a GIS environment @article{Tsukamoto2009UnfoldingTL, title={Unfolding the landscape drawing method of Rakuchū Rakugai Zu screen paintings in a GIS environment}, author={Akihiro Tsukamoto}, journal={Int. J. Humanit. Arts Comput.}, year={2009}, volume={3}, pages={39-60} } Akihiro Tsukamoto Published 2009 Geography, Computer Science Int. J. Humanit. Arts Comput. In this paper, I propose a new methodology for analysing landscape drawing methods using a GIS. The subject of my analysis is the genre of Japanese screen paintings known as rakuchū rakugai zu, created between the 16th and 18th centuries. Rakuchū rakugai zu provide bird's-eye views of the then-capital city of Kyoto, including buildings, natural features, and human activities. The methodology introduced here uses GIS spatial analysis functions to scan the painting surface onto a survey… Expand View via Publisher cga-download.hmdc.harvard.edu Save to Library Create Alert Cite Launch Research Feed Share This Paper 1 Citations View All Figures and Topics from this paper figure 1 figure 2 figure 3 figure 4 figure 5 figure 6 figure 7 figure 8 figure 9 figure 10 View All 10 Figures & Tables Geographic information system Spatial analysis Associative entity One Citation Citation Type Citation Type All Types Cites Results Cites Methods Cites Background Has PDF Publication Type Author More Filters More Filters Filters Sort by Relevance Sort by Most Influenced Papers Sort by Citation Count Sort by Recency Extracting spatial data from historic artwork of Hobart and its region M. Farag-Miller Geography 2015 PDF Save Alert Research Feed Related Papers Abstract Figures and Topics 1 Citations Related Papers Stay Connected With Semantic Scholar Sign Up About Semantic Scholar Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Learn More → Resources DatasetsSupp.aiAPIOpen Corpus Organization About UsResearchPublishing PartnersData Partners   FAQContact Proudly built by AI2 with the help of our Collaborators Terms of Service•Privacy Policy The Allen Institute for AI By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License ACCEPT & CONTINUE