Vra 2013 cultural heritage data visualizations sulaPresentation Transcript
Visualizing cultural networksChris Alen SulaSchool of Information & Library Science, Pratt Institute4 Apr 2013 – Visual Resources AssociationSession #11: Cultural Heritage Data Visualizations
Overview‣ Case study: Occupy Wall Street Project List‣ What networks can represent‣ Data structure for networks‣ Gephi network software‣ A few recipesCultural heritage documents often containinformation about relationships—the types ofrelationships that can be explored and studiedthrough network visualization.
Case study: Occupy Wall Street Project ListProject informationStructured dataDAP <—> Occupy Town SquareDAP <—> OWS Direct ActionDAP <—> OWS Silkscreen Guild. . .Occupy Town Square <—> OWS Direct ActionOccupy Town Square <—> OWS Silkscreen Guild. . .
―less than a year after the last protester wasremoved from New York Citys Zuccotti Park, themovement has re-emerged as a series of laser-focused advocacy groups that, looselyorganized under the Occupy umbrella, are tryingto effect change in a variety of sectors, financialand otherwise.‖Time Magazine, Dec 3, 2012
Case study: Occupy Wall Street Project ListFeb 2012 Apr/May 2012 June/July 2012
What networks can represent‣ kinship and personal connections (friends, partners, co-performers, colleagues, acquaintances)‣ organizations (roles, partnerships, alliances)‣ linguistic associations (words, topics)‣ trade routes, voyages, infrastructure‣ communication (letters, social media)‣ ideological ties (claims, theories)‣ In each case, we look for similar things (nodes)which are related in regular ways (edges)
Data structure for networks‣ edge table*‣source* / target* / direction / weight / timespan‣ node table‣id* / name* / property 1 / property 2 / etc.*required
Data structure for networks‣ Look for consistently recorded information (recurringentities, similar connections)‣ Think hard about whether connections are directed orundirected—this may change the structure‣ Expect that you‘ll have to resolve some problems withmessy data (ambiguity, variant spellings, etc.)‣ Search for familiar real-world objects to make intonodes (e.g., people) and treat relationships betweenthem as edges (e.g., being displayed together at thesame gallery event)
Nodes v. Edges‣ Networks can be single mode (one type of node, e.g.,people) or multi-mode (e.g., people and institutions)‣ Most layouts and network statistics are built for singlemode networks, including ones in Gephi‣ In many cases, it is advantageous to‣ attribute-ize node properties (e.g., make ‗person‘ and‗institution‘ values in a ‗type‘ attribute)‣ edge-ize abstract ―things‖ that actually only connectnodes (e.g., being included in the same catalog)‣ rather than treating everything as a new type of node.
Gephi network software‣ free and open-source forWindows, Mac, and Linux‣ allows for detailed designadjustments (sizing, coloring,filtering, labeling)‣ community-developed pluginsprovide additional layoutoptions and customization‣ computes network statistics,detects clusters‣ exports files as image (PNG,PDF), vector (SVG), or webformatshttp://gephi.org
A few recipes‣ CategorizationMap terms that appear throughout a subject vocabulary by treatinghierarchy as an edge and each term as a node. (Reveals conceptualstructure of categorization system.)‣ CollaborationMap people that collaborate on works by treating each person as a nodeand each instance of collaboration (i.e., work) as an edge that connectsthem. (Reveals social patterns.)‣ ProvenanceMap provenance relations between works by treating each work as anode and co-location/co-exhibition as a relationship that connects them.(Reveals curitorial relationships.)
ContactChris Alen SulaAssistant ProfessorPratt Institute, School of Information & Library Sciencehttp://chrisalensula.org@chrisalensula on Twittercsula@pratt.edu