Toward the Visualization of History: The Past as Image

Paintings by Mondrian and Rothko are mapped on image plots. This technique is shown in Figure 7, which presents paintings by Piet Mondrian and paintings by Mark Rothko as image plots. Using ImagePlot software, miniature images of the paintings themselves are mapped according to their brightness x-axis and saturation y-axis. Showing the original data object, in this case the image of the painting rather than a representational surrogate, allows patterns to emerge in their original context that could not be represented in mapping the raw data itself.

Discovering patterns across large data sets in the humanities is also known as a mode of inquiry called distant reading. Manovich questions the usefulness of visualization reductionism in analyzing cultural heritage data: Consideration of context becomes more meaningful when examining contemporary theories and practices of information visualization, especially in the art history and cultural heritage areas of collection management and fostering and supporting scholarship.

Information visualization is now offering researchers methods of exploring visual data sets both by object and by aggregate, and in the process reshaping the visual language of data. It is also providing curators, librarians, and archivists new ways to enhance metadata, expand contextual description, and foster new uses of materials under their stewardship.

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In describing the many ways that data visualization can facilitate new ways of analyzing both individual works and aggregated collections, representative examples are perhaps best organized within certain conceptual categories. Three different means by which contemporary information visualization use cases have supported new kinds of inquiry and understanding are pattern analysis, narrative modeling, and collection analysis.

This is a broad ontology—new methods of visualization will emerge as new technologies themselves emerge. No single ontology can capture the dynamism and creativity of humanistic analysis even within the realm of information visualization. However, this loose categorization provides an entry point into thinking of ways that visualizing artistic and historical data can spur new methods of scholarship, curatorial practice, collection management, and aesthetic and creative interpretation.

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The categories can help delineate the shifting, overlapping ways that data visualization can be utilized. Exposing outliers, trends, and abrupt changes are all traditional functions of pattern analysis as a visualization strategy. What marks contemporary information visualizations as different from those of previous eras, however, is the scale of data these visualizations can capture and the novel types of patterns they can display. Network diagrams are a method of visualizing relations and interactions between persons or entities.

This was less a representation of a network, with its assumption of transactional exchange, than it was an abstraction of influence.

Histomap: Visualizing the 4,000 Year History of Global Power

Network diagrams, instead, have the ability to visually represent actual occurrences, be they citations, purchases, or other modes of interdependence. The Getty Provenance Index has extracted information on art buyers, sellers, auctioneers, and others to analyze the patterns of exchange in the European art market in the early nineteenth century.

Using , records spanning —, this visualization accounts not just for individuals and transactions, but also place, type of transaction, and total market activity Figure 8. Network diagram of agents connecting the British, Belgian, Dutch, and French auction markets from — using , records from the Getty Provenance Index databases.

Paul Getty Trust and Maximilian Schich. Visualization can be seen at https: The volume of individual data points contained in this single visualization zoomable for detailed viewing goes far beyond simple statistics. Clusters of activity illustrate relationships within the data that simple numbers and statistics might not expose. Patterns reveal larger relationships. For collection managers, these patterns can expose other qualities.

Network diagrams and other visualizations of aggregate data can help identify outliers, inconsistencies, or inaccuracies within data that can be indicative of errors in cataloging, uncontrolled taxonomies, or other metadata issues requiring correction. The project analyzed tens of millions of individual images scraped from Google Street View of Paris and ran algorithms to determine which visual features of the built environment occurred most commonly in the city and which architectural details most evoked the character of the urban landscape Figure 9. While identifying common characteristics has long been the purview of academic analysis, the computational processing power of projects like this have the ability to identify characteristics that may not appear through individual, subjective scrutiny.

Specific architectural elements— doorway arches, balcony railing patterns, window shutter styles—are identified as commonalities and come to be more representative of visual identity than what may be more apparent to the classically trained eye. Visualization, in this example, is both generative and deductive.

The visual features of a place, of created space, assume their own grammar and, through redundancy distilled from a massive amount of image data and juxtaposition, evoke a novel pattern and meaning latent in a set of architectural images. In a similar project, researcher John Resig used computational image similar- ity analysis to examine digital images of anonymous i.

His project also generated a visual interface of similar works that subject experts and researchers could then review for correction or annotation. In this instance, visualization, and its underlying algorithms, enables curators and collection managers to review potential matches and update and augment descriptive information.

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Visual analysis brings computational tools and a preliminary level of automation to metadata enhancement. These depend on embodied and situated knowledge, cultural conditions and training, the whole gamut of individually inflected and socially conditioned skills and attitudes. Data is visualized, charted, and parsed in order to frame an assertion and to provide an interface into an avowal, yet in many ways the data itself remains static.

This stagnancy of the data impedes the narrative movement necessary for a visualized argument. Contemporary visualizations, however, are able to make use of the interactive nature of the web to allow greater user manipulation of visualizations in a manner that supports the widening understanding needed to fuel interpretive insight. Image Atlas is an example of visualizing information in that it is both content-dependent and also equivocal towards its narrative model.

The website Figure 11 allows the user to enter a search term, and it then pulls the top Google Image results from different country domains, which can be sorted by Gross Domestic Product GDP or alphabetically. It manages to recontextualize visual materials through the lens of nationality and economic power and reveal comic or astonishing comparisons through juxtaposition. It also evokes the mystery of the algorithm that drives the tool and reminds the user that any argument, verbal or visual, remains idiosyncratic. Image Atlas, though, also slyly demonstrates the potential for the flat neutrality of visualization.

Though clearly a mediated, produced interface with a tacit political argument and clear artistic intent , its content is returned via a search engine whose PageRank algorithm remains unknown. Are they illustrative of national taste in aesthetics? Are they simple algorithmic sorting of search popularity? Information visualization, of course, presumes that these are all equally valid propositions, and that all are potential avenues of analysis and meaning.

In this case, the visualization inverts the logic of traditional research and analysis, instead suggesting that cultural identity is reflected and refracted by the similarity and contrast of images related to its search habits. These narratives emerge from both the immediacy of the images and the obscurity of the algorithm.

By providing multiple methods of sorting and color-coding, along with interactivity, animation, and rollover thumbnail images, the visualization accomplishes a number of goals. Much like traditional static visualizations, it reveals trends and patterns, outliers and commonalities, assigning importance by size and using colors and arrangement to categorize. But by being dynamic and interactive, it allows the same underlying set of data artwork sales to be parsed in different, interconnected visualizations, all appearing within the same interface. Selecting an alternate sorting method allows the user to reform the same set of visualized objects in this case, color-coded bubbles into new arrangements.

Toggling between different methods of arrangement begins to allow one to iteratively explore the data in a way that is layered, progressive, and akin to discursive exposition. Data Visualization at http: Geographic visualizations provide their own narrative impetus to cultural and artistic change as they track the movement of trends across time and place. The narrative is one of connection and sway, showing how the impact of objects, collections, or technologies can be understood as a process, a movement, or a flow and not just as a set of discontinuous data points.

Though the previous examples have been situated conceptually, their utility to art historians, curators, and content stewards is clear. Visualization can reveal patterns and uncover narratives that have the potential to enhance how collections are managed, accessed, and used. The term collection analysis is used here quite broadly, referring both to the contents of a curated collection as well as a large, less-mediated aggregation of items or objects sharing a similar trait, such as creator, owner, exhibition, or other technical or descriptive metadata.

Information visualization, beyond illuminating shared characteristics for further scrutiny or exploration, has also come to serve an administrative or technical function, allowing curators and collection managers to gain intellectual or physical control of a collection. This function is, however, not merely an administrative one, but one that offers its own revelations about provenance, collecting trends, and otherwise unseen characteristics of institutional practice. Kress History and Conservation Database Project is an example that offers a bridge between the concepts of collection and pattern analysis.

Metadata documenting the 3, paintings, sculptures, medals, and decorative art collected by the Samuel H. Kress Foundation between and was exported from the database system, refined, and then imported into the online visualization tool Viewshare. The multiple visualization interfaces revealed trends that, for a researcher, could take days or weeks of documenting and analyzing hundreds of individual records. But in this example, the data can be visualized immediately in a way that elicits information immediately confirmable through individual analysis.

The method of inquiry is, in some ways, reversed. Instead of the characteristics of exemplars informing analysis, the trends within an aggregation can serve as wayfinders for further exploration. Screenshot from Kress Collection in Viewshare. In Figure 14, manipulating histogram sliders for date range changes the scatterplot visualization. Here the x-axis represents date and the y-axis indicates the purchase price of each individual artwork.

Comparing these two images a process that can be done automatically using the online interface shows us the dramatically different costs of artworks purchased in two different time periods.

Histomap: Visualizing the 4, Year History of Global Power

As a comparison of the images shows, Kress family purchasing patterns turned towards far more expensive works during the second half of their timespan of collecting. In Figure 15, from the same data set, the pie charts quantify the number of works purchased from a specific dealer. Here, limiting the results by specific date spans shows that one dealer dominated the first eighteen years of collecting. It can also be easily seen that the same dealer played a far smaller role in the final thirteen years of collecting. While these trends are made apparent through dynamic visualization, they give away none of the reasoning behind, or origins of, this shift.

The scatterplot or pie chart cannot reveal, as happens to be the story in these specific examples, that Kress family acquisition tastes changed due to the death of one of the key family members overseeing purchases. The visualization does not show that near-exclusive reliance on one specific Italian art dealer waned at a certain period due to the complications of World War II. But visualizing the provenance information of this collection leads to a suggestion, a curiosity, that may not emerge from study of the collection data itself, and these qualities of intrigue have value not just to the researcher but to the collection stewards supporting that research and maintaining this and other related data sets.

The other essential feature of these visualizations is their dynamic and interactive nature. This is not a static visualization that codifies a statistic or an argument, but one that responds to faceting, sliders, text search, and other user inputs. They are visualizations that can be manipulated according to the interest and analysis of the user and the affordances of the underlying data itself.

While information visualizations are often thought of as being generated externally from the data they represent, they can also be an elemental part of online collections themselves. To create views that allows a person to understand the outline of a history and invite further investigation. Item-specific timeline visualization from the Smithsonian Cooper-Hewitt Museum collection website. A version of this interactive visualization can be turned on to appear on every item page.

Information such as the date of creation or acquisition, while valuable, lacks the ability to manifest the larger historical and circumstantial provenance. In this case, the visualization serves to provide relevance, to situate an individual item within a larger institutional context, and to allow a user to better understand collecting practices and the historical contingency of creation and acquisition. Collection analysis is, of course, the province of researchers as well as curators, collection managers, and museum technologists. The advent of open data in the museum world, primarily collection data extracted from systems like The Museum System TMS , has allowed individuals outside the institution to create their own visualizations.

An example is Figure 16, in which the collection data of the Tate Museum was used to parse the distribution of artworks by the birthdate of artists, with the size of each bubble representing the number of works by that artist in the collection the vertical position is for visual clarity and has no statistical meaning.

Distribution of artworks in the Tate collection by birthdate of artists. One of the exciting aspects of data visualization for art librarians, museum archivists, and collection managers is that it breathes new life into the descriptive functions these roles often entail or support. Catalog records, TMS notes fields, curatorial annotations, registrar records, contextual descriptions and abstracts—the data that enables visualization is the data that the art library and museum community is especially talented at and uniquely trained for creating.

With information visualization, that ability to describe, to assign meaning and detail, serves not just to help users find items in a catalog, or better understand the contents of a book or the origins of a sculpture, but also allows for the creation of visualization and data-dependent interfaces that can enable new modes of research and inquiry across all of a collection, encompassing entire genres and oeuvres. Information visualization is far from being an ancillary discipline divergent from the services and skills of collection managers and catalogers, but is instead a domain that can reenergize, amplify, and expand the talents of curatorial and informational cultural heritage professionals.

Critiques aside, work like this paved the way for the production of modern data visualizations and charts that help people better understand the world around them today. Get your mind blown on a daily basis: Given email address is already subscribed, thank you! Please provide a valid email address. Please try again later. Using the Impartial Bean to Value Currencies.

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