Using Historic Imagery to Improve Historic Cartographic Literacy and Foster User Engagement in Deep Mapping Applications

James Juip, Michigan Tech University
Robert Cowling, Michigan Technological University

Deep maps integrate data and representations of space, time, architecture, material culture, environment, and community knowledge into a spatially and temporally scaled digital platform that affords open-ended exploration of a particular time and place. To effectively meet these goals Deep Maps are powered by Historical Spatial Data Infrastructures (HSDIs) which not just house this data but create spatio-temporal links between these vastly unique data sets. To utilize a deep mapping application at full effectiveness users must not just be able to effectively navigate the user interface but must also have a basic comprehension of the historic records and cartography utilized to represent the historic environments within the deep map. However, a vast body of literature has illustrated the challenges students and members of the public have in reading historic cartographic materials, especially Sanborn Fire Insurance Plans. This gap in comprehension limits users' ability to engage and explore the spatial-temporal relationship between historic data within deep mapping applications. To address this gap two deep mapping applications (The Keweenaw Time Traveler Historic Image Viewer and the Historical Atlas of Duluth) use two distinct methodologies to integrate historic imagery into their HSDIs and represent them in their user interfaces, in an effort to use these links to allow users to compare historic photography and cartography both growing their knowledge of the cartographic symbolism of the built environment illustrated by the historic cartography and also fostering more engagement with the data illustrated within the application. Through the use of user surveys this study investigates the efficacy of each of these methods in fostering growth in the comprehension of historic cartographic symbology and user engagement in an effort to establish a set of best practices for historic image integration and representation in deep mapping.

No extended abstract or paper available

 Presented in Session 207. Advancing Spatial Methods