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Interface designs for deep maps: a presentation from #PolisNEH to #UCLADH
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Interface designs for deep maps: a presentation from #PolisNEH to #UCLADH

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Sample results from three days of exploratory prototyping for deep maps around a case study of religion and community in Indianapolis.

Sample results from three days of exploratory prototyping for deep maps around a case study of religion and community in Indianapolis.

Published in: Education, Technology, Spiritual

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  • For more information: http://thepoliscenter.iupui.edu/index.php/introducing-the-neh-institute-participants/Our group picked a particular path through a deep map to guide us in thinking about importance of this technology. We used this environment to explore the contested meanings of 'neighbourhood' as it was used by religious communities in one tightly-bounded late-20th century place. In doing so, we explored the tension between interfaces for traditional views of data and sources, and the desire for a sense of immersion in a deep map. We wanted to support different scholarly and disciplinary practices without ending up with the lowest common denominator. The fact that we were an interdisciplinary and international team that included expertise in geography, technology and history helped with that, though it also meant finding ways to understand the lens through which we each saw the world and deep maps.
  • The first outcome of our prototype isn't an interface but a model. Developing prototype interfaces around a particular case study or research question meant we had to articulate questions about the conceptual models and modes of interaction underlying the designs visible on the screen. In this case, the act of labelling one of the interface elements uncovered the two different models of the 'deep map' shown. We uncovered ambiguous definitions of a 'deep map' in relation to the intersection between the individual research question and the wider or shared deep mapping environment. They may be two aspects of the same thing, or so permeable that the difference doesn't matter. The fuzziness of the division also highlights the importance of collaboration in creating and using deep maps.Here I use chorography as the antiquarians did - it's a view that includes every conceivable source from the universe of data without any curation. The deep maps help construct the argument contained in the spatial narrative and provide a platform for presenting it.‘deep maps and spatial narratives’ pyramid (‘more data’ going down, ‘more curation’ going up):Top layer: Spatial narrativeMiddle layer: Deep map 'personal': your working space // Deep map 'prime': constructed around research interestBottom layer:Chorography (the universe of data)
  • This interface is designed to support an immersive experience. We're aiming for a sense of flow within the process of finding and analysing data. The interface allows people to discover and interrogate content without being confronted with the blankness of a search box (though search functionality is supported). The time and place sliders are always present at the bottom of the screen, making navigation intuitive and easy. The timeline includes a histogram that shows the density of different types of documents and resources over time to help people get a sense of the scope of the content and to help deal with the patchiness and messiness of humanities data.The spatial zoom includes 'spatial bookmarks' that not only expose the administrative structures relevant to that location but also help you jump quickly between scales or locate yourself precisely within space.You can also navigate through the content sources and their multiple relations to each other and time and place.The interface supports different modes of interaction, accessed through and indicated by the state of the tool bar. It can fold down into a compact toolbar or expand into a working view. The working view provides access to the source material and supports for the usual scholarly processes of comparison, review, annotation of resources, as well as the management of sources such as documents, images, audio, video, datasets, maps and advanced search functions. This view would also help the user view multiple locations and things related to the one resource, and enable the multivocality so vital to deep maps.
  • This screen shows some of the functionality available in the 'content bar'. The content bar changes to show content relevant to the points in time and space shown on the screen. It's designed to hint at the available content and functions while letting the user stay immersed in the spatial experience. It supports zooming between close and distant readings over space and time and between sources.Media: pretty straightforward, displays related media and links to fullscreen viewing.Documents: these documents are added through searches or related dates, locations, the built environment, organisations, people, events and subjects. Our interface relies in part on entity recognition and topic modelling to pull out things like people, places, events and topics within the content sources. We've made assumptions about the level of OCR or transcriptions and metadata available to allow the dynamic mining of content. The order of results is weighted by density or by your research interests.Data sources: we thought a lot about how to relate traditional GIS-style analysis to a deep map, and this box is a hint toward screens that overlay different views from data sources on the map view. Again, these can be explored, interrogated and related to other types of content in another view.Historic maps and aerial photos: we thought it was really important to be able to pull in as many historic maps and images as possible. We'd like people to be able to see the changes in areas by sliding through different maps over time.Subjects: as I mentioned, this relies on metadata as well as natural language processing techniques. Any person, place, topic or event recognised within the content sources related to that view are presented here, and each can be used as a form of facetted search or to follow new paths of investigation.Snapshots: this is a function to support the scholarly use of deep maps. When you find a view of level of scale in time and space and related content sources etc that you want to return to or that you have used to construct your argument, you can capture those parameters. Shareable links to the snapshot might also help support open peer review.The final box isn't visible but it's one of most important - 'about this deep map of religion in Indianapolis'. A deep map makes the context of creation evident: you always know who created it and how their research interest influenced selection of sources, spaces and time.
  • I'll stop there, but these screenshots have given you a sense of the potential of deep maps and the architecture and models that might underlie them. For more information: http://thepoliscenter.iupui.edu/index.php/introducing-the-neh-institute-participants/
  • Transcript

    • 1. Interface designs for deep maps Daniel Alves Don Lafreniere Scott Nesbit Mia Ridge Presentation from #PolisNEH to #UCLADH
    • 2. More dataMore curation
    • 3. Interface designs for deep maps Daniel Alves Don Lafreniere Scott Nesbit Mia Ridge Presentation from #PolisNEH to #UCLADH