4. Maps and Space

Spatial data can be a critical part of constructing a digital art history project, whether it is following art markets, understanding building constructions, or tracking the provenance of a particular artwork. In this section, you will find resources dedicated to defining what spatial data is and how it can be displayed using various mapping platforms.

4.1 Readings

Dunn, Stuart. “Space as an Artefact.” In Digital Research in the Study of Classical Antiquity, 2008.

Goodchild, Michael F. “What Does Google Earth Mean for the Social Sciences?” In Geographic Visualization, edited by Martin Dodge. John Wiley & Sons, 2008.

Harley, J. B. “Deconstructing the Map.” In The Map Reader, edited by Martin Dodge, Rob Kitchin, and Chris Perkins, 56–64. John Wiley & Sons, Ltd, 2011.

Isaksen, Leif. “Lines, Damned Lines and Statistics: Unearthing Structure in Ptolemy’s Geographia.” E-Perimetron 6, no. 4 (2011): 254–260.

Presner, Todd Samuel, David Shepard, and Yoh Kawano. Hypercities: Thick Mapping in the Digital Humanities. Cambridge Mass.: Harvard University Press, 2014.

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4.2 Resources

The Geospatial Historian: A sibling to The Programming Historian, The Geospatial Historian offers a growing collection of well-edited step-by-step tutorials.

Geocoding Your Data: A list of several options to add latitudes and longitudes to your data.

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4.3 Tutorials

Moving Around Geocoordinates: Retrieve geocoordinates from one spreadsheet and put them in another.

Google Fusion Tables: A great starting point for creating data visualizations, especially for simpler charts, like pie charts and bar charts. While it does not offer much customization, you can produce results with just a few clicks. Here is a basic tutorial.

“Journey to the open data jungle with OpenRefine, CartoDB, Leaflet and Javascript,” Opensas Blog: A tutorial that illustrates how to go from raw, messy data to spreadsheet to map and finally to web service.

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4.4 Tools

Google Fusion Tables: An experimental data visualization web application to gather, visualize, and share data tables.

Google Earth: Google’s Geo products have long been identified as a powerful learning toolkit to empower people to visualize, share, and communicate information about the world around them. Google Earth Pro, which is now free, extends the power of Google Earth with additional capabilities. Features include: GIS data import, Movie Maker, High-resolution printing, Area and 3D measurement tools, Elevation viewsheds, and more.

Mapline: While you can buy an expensive business intelligence mapping software that requires a Ph.D to use, Mapline can create a map from Excel spreadsheet data in seconds. There are also a number of data sets that are offered through the tool, if you do not already have one but want to check out the available features.

Palladio: A web-based platform for the visualization of complex, multi-dimensional data. As with ManyEyes,an older IBM data viz tool, you can upload a dataset and visualize it in a number of different ways. But the kinds of visualizations you can do are somewhat more advanced: you can combine maps and timelines, filter your data, make bimodal network graphs, create relationships among tables, and download your data model. This may be the tool to try if you find that other platforms cannot quite handle what you want. Palladio is still new, so the documentation is not as robust as one might like, but this intro video is very helpful. Example.

Neatline: Designed to work with the exhibit platform Omeka, Neatline is best for smaller mapping projects for which visual detail and the element of time is important. You can draw polygons on the map and use Neatline’s timeline feature to walk users through changes over time. (You can also use Neatline to annotate an image.) One thing that’s distinctive about Neatline is that every point on your map can be an item from your Omeka collection. So, for example, if you have an Omeka collection that contains works of art, you can automatically plot each one, metadata and all, on your Neatline map. Neatline’s documentation is the best source of information about it. Example.

StoryMap: A mapping platform that is designed to show how a particular event (like the Olympic relay) or concept has unfolded over time. An easy-to-use interface walks the user from point to point, and each map point can be enhanced with words and pictures. Bonus: Gigapixel, also a Knight Lab project, allows you to do the same thing, but on a picture. Example.

Google Maps Engine: Go from spreadsheet to points on a map with this easy-to-use Google tool. Example.

Harvard WorldMap: The benefit of WorldMap is that you can not only plot your own points; you can also combine them with layers that display other information — for example, population numbers or demographics. It is a way to see how the points you are interested in correlate with other geographical information. Example.

Esri Story Maps: Confusingly, this is different from StoryMap. Story Maps are great for things like tours or travel narratives. It’s easy to create map points that incorporate images and text, and you can switch among many different templates. Each point has to be plotted individually, though, so it’s not great if you have a huge amount of data. Example.

CartoDB: An easy-to-use mapping platform that also has some advanced features. Uploading a spreadsheet of data is straightforward (CartoDB can grab latitudes and longitudes for you if you do not already have them), and mapping data is a matter of a few clicks. CartoDB maps tend to look really nice, and you can custom-style them if you know CSS. However, CartoDB only lets you work with 50 MB of data for free; after that, you have to pay. CartoDB’s documentation is excellent. Example.

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4.5 Projects

1. HyperCities projects

2. Holocaust Geographies


4. Digital Harlem

5. GhostMetropolis


7. Mapping the Republic of Letters

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