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What do you do with 36,409 places and 6,506 connections? Some cartographic representations of Pleiades data

Two projects that I am involved with, Pleiades and the World-Historical Gazetteer at the University of Pittsburgh, have been devoting considerable time and energy to modeling conceptual places and their connections, so I thought it was worth discussing a few of our observations and presenting some preliminary steps to visualize what we are doing.

First, a somewhat crowded overview of all of the Pleiades data set with map symbols representing different place types.

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Figure 1: All Pleiades places

At this level of zoom the map is nearly incomprehensible, but it does reveal some interesting aspects of our data set. The Grid like structure in India and central Asia is the result of “dumping” places for which we have insufficient data into the middle of Barrington Atlas grid squares. For the editorial board such a view is actually quite useful, as it highlights where we need to clean our data and focus on creating better locations.

Another way to show the reach of the Pleiades project is through a choropleth map, which shades different countries according to the number of Pleiades places within them.

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Figure 2: Choropleth Map

This is interesting, but I think it gives a fairly misleading sense of Pleiades coverage. From this map a reader would be unable to tell the extent of our data into Russia, China, and other countries where our locations are clustered around certain areas, not evenly spread throughout the country. It does highlight the areas where we have fairly extensive coverage, namely Italy, Greece, and Turkey.

To get around these issues, very often projects like ours use heat-maps to show both the concentration and extent of their data. I find this particular approach to be more aesthetically pleasing than simply throwing all of the points on the map, but due to the nature of a heat-map, I am still not convinced that it accurately depicts the extent of our coverage.

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Figure 3: Heat Map

One of my issues with heat-maps is how the colors “bleed” into areas where there are not points. While this can be adjusted and refined by decreasing the radius around each point, if taken too far the heat-map will simply show isolated dots of color instead of the expected continuous whole.

One experiment that I have done is to try and combine heat maps with a Voronoi diagram. The basic idea behind this approach is that the GIS system creates a polygon around each point, and any spot within that polygon is closer to that particular point than any other known point. This helps Pleiades editors, as a “hotspot” in one polygon indicates that there are multiple places “stacked” on one another on the same point, which is a good indication that we are dealing with inaccurate data. Conversely, a “hotspot” that extends through multiple polygons is expected behavior, and signifies that there is a dense cluster of points that are in close proximity but nevertheless still are in distinct locations.

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Figure 4: Detail of Voronoi Polygons and a Heat-Map

This is a very aesthetically pleasing map, but it is still difficult to quickly identify the correspondence between points, polygons, and the heat map. Using a hex-bin map (which is essentially a choropleth map with small hex shapes) styled like a heat map perhaps provides the cleanest and most comprehensible view of both our data coverage and density.

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Figure 5: Hex-bin map with heat map coloration

Of all the representations mentioned here (and many tests which were far too incomprehensible to show), I believe this map offers by far the best combination of understandability, honesty, and presentation. It clearly shows the concentration of our data in the Mediterranean like a heat map, but does a far better job of showing the precise location of the data points. It also shows a far more honest depiction of the number of points per country and the actual location of those points, which is not the case with a choropleth map at a country scale.

What these maps do not capture is the presence of connections in the Pleiades data set. As part of our evolving data modeling and best practices, we are now experimenting with a more robust system for expressing relationships between different places in our data set. These relationships could be political, geographic, or highly conceptual. One highly interesting product of this approach is that we can start thinking of the Pleiades gazetteer as a description of a network of places, not just as a list of their names and locations.

As a result, it is now possible to graph some of the relationships in our data. This is highly experimental and very incomplete, but I hope that by sharing our first steps in this direction that we can generate some discussion on our approach.

The first thing that I did was to download the Pleiades data set, then extract the connections information, creating a spread sheet that listed each connection as a source – target combination that social network analysis software would understand. Essentially any place that connected to another place was the source, while the place connected to was the target. This was then put into Gephi, where different “communities”, or places with denser connections to each other, are indicated by different colors.

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Figure 6: Detail of the Pleiades connections graph

The figure above is a detail of a portion of the resulting graph. You can see communities clustering around regions like Sicily and Sardinia, or around extremely important cities like Rome. The square on the outer reaches of the graph is simply a number of unconnected places that are pushed to the edges by the Gephi visualization software. While this is an interesting and somewhat compelling visualization, it is devoid of any geographic context. Luckily, Gephi has a plugin that places nodes (in our case the places) in a geographic location of there is data available. As we have location data for most of our places, we can use this plugin, which yields the result below.

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Figure 7: Pleiades places as a geospatial network

Now we are getting somewhere! The broad outlines of the Mediterranean are visible, as are features like the Nile river and even the outline of India. However, this network is still not on a geographic map (the Gephi globe plugin does not exactly match the coordinate system used by the geography plugin, and also it is based on modern geography), so we are somewhat missing the larger spacial context. Unfortunately there is not an easy way to export the specially enhanced network with Gephi’s statistics and colors – the .kml plugin does capture the color, but lumps all of the statistics into a single description tag.

After some experimentation with exporting, importing, and reexporting in Gephi and QGIS, I finally found a solution by importing the .kml exported from Gephi into QGIS and exporting that as a .csv file which can then be manipulated in OpenRefine to “extract” all of the information from the description field.  From there, the .csv file can be re-imported into QGIS, which results in the visualization below.

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Figure 8: Pleiades spatial connections overview map

While somewhat crowded and messy, a closer of Italy view shows the power of this visualization.

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Figure 9: Network around Rome without labels
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Figure 10: Network around Rome with labels

These visualizations show the networks of connections within a spatial context, and are an intriguing way to approach entities like kingdoms, political entities, or other place groupings. We are already experimenting with placing regions and larger entities (like Sardinia and Sicily) as the “midpoint” between all of their constituent connections, which you can see displayed on the maps above.

However, I want to take this idea one step further and eliminate the representative point entirely from such places. To do so, I decided that a mono modal network, or a network of just one place type, would be an interesting way to represent these connections. In short, any place that connected to the place Sardinia would now connect directly to all of the other places that connected to Sardinia, and the place marker of Sardinia would be eliminated from the network entirely. This resulted in a very interesting visualization where the density of network connections almost resembles a polygon.

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Figure 11: Single mode network representation of Pleiades data

Even though I am still figuring out a method to transfer the color of the links from Gephi to QGIS, this type of representation has tremendous potential. If we can class different connections and pull those form the data set, we can begin to represent political areas, land masses, and other groupings as the sum of their shared connections in geographic space. So, instead of drawing arbitrary polygons, it is the connections themselves that create the “area” of a place. If these connections are able to respect underlying geography (roads, mountain passes, navigable rivers, springs, and other features), I think we may have a very powerful way of representing economic regions, areas of social interaction, political control, etc, and explore how those different networks interact and influence each other in geographic space.

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Ancient Itineraries: The Digital Lives of Art History

I am very happy to announce that I have been chosen as a participant for Ancient Itineraries: The Digital Lives of Art History institute, which is supported by the Getty Foundation as part of their Digital Art History initiative.

 

A (VERY!) brief synopsis: The institute will focus on three areas of concern to digital art history: provenance, geographies, and visualization. We will create detailed specifications, assess different methodologies, and create a detailed proof of concept for each of these three areas. The results of this work will be translatable to different project plans and research opportunities at the close of the institute.

 

Given the detailed description of the institute (linked above) and the various specialties and strengths of the organizers, I think this will be a fascinating exploration of the intersection of art history, ancient history, linked data, geospatial research, material culture, and digital humanities. I expect that this institute will not only create outstanding scholarly output, but will serve as the core of a new, robust community of scholars interested in linked data, material culture, and art history.

SNA, Wikipedia, and the Hellenistic World

Part of my work on the Big Ancient Mediterranean project involves creating a general software framework that can display social networks produced with Gephi, either as “stand alone” displays or integrated with geographic and textual information.

I created this particular module, “Hellenistic” Royal Relationships, to highlight the “stand alone” social network analysis (SNA) capabilities of BAM, and to serve as the start of a more generalized Hellenistic prosopography. Some other, more specialized work has been done in this direction; notably Trismegistos Networks and the efforts of SNAP:DRGN to create data standards for describing prosopographies and linking to other projects. Eventually this module will take advantage of these efforts, and provide stable URIs for its own data.

I envision this module serving several purposes. First, it provides an interesting visual representation of data contained within Wikipedia articles, including textual data that is not “linked” to other entries  and therefore not discoverable by automated means. It serves as a quick reference for familial relationships, and provides an entry point for further exploration and study. This project has created a “core” of relationships that can be further expanded by different projects. It also can function as a check on Wikipedia data; some of the relationships here are highly controversial, or could even be wrong.

For future development, the next steps are to add more data on the subjects, including birth / death / reigning dates and a time-line browser based on those dates. As mentioned above, more work needs to be done to take advantage of linked data projects, including linkages to Pleiades locations where appropriate, linkages to Nomisma IDs if the monarch minted coins, and the presentation of the underlying data in a format that is compatible with SNAP:DRGN. Finally, I would like to develop a method for the automatic discovery and extraction of relationships described in Wikipedia articles, which is an interesting, but difficult, problem.