After the wonderful but time-intensive relationship with Twitter in my world history surveys last semester, I decided to take a break from group projects and social media. This semester I gave my students a chance to create their own uncompromising, individual vision for a history project. Using the free, web-based software provided by Tiki-Toki, my 260+ students all “translated” research papers they completed earlier in the semester into digital, interactive timelines available for public consumption. As I stated in my post concerning the Twitter project, I am a huge believer in the capacity for these types of digital projects to empower, inspire, and educate students through the use of such a student-centered approach to learning. Through the creation of these timelines, students learn a greater appreciation of chronology and periodization, the art of constructing a historical narrative fit for “everyman” usage, and training in the usage of a powerful digital toolkit. I’m happy to say it was extremely difficult to narrow it down, but below are the top ten timelines produced by my students this semester. Enjoy!
In an attempt to answer a question frequently asked on Twitter: “What universities have graduate programs in the digital humanities?” I offer up this latest post. As of now, there is no “master list” of universities offering digital humanities courses at either the undergraduate or graduate level, provoking me to produce my own list of such programs. However, as much as I believe my initial attempt to answer this question is a step in the right direction, it heavily relies upon a certain assumption. My undoubtedly incomplete list found below is composed of DH centers, projects, and initiatives at a number of universities in the United States. Considering that these universities have devoted substantial financial and intellectual resources to the study of the digital humanities, I assume that they also are offering courses on the subject as part of their regular curriculum. Certainly @DHcenterNet’s list of digital humanities centers is a good place to start, but at the time of this post, the following serves as a more comprehensive list (please feel free to email me at firstname.lastname@example.org to add additional universities to this list or to correct any of the information found below):
Alabama Digital Humanities Center at the University of Alabama
Baker-Nord Center for the Humanities at Case Western Reserve University
CATH: Center for Applied Technologies in the Humanities at Virginia Tech University
CDH: Center for Digital Humanities at the University of South Carolina
CDRH: Center for Digital Research in the Humanities at the University of Nebraska-Lincoln
Center for Digital Humanities and Culture at the Indiana University of Pennsylvania
Center for Digital Humanities at Brock University
Center for Digital Scholarship at Brown University
Center for Scholarly Communication & Digital Curation at Northwestern University
Centre for Oral History and Digital Storytelling at Concordia University
CHNM: Center for History and New Media at George Mason University
CMS: Comparative Media Studies at Massachusetts Institute of Technology
CPHDH: Center for Public History + Digital Humanities at Cleveland State University
CTSDH: Center for Textual Studies and Digital Humanities at Loyola University
CUNY Digital Humanities Initiative (all 24 CUNY campuses)
DHC: Digital Humanities Center at Columbia University
DHI: Digital Humanities Initiative at Hamilton College
Digital History Lab at California State University-San Marcos
Digital History Lab at Harvard University
Digital History Lab at Princeton University
Digital History Lab at the University of Massachusetts-Amherst
Digital Humanities 2.0 Collaborative at the University of Minnesota
Digital Humanities Initiative at Hamilton College
Digital Innovation Lab at the University of North Carolina
Digital Scholarship Commons at Emory University
Digital Scholarship Lab at the University of Richmond
Digital Studio for Public Humanities at the University of Iowa
DSC: Digital Scholarship Center at the University of Oregon
HASTAC: Humanities, Arts, Science, and Technology Advanced Collaboratory at Duke University
Humanities Research Center at Rice University
Humanities Technology and Research Support Center at Brigham Young University
I-CHASS: Institute for Computing in the Humanities, Arts, and Social Science at the University of Illinois
IDAH: Institute for Digital Research in the Humanities at the University of Kansas
IDHMC: Initiative for Digital Humanities, Media, and Culture at Texas A&M University
Institute for Advanced Technology in the Humanities at the University of Virginia
IRIS: Interdisciplinary Research and Informatics Scholarship Center at Southern Illinois University
MATRIX: Center for Humane, Arts, Letters, and Sciences Online at Michigan State University
MITH: Maryland Institute for Technology in the Humanities at the University of Maryland
Research in Computing for the Humanities at the University of Kentucky
SJCDH: South Jersey Center for Digital Humanities at Stockton College
Spatial History Project at Stanford University
Virginia Center for Digital History at the University of Virginia
Wired Humanities Project at the University of Oregon
For this post, I’m going to provide step-by-step instruction for those of you interested in creating network graphs using Gephi. Certainly there is other open-source software available for visualizing social network and textual data such as Pajek (this website could use a serious design update), but at the time of this post, Gephi 0.8.2-beta has some significant advantages. Software such as Pajek allows you to save your project file as a .bmp, .png, or .svg, but Gephi allows you to save your graph image as a .pdf.
Additionally, Gephi’s most significant advantage over the competition comes from the inclusion of the sigma.js plugin, which uses the HTML canvas element to display static graphs like those generated in Gephi. This is a massive leap forward for sharing graphs generated in Gephi, as now they can be uploaded directly to your server/website using an FTP file manager such as FileZilla. To interact with the graph rather than view it as a static image used to require downloading the specific, proprietary program file containing the graph from its creator, then downloading the specific software to open the file. However, with the sigma.js plugin, interactive graphs can be displayed and shared instantly via a simple web address.
To begin the process of creating and sharing your own network graph using Gephi, I’ll break the process into a series of simple steps. I think these instructions will be useful to those of you starting out, as a simple, step-by-step “Gephi for Dummies” manual simply doesn’t exist at the time of this post, something that I wish I had when I first started working with the software. Gephi has a “Quick Start” guide here which is worth a look, but it leaves much to be desired as a basic guide for a novice user. The following instructions which I’ve created owe much to the wisdom and experience of Jason Heppler and Rebecca Wingo, graduate school colleagues who provided a lot of assistance during my trial and error process of figuring out Gephi’s software.
1. Download and install Gephi from their website.
2. To download and install the Sigma.js plugin, open Gephi. Click on the “Tools” tab and click “plugins” from the drop-down menu.
3. Click on the “Available Plugins” tab and scroll down nearly to the bottom of the list to find the “Sigma Exporter” plugin.
4. Click the check box next to the “Sigma Exporter” plugin, then click the “Install” button at the bottom left corner of the window. (See screenshot below.)
5. Once the plugin is downloaded and installed, close and re-open Gephi to complete the plugin installation.
6. Now it’s time to format your data for importation into Gephi. Using Microsoft Excel, create a two column data set. The specific format for the data needs to be divided into one column as SOURCE and the second column as TARGET. (See example screenshot below).
7. The SOURCE column on the left determines the number of nodes your graph will contain and the TARGET column will determine the number of edges (or connections) between nodes. Repeat the node-edge/source-target pattern in these two columns for each connection between nodes you wish to visualize.
8. Once you’ve entered in (or hopefully imported) your data and saved it as a .csv file, you’re ready to import the file into Gephi.
9. Open Gephi, click the “File” tab, then click “Open” from the drop-down menu. Browse for your .csv file, and click the open button at the bottom of the window.
10. This will open the “Import Report” window. Make sure the “Create Missing Nodes” box is clicked, then hit OK. (See screenshot below.)
11. To label your nodes in the graph, click on the “Data Laboratory” tab.
12. At the bottom of the screen, click the “Copy data to other column” button, then select “ID” from the drop-down menu. (See screenshot below.)
13. In the pop-up box, select “Label,” then OK. (See screenshot below.)
14. Now click on the “Overview” tab to tinker with your graph’s spatial/visual layout.
15. Click on the “Choose a layout” tab on the lower-left part of the window to determine how you want to display your nodes and edges. A popular choice is either of the “ForceAtlas” templates, but I’d recommend tweaking the value of the “gravity” input to expand/contract the spread of your nodes to your liking.
16. At this point, you should be able to see your network graph on the “Overview” page and can choose to export your graph as a .pdf or as a Sigma.js template. Click the “File” tab, scroll down to “Export” and select your preferred format for exportation. If you choose to export using the Sigma.js template, Gephi will create a folder containing files ready to be uploaded to your server/webpage. If you want to view the graph in a browser, click on the “index” file in the folder.
17. (ADVANCED) For those of you who wish to further tweak your visualization, you can customize your node colors and sizes by linking them to particular data attributes.
18. This is accomplished using the statistical analysis options on the right side of the “Overview” interface. (See screenshot below.)
19. To color code your nodes by cluster and to emphasize the significance of particular nodes via node size, you’ll need to run at least one of several statistical analyses, which are located on the right side of the screen.
20. I found that running “Avg. Path Length” under the “Edge Overview” tab to be particularly helpful in visualizing relationships between nodes. This statistical analysis generates new metrics such as “betweenness centrality,” which, when tied to node size, visually emphasizes the more significant nodes in the graph. (See screenshot below.)
21. To link “betweenness centrality” to node size, click the red jewel icon on the upper-left-hand side which selects the size/weight attribute for each node, then click the “Choose a rank parameter” tab on the upper-left side and select “betweenness centrality” as the attribute you wish to link to size/weight. (See screenshot below.)
22. You can link any of the attributes from the “Choose a rank parameter” tab to node color, size/weight, and labels, so there’s a great deal of customization options available to you.
23. Once you’ve established the link between your nodes and your attribute of choice, you can adjust the min/max size and color of your nodes to further customize your graph.
24. This is as far as I’ve gone with Gephi, so I’ll end here with a couple final troubleshooting tips after you’ve exported using the Sigma.js template.
One issue I had during my first project was that in Overview, my labels and custom node sizes showed up fine, but upon exporting the graph, all the nodes were the same, tiny size and had no labels.
To fix this particular issue and/or to customize which nodes are labelled, you’ll need to open the folder created by the Sigma.js template and then open the “config.json” file with Wordpad or an .xml editor. (See screenshot below.)
Once you’ve got the file open, scroll down to the lines “maxNodeSize” and “minNodeSize,” change the values until your nodes are large enough, save the file, and re-open the “index” file in your browser. (See screenshot below.)
To fix the label issue, lower the “labelThreshold” value in the config.json file to assign labels only to nodes equal to or above a certain weight. Again, save the file once these changes are made, and re-open your “index” file in your browser to view the results.
My first project using Gephi resulted in a network graph created using 33 nodes and 213 edges. This was just a test run using the names of several prominent political figures from twentieth-century Mexican history, so my data set doesn’t actually analyze anything (however, I have plans, big plans for the near future.)
This screenshot is equivalent to the level of interactivity you can gain from viewing the graph as a .pdf, a static image. However, with the aid of the Gephi.js plugin, you can view a fully interactive version of the graph here, complete with clickable nodes containing a variety of attribute data.
I hope you find this tutorial useful, and I look forward to seeing your future projects using Gephi. Stay tuned for more mapping and network graph projects I’m churning out this fall, and feel free to contact me at email@example.com if you have any questions.
The next step in my mapmaking project on Mexico City during Uruchurtu’s tenure as regent from 1952-1966 was the addition a new data set focused on the construction of new public markets not only within the confines of the Federal District, but within the greater Mexico City metropolitan area as well. Before examining the processes by which I created this latest layer of data, a bit of historical background should be helpful. From 1953-1966, Uruchurtu and the DDF were responsible for the construction of 172 new markets containing over 52,000 individual vendor stalls at an estimated cost of more than half a billion pesos. Precise numbers for construction, renovation, and maintenance costs are very difficult to obtain from existing archival sources. However, DDF records do show that from 1953-1958, the city spent 350 million pesos renovating existing markets or constructing new ones, representing almost 8.5 percent of the total expenditures by the DDF during this period. The financial gains by the modernization of commerce in these new markets were insignificant, and such massive expenditures for the city treasury instead served as state propaganda and a guarantee of support from a new political interest group composed of comerciantes en pequeño (petty merchants).
This newly formed economic and political covenant with the Frente Unico de Locatarios y Comerciantes en Pequeño del D.F. would help to reverse the PRI’s political fortunes in Mexico City, helping to boost support for the ruling party among the working classes. The openings of major markets such as La Merced, Jamaica, and Tepito were highly touted political events showcasing the commitment of the Revolution to bringing about economic equality for all. In October 1957, the opening of a massive market containing 4,488 vendor stalls in the barrio of Tepito was attended by thousands of vendors, members of Congress, senior ministers, Uruchurtu and department chiefs within the DDF, and even President Adolfo Ruiz Cortines, reportedly the first Mexican president to ever step foot, let alone hold a major political event, in this neighborhood notorious for its poverty and street crime.
However, beyond the political gains which resulted from the construction of new, modern marketplaces, Uruchurtu sought to eliminate the disease, crime, and immorality which city officials associated with the “market days” or tianguis which had been a part of Mexico City’s economic and social traditions since the time of the Aztecs. These chaotic, unregulated markets were portrayed as being rife with pickpockets, dealt in black market goods, sickened residents through the sale of spoiled food, and corrupted the morality of the populace through the sale of cheap alcoholic beverages. The commercial activity created by these marketplaces spilled out of plazas into the surrounding streets of the city. Pedestrians and large trucks continually flowed past sidewalks filled with vendors’ stalls, slowing traffic to a crawl.
This “cork” on the flow of buses, sanitation crews, and general commerce reportedly affected more than 530,000 square meters of the urban landscape, and according to the DDF, was analogous to an “ever-growing cancer” on the city. The destruction of these old markets and the containment of petty merchants within new, modern market buildings was a priority for the well-being of the city and its residents. Clean, modern markets complete with electric lighting, refrigeration, ventilation, an open, spacious design, childcare for vendors, and police surveillance could help to sanitize and decongest the city streets while at the same time improve the physical health of urban residents by providing them with fresh, healthy food and a safe, moral environment in which to shop for the basic necessities of life.
My data set on the period from 1953-1964 contains information on 129 public markets within the metropolitan area, for which I included the names, the number of stalls, and the latitude/longitude coordinates of each market. As with the information on public lighting, this data came directly from the DDF’s own records at the Archivo Historico del Distrito Federal (AHDF) in Mexico City which I compiled during my dissertation research in the fall of 2011. However, in my attempts to locate and verify the address information provided by the DDF, a fair amount of detective work was required. The DDF data only provided the cross streets for each market’s address, a method which works fine for an address listing like Wagner y Mozart due to the unique street names. However, the ease of geographically locating a market can be dramatically different with a listing like Constitucion y Jalisco, for which there might be four or five streets named Constitucion as well as Jalisco located throughout the city.
Additionally, in several cases, the address listing provided by the DDF (and sometimes the geographic marker provided by Google Maps, if there was one) was incorrect by entire city blocks. This led me to walk the city streets using Street View in Google Maps, doing some detective work until I located the market around a corner or down the street several blocks. This ability to virtually explore the city via Google was invaluable in making my map as accurate as possible and it really is a technological marvel that I can track down a public market in Mexico City from my computer at home here in Fort Collins. What would normally take a physical visit to the city and possibly asking locals for directions now can be done in minutes on a computer thousands of miles away. But I gush too much about my appreciation of all things Google.
As opposed to the previous layer of data on public lighting which was composed of both linear and polygon overlays onto Google Earth, I created three separate maps each capable of representing the data in a unique way. For the first map, rendered on Google Earth, I imported my data set created in Excel into Google Fusion Tables, which then allowed me to create a .kml file which can be downloaded here. A simple upload of the .kml file to Google Earth, and the new layer was added on top of the existing layer on public lighting. This simple layer of pushpins is not very visually telling in itself, but the great thing about Google Earth is the ability for users to attach photos, videos, and links to other sites to each marked location on the map.
For La Merced, one of the more famous and grandiose markets in Mexico City from this time period, I attached an image of the market’s interior just prior to its opening in the fall of 1957. Hypothetically, a group of users could collaboratively attach media and links to further information on any marked location on this map, creating an interactive, historical atlas of the city which visitors could explore location by location or via tours along preselected routes. This interactive capacity is a big selling point for Google Earth as a means of conveying a variety of information on historical locations and its ease of use makes it possible for just about anyone to create a map quickly.
For the second map, I made the map pictured below using Tableau Public. As I can’t embed html directly into this blog, please click here to explore the interactive version of this map. For this visualization, I’ve linked the number of stalls attribute directly to the size of the bubble, moving beyond the simple pushpin display found on Google Maps/Earth. This allows the viewer to see the relative size of each market based on vendor capacity and thus get a sense of market concentration in various parts of the city.
Lastly, to further analyze market concentration in various parts of the city, I used CartoDB to generate this map. Again, please do click the link to see the interactive version. This intensity map was created on my favorite basemap template, GMaps Dark (which I dearly wish other map programs had available), and uses three thermal rings surrounding each market location to emphasize the physical concentration of markets in various parts of the city. It’s not quite as striking as a true choropleth map, but I just don’t have the data sets to do something like that for Mexico City.
The process of creating these three maps has been an incredible learning experience for me both technically and as a scholar, and I hope the information conveyed helps to further illuminate this period of the city’s history. My next layer of mapped data will likely be on new school construction, but first I plan to take a small break from maps to create my first network graph on Gephi. Stay tuned for that graph in the very near future and thanks to everyone for the support, advice, and encouragement during this series of projects.
For my initial mapmaking projects on Mexico City during Uruchurtu’s tenure as regent, I plan to focus on the construction of new public works throughout the growing metropolis. As an aside, in my descriptions of this project as well as in future projects, will I use the terms “Mexico City” and the “Federal District” interchangeably, as both terms are commonly used to describe the geographic territory composed of the actual capital and its twelve surrounding delegations (administrative divisions). The greater metropolitan area composed of numerous municipalities adjacent to the Federal District remained outside the domain of city leaders and was administered by corresponding state governments. This first project will focus on the construction of new street lamps for the city, part of Uruchurtu’s plan to modernize and moralize the urban landscape.
During Uruchurtu’s first two terms as regent from 1952-1964, over 81,000 mercury lamps and over 35,000 incandescent lamps were installed throughout the metropolis, actions which purportedly turned Mexico City into “one of the best illuminated cities in the world.” The geographic placement of these two basic types of lamps revealed the dual function which illumination could serve for the production of power within the spaces of the city. In the wealthier colonias near the city center, DDF engineers installed 250 watt, mercury “colonial-style lanterns” which sought to “add a touch of bygone elegance, suitable to this part of the city which is a product of Mexico’s illustrious past.” Uruchurtu and the DDF used the installation of such softly lit, ornate street lamps to reveal the beauty of this sector of the city, the tree lined sidewalks and colonial architecture of these older neighborhoods symbolic of an idealized cultural past. Near the Centro Histórico, structures inscribed with cultural and nationalistic meanings such as the Catedral Metropolitana, the Palacio Nacional, and the Plaza de la Constitución were especially well illuminated. These ornately illuminated religious and civic temples were capable of inspiring intense loyalty to the imagined community of la patria and served as powerful political instruments for the ruling party.
In contrast, for the colonias proletarias, the DDF installed extremely tall, 400 watt incandescent lamps which cast a wide arc of intensely bright light. These modern, industrial looking street lamps were not designed to illuminate the beauty of the neighborhoods they were installed in. Instead, they were intended to penetrate the dark spaces within working class neighborhoods, areas which were considered a breeding ground for immorality. Within the rhetoric of urban planners, the electrification of these “modest zones” of the city went hand in hand with modernization, the safety and security they provided a “necessary requirement for any modern city.” In early 1955, Uruchurtu ordered the Department of Public Works to cooperate with the DDF police in identifying gaps in the illumination of the colonias proletarias, with the regent’s focus primarily on the security aspect of lighting for the city. The electrical illumination of these neighborhoods increased the visibility of residents to police patrols, thus discouraging the potential criminal activities of residents through an expansion of the state’s surveillant gaze.
The map displays major roadways and neighborhoods with mercury lamps in blue and the incandescent lamps in red. To create this map, I compiled data from the public works archives of the Department of the Federal District (DDF) which provided street names and the types of lights, but not the exact total of lights attributed to each street. The DDF data provided the total number of street lights constructed during this time period, but no data from year to year or month to month. However, as much as this mapmaking project is incomplete in some respects, it is a starting point for further investigation and just one of many layers I hope to overlay on top of the city’s landscape in order to partially recreate Mexico City during this formative time period. To view a more interactive version of this map (something which I highly recommend), you can directly download the .kml file to be opened with Google Earth here. Stay tuned over the next week or two for another project on market construction.
I’ve been spending a lot of time lately tinkering with potential mapmaking tools, looking at existing projects, and getting frustrated with my lack of knowledge on the developer/GIS side of things. Modest Maps, Polymaps, and R can produce some excellent results, but they all require a basic understanding of programming in order to use them properly, a skill which I simply don’t have time to learn during the remainder of the summer. ArcGIS on the other hand, the industry standard for professional cartographers, doesn’t require any programming knowledge, but its interface is incredibly complex and requires a great deal of training to understand. Therefore, I’m going to stick with using some simple mapmaking tools for my first few cartography projects.
There are a number of intuitive mapmaking programs worth mentioning which are capable of quickly producing high quality results, so picking the easiest/best one can be difficult. Tableau, Many Eyes, GeoCommons, Indiemapper, CartoDB, and TileMill can all be used to build maps by uploading layers from a wide variety of data sources. However, the simplicity of use and the customization options vary greatly among these programs. Tableau is one of the more versatile and highly customizable tools, but it is extraordinarily expensive. The personal edition and professional edition cost $999 and $1,999, respectively, at the time of this post. However, there is a “public edition” of Tableau available for free, but just like Many Eyes, users are required to upload their work and cannot keep it private/unlisted. GeoCommons, Indiemapper, CartoDB, TileMill, and Many Eyes are all free, but each mapmaking tool has its own set of strengths and weaknesses in visualizing certain kinds of data sets. I’d recommend giving many of these sites a quick glance to see which program works best for your particular project, the right program being largely dependent on the complexity of the data set you are trying to visualize.
For now, Google’s toolset should work just fine to get me started. Google just recently granted access to Google Maps Engine Lite, which allows you to create clickable layers (a much needed feature unavailable in Google Maps), but limits the size of the data sets to 100 rows. Unfortunately, nearly all the projects I have in mind are much, much larger than 100 rows. Therefore, to visualize the street lighting data, I plan on exporting my data from Google Maps into Google Earth as a .kml file, a task which isn’t as simple as a click of a button, but not hard to learn how to do. From Google Maps, just click on the create a link button, which gives you a copy/pastable url for your map. Paste that url into an empty address bar and type (without the quotation marks) “&output=kml” at the end of your url. Hit enter and that should create a downloadable .kml file which you can then open with Google Earth. So far, my data looks great superimposed over Google’s 2013 map of Mexico City, giving the viewer a good sense of the rapid growth of the city over the last 50+ years. I should be able to finally publish the data in the very near future, so stay tuned.