Information Visualization
Python/R Code
The Python code to generate the genre tree discussed in this section is available in the file GenreTree_Example.py, and the corresponding interactive Jupyter notebook for this code is available in the file GenreTree_Example.ipynb. The Python code to generate the sunburst plot discussed in this section is available in the file Sunburst_Example.py, and the corresponding interactive Jupyter notebook for this code is available in the file Sunburst_Example.ipynb. All files are included in the distribution for this course. The code to generate the many of the other plots discussed in this section will be presented in subsequent sections.
A complete list of code, data files, and Jupyter Notebooks (where applicable) follows:
Visualizations_Matplotlib_Plotly_Example.py
Jupyter Notebook: Visualizations_Matplotlib_Plotly_Example.ipynb
WordCloud_Example_1.py
SocialRelations_Network_Visualization_Pyvis_Example.py
(Note: This code requires the file SocialRelations_Network_Visualization_Pyvis_Example.csv.) (Note: Weights are slightly different than in the figure in the text.)
SocialNetworks_GIS_Example.py
Jupyter Notebook: SocialNetworks_GIS_Example.ipynb
(Note: This code requires the files capitalCities_LatitudeLongitude.csv and ESC2018_GF.xls.)
GenreTree_Example.py
Jupyter Notebook: GenreTree_Example.ipynb
Sunburst_Example.py
Jupyter Notebook: Sunburst_Example.ipynb
(Note: This code requires the file genresExamples.csv.)
The Concept of Visualization
The digital humanities emerged from text. The field arose from the collaboration of Jesuit scholar Roberto Busa and IBM to produce the Index Thomisticus, a computer-generated concordance of the corpus of Thomas Aquinas. Text encoding, processing, representation, analysis, and interpretation are the main foci of the digital humanities. The Text Encoding Initiative, or TEI, is considered as the most important innovation in the humanities computing, which became the digital humanities (Hockey, 2004). Digital textual archives, hypertext, text mining, are primary concerns of the digital humanities.
One of the most fundamental practices in humanities scholarship is close reading, referring to the thorough analysis and contextualization of a single text. The goal of close reading is to gain deep insights into the text, and to interpret its meaning, “to uncover layers of meaning that
lead to deep comprehension” (Boyles & Scherer, 2012). Close reading analyzes the interactions between individuals, events, and ideas. It is concerned with argument patterns in the text. It also concentrates on specific words and phrases in the text, as well as the text structure and style (Jänicke et al., 2017). Close reading, at least as traditionally practiced, is almost exclusively text-centred and human-centered, and, at least until recently, was not influenced by or in need of computational tools. In the early years of the twenty-first century, however, the concept of distant reading was introduced