Tools for Visualization
By Sarah Pittroff
Canon(s) of Digital Cultures – Data Visualization
01 What is Data Visualization?
What is Data Visualization in the Humanities?
and oftentimes networks:
What is Data in the Humanities?
- (Research) Data
- digitized object data
- moving pictures
- Meta Data
- creation date
- digitazion date
From variables to visuals: What kind of data can be visualized?
table, database, network, geo-spatial, time series, 3D model
text, image, video, sound
-> must be structured to be visualized :D
Structured data (variables) goes to+ visual/graphical form (visuals)
What is data visualization good for?
- Tool for handling amounts of data or information that are either so large or so complex that the human mind cannot oversee them without external tools
- Generate knowledge through explorative analysis from large (unstructured) data corpora
- Finding errors in the data set
- Showing an argument
Reasons to Visualize Scientific Data
Visualization of data in different functionalities for the research process:
I am interested in the data set in general and try to pose undirected questions to the data by different designs of visualization (–> targeted array with different parameters that are changed according to a certain pattern).
I am specifically pursuing an assumption and would like to work out a significance (accumulation) or correlation (context). Perhaps I have worked out a clue by explorative data analysis, which I can now track down with a modified data set in a confirmatory analysis.
Here I present data to a public. The focus is on a very specific aspect, which I would like to present visually within the scientific argumentation. As the term “explanation” already says, an argument, a relationship is explained, but the visualization also has a proving character.
Explorative and confirmatory analysis foster gain in knowledge, explanatory analysis explains this knowledge.
Visualization of information as in different points of view:
- Macroscopic level shows overarching structures (Distant Reading)
- Microscopic level enables detailed views on data sets
Visualization of information is interconnected in the resarch process
- Data Sampling
- Analysis based on algorithm
- Visualization of data
- Hermeneutic interpretation of visualization
These four steps are interdependent phases. They have to be thought of as biderectional dependencies and can’t be imagined as seperated actions. For the sake of a good visualizaton we have to take care of two premises: comparability and consistency of data.
Popular Visualization Techniques
- 1 variable: e.g. bar plot, line plot, histogram, pie plot, radar plot.
- 2 variables: e.g. scatter plot, heatmap, map.
- multiple variables: e.g. scatter plot matrix, mosaic plot, parallel plot.
Good overview on many techniques (starter):
Good overview on many tools (progressiv):
Visualization of Variables
Reference systems and their visual structures:
- Temporal reference is displayed with time series: bar plot, line plot, histogram, heatmap (= techniques)
Raw Graph App and Raw Graph Turtorials
- Spatial reference is displayed via data maps: heatmap, map, radar plot (= techniques)
- Abstract reference is represented by different forms of diagrams (= technique)
- Relational reference is represented by graphs and trees (=techniques)
Visual structures: examples
Number of job advertisements placed in the daily newspapers Passauer Zeitung und Kurier für Niederbayern in the period 1914–1918
John Snow: Spatial Visualization of Cholera Cases in London 1854
Time series / Data Map
Charles Minard: Carte figurative des pertes succecives en hommes de l’Armée Française dans la campagne des Russie 1812-1813
Abstract reference: Diagram
Gantt chart: Results of the Bundestag elections 2017
Briefnetzwerk der „Fruchtbringenden Gesellschaft"
04 Data Hands On
- Roxana Kath, Gary S. Schaal und Sebastian Dumm, „New Visual Hermeneutics“, in: Zeitschrift für germanistische Linguistik 43/1 (01.01.2015). Online: Crossref, DOI: 10.1515/zgl-2015-0002.
- Malte Rehbein, Informationsvisualisierung. in: Digital Humanities: eine Einführung, herausgegeben von Fotis Jannidis, Hubertus Kohle und Malte Rehbein, S. 328-342, Stuttgart 2017.