A Comparison of Geographical Propagation Visualizations

Supplementary Material

V. Peña-Araya, A. Bezerianos, E. Pietriga

CHI '20: Proceedings of the 38th SIGCHI conference on Human Factors in computing systems, pages 223:1--223:14, April 2020

DOI:10.1145/3313831.3376350

Referential images of each visualization

Study Material

The following items link to the files we used to conduct our experiment:

Folder visualizations_and_datasets
To try the visualizations used in the study, open a local server in that folder and then access it with localhost from your browser. For example, with Python, run python3 -m http.server and then open http://localhost:8000/.

Overview of the experiment described to the participants, with examples of the possible answers for each task.
- introduction.html (note: task 'speed' was later renamed to 'arrival', 'distance' to 'Scope' and 'jumps' to 'hops' in the paper)
The three visualizations with the description used to explain them to participants:
- smallmultiples.html
- animation.html
- glyphmap.html
Folder Propagation patterns: 30 for main trails and 10 for training in JSON format. To see them in any of the visualizations, change the variable this_dataset with the name of the dataset and the variable this_loc with the name of the correspondent location. The name of the location is contained in the name of the data set as one of the options: biobio, ica, zacatecas.

Folder Locations data: The maps in GEOJSON for the visualizations. The folder also includes a PDF with a picture of the map, including the underlying network , and the metadata used for the propagation simulation.
Folder datasets_generation:
- data_generation_process.pdf: gives an overview of the process of dataset generation with the functionalities of each Python class.
- requeriments.txt: List of requirements to install in order to execute the jupyter notebooks.
- location.py: contains the class Location that, given a GEOJSON file, creates a location to generate a propagation simulation
- propagationsimulator.py: Given an instance of Location it generates a propagation simulation.
- propagationcountryanimator.py: helper class to create animations for the generated propagation pattern.
- historystochasticdynamics.py: Helper function to record propagation events in an stochastic process.
- sirmodel.py: Implementation of the SIR model.
- Jupiter notebooks to create the locations to use in the study:
- Jupiter notebooks to generate propagation patterns for each of the locations:
Folder experiment:
- questions_training.csv and questions.csv: contain the questions and configurations for the 10 training trials and for the 30 main trials.
- answers.csv: possible answers for each type of task.
- user_configurations.csv: detailed configuration of the order of visualization and dataset config (column question_config).
- user_question_configurations.csv: shows complete configuration of each trial assigned to each user.
- user_answers_ms.csv: answers, self-perceived confidence and time taked for each trial for each user.
- user_postquestionaire.csv: strategies and self-perceived easiness to complete the tasks for each visualization for each participant.
Folder analysis:
- Detailed_CIs.pdf detailed CI values used for our analysis (Completion Time and Error Rate).
- user_answers_ms.csv -> experiment results used for analysis (same as in experiment).
- A set of R scripts to be ran in order for each measure:
  - 1_aggregate_data.R aggregates (common for all measures)
  - files of the type 2-*.R that report means and CIs for all tasks (2-tasks_all_CI_TIME.R, 2-tasks_all_CI_ERROR.R, 2_tasks_all_CONF.R)
  - files of the type 3-*.R that report means and CIs for per-task analysis (3_tasks_separated_CI_TIME.R, 3_tasks_separated_CI_ERROR.R, 3_tasks_separated_CONF.R)
  - CI-Functions-Bonferroni.R includes the analysis functions. Bonferroni corrections were not applied in our analysis.
- Analysis-README.pdf file with further information about task naming.