Discussion points:
- Current idea to display major airport hubs in the US is not a deep insight from the dataset
- How could we supplement the dataset to answer a more insightful question with our visualisation?
- Maps are a heavily overused component with aviation data
- Another option is to take an exploratory approach and focus on transitions between visual components
- The simple road is: focus on doing something fancy with a map
- Exploratory road: asking the question of what we can achieve with aviation data without a map
- General consensus is that the exploratory approach might be more interesting
Action items for next week:
- Come up with ideas for doing a visualisation with no map
- Come up with more interesting questions that an exploratory approach might answer
- By next week, decide what approach to take for the project
Discussion points:
- Emphasised that we have a delay dataset
- The visualisation will not focus on predictions for the future
- The visualisation basics are two components, the main map view with origin and destination selection and the airline view
Action items for next week:
- Wireframe design of the minimum visualisation with data, with the framework of choice and runnable in browser
- Upload to GitHub
Discussion points:
- single screen view idea: map moves between halves of screen, explore this idea, destination and origin side by side when selected
- easy change of airports once selection is made: Dropdown or search to change airport once airports are selected
- Encoding information: lighter view, when all dots on screen encoding information not useful
- remove irrelevant airports compeltely once origin is selected
Action items for next week:
-
integrate map and airline view into one page: try out single page view with origin airport view appearing on left side when origin selected
-
add average delay to routes serviced (data)
-
Finalize table of delays
-
Improve the map view (larger, more visualy appealing)
Discussion point:
- Option B more informative
- Avoid scrolling as much as possible
- Perhaps adding a toggle button to switch between different views
- Report should honestly report what is in the demo, even if the demo is not of high quality
Action items for next week:
- Focus on functional aspects, make sure most of functional work is finished
- Leave visual appeal for week after
First instinct about user story is usually correct: rely on intuition
3 min video for 1. April: contain/highlight every function of the airline Introduce components on screen Pose a question: anwser it by clicking on tabs Come to conclusion about components: explain why your components make sense
Slides: Screenshots of every component with explanation and arrows
Report: Scientific report, explain why choose visualizations, why color, why layout, why chart type
For next week:
prepare main component for feedback, along with the video outline (structure, , references to theory as explaining design choices, introduce all components in order)
Make tool public to improve understanding, deploy on free hosting
This repo contains the first assignment for the course Information Visualization. Students need to create one visualization using html/js/d3 and one visualization using the python package Bokeh.
- Install docker via: https://docs.docker.com/engine/install/
- Run "docker-compose build"
- Run "docker-compose up" to start the docker container you just build
- Navigate to localhost:5000 to access the app
Note: you do not have to rebuild on every change you make. Just save changes to the code and refresh the page.
See the requirements.txt file You can automatically install all the requirements by running: pip install -r requirements.txt
You can get the app to run in your local browser by following the steps below.
- The app can be started by running: bash start_app.sh
- The app can then be accessed by navigating to http://127.0.0.1:5000/
- Type the following in your terminal when using windows CMD: set FLASK_ENV=development OR when using windows powershell: $env:FLASK_ENV=development OR conda env config vars set FLASK_ENV=development (when using anaconda powershell)
- Followed by: python run.py
- The app can then be accessed by navigating to http://127.0.0.1:5000/