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Feedback
It is important to receive feedback for every project. When you are working on a project, chances are that you will get a tunnel vision: You are so involved in the project that other ideas are less visible. By having someone else look at your work you can improve your project.
Jump to feedback from:
- Feedback Concept - Peer review
- Feedback Repository - Peer review
- Feedback Visualization - Client
- Feedback Functional Patterns & Frontend Data - Peer Review
The first feedback moment was about the total concept that I would eventually deliver to the client. This feedback was provided by two fellow students: Jordy Fronik and Lars. First I will describe what kind of feedback Jordy has given me.
Jordy gave tips including: a new sub-question and how I could eventually make my visualization. In addition to these tips, he noted that I had nowhere indicated which data sets from the RDW I wanted to use and which values I would like to get from those data sets. He gave me another link as an external link that I could use in addition to the data sets of the RDW. This was very helpful! In this feedback issue on GitHub, my teacher Laurens also commented on my main question: he felt that I tackled the question in a complex way. He gave a tip to open up the question a little more openly.
Lars then also agreed with the points that Jordy and Laurens had also told. In addition, he gave some ideas for sub-questions that could also be important for this visualization.
Based on this feedback, I adjusted my main question and looked at the datasets that I wanted and could use. After this I changed my main question to: What about parking an electric car compared to parking as a disabled person in the Netherlands? to make it more open yet more interesting. In addition, I described in my ReadMe that I will be using two datasets for the visualizations:
- Specifications parking area in the Netherlands
- Location of parking garages in the Netherlands
The second feedback moment was about my repository given by a fellow student. In my case this happened to be Jordy again who this time gave a number of points on the basis of a peer review form. He gave as feedback:
- In the readme I miss some sketches of the datavisualization you want to create.
- Be aware that there is a .json in your .gitignore.
- Try to add more comments to your code, this will make it easier for an outsider to read.
- Try to describe what the code does in your wiki.
- Try to call everything inside one function to clean up the eye colors.
Based on this feedback, I extracted my .json from the .gitignore, posted more comments in my code and described my process more in the wiki.
In order to give the client an idea of what has already been thought up, a presentation moment was scheduled to ask what could be done better / differently. I had a feedback moment with Xander van Uffelen on my visualization.
He gave a keen insight into my idea of comparing disabled parking spaces with electric parking spaces. He wrote:
"Comparing is great fun, but be careful. Can you make a fair comparison? Find enough information. Create different charts."
Of course he explained this during the presentation. He wondered whether the comparison (for example more electric parking spaces compared to fewer disabled parking spaces) is fair. Perhaps there are more people with an electric car in the Netherlands than there are people with disabilities. I could solve this by researching the number of disabled people in the Netherlands and the number of electric cars in the Netherlands. This way you can make a more fair comparison.
I could also solve this by thinking more easily: not making a comparison, but taking a step back. This could be a good option because the chances of finding an answer to the equation are very small.
He also indicated that it is better to show all visualizations as a kind of 'spread' instead of putting everything in 1 visualization.
Based on this feedback, I have therefore decided to adjust my research question by making it simpler. This seemed more feasible to me in the short time we have to make all visualizations for this semester. In addition, I will work with his feedback to make several small visualizations to tell the story.
To ensure that the first official assessment (which was also a double assessment) was passed, feedback was given by a fellow student on the work done one day before the assessment. This feedback was provided by Sergio Eijben who looked at my README, code and wiki using a peer review form.
In short, he thought my work looked good so far, but I missed a few small things to pass the course. I have listed these points below:
- The README does not yet contain an image / movie of the visualization.
- There is no link to the preview of the visualization in the README.
- The concept is not fully described in the README.
- There is no sketch of the visualization available.
- It has not yet been fully described what happened with, for example, the unknown data.
- There is no example of the object before and after the data cleaning.
- The update pattern has not yet been described.
- What you have learned has not yet been fully described.
- No interaction can be seen in the visualization yet.
Based on this feedback, this repository has been updated and thus improved. For example, I have adjusted every point, except for sketching the chart and typing out certain studies, in my repository for the moment of delivery.
©️ Veerle Prins, 2020.
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