Experiential Information Design

This week I again took lead from the way Molly from medium examined her data set and opted to isolate all users whom had either a 4/5 visual attractiveness rating, to gain better insights into the things I was most attracted to. Interestingly, this whittled my 199 match data set down to a mere 37 users. This was the dataset I used for this: And these were the graphs I created from it: Initially, I found this revised subset of my data, really difficult to wrap my head around as it told quite a different story to that of my other data. But after further examination, I realised it was a lot more telling in revealing things that I am attracted to. I used a count to determine the number of users that had any particular trait or engagement characteristic.  For example, this data clearly showed that I was significantly more attracted to guys whom didn't use pickup lines. And a huge visual trait which I valued was deeming a guys clothing to be s...

This was the data set I am using. https://docs.google.com/spreadsheets/d/1aIvcYOGFxkBH0evEooBnclVGWGc_kqeZoocTlpkkY_k/edit?usp=sharing During my research, I was surprised to learn that this is not the first time someone has examined their tinder swiping habits, with a UX designer from medium having given her data to a professional data analyst. This gave me some insights about how to examine my own data. But also gives me a lot of freedom as she never created any sort of tool to display this. This was really interesting as it inspired me to include a couple of additional fields into my data set, such as key wording to describe my matches personality and style and whether or not I would be likely to match with them again. This week was also all about examining my data to find further insights and correlations, to do this I began creating data and charts which examined the correlations between closely related fields, eg. How I rank a persons attractiveness on a scal...

This week, we had to present our project progress back to the class, I had decided on a topic, using the dataset of all of my tinder matches who either I have engaged with or if they have engaged with me. I am using a mixture of qualitative and quantitative data, things such as hair colour, height, occupation, how attractive I find them on a scale of 1-5 etc. This was a model I used which helped me to determine the type of data I want to collect from people.  I also did some further research into how tinder users are engaging with the app. https://buildfire.com/tinder-statistics/

We were introduced to the project this week and encouraged to find a data set that would challenge us to effectively communicate the data. We were also shown some examples of projects which were effective examples of how last years data sets were employed and we were also given an overview of the differences of qualitative and quantitative data.