Hi all, here I am again with some updates about my project. This week, I struggled a bit with the creation of a simple line graph and the switch between the first and second window (as I mentioned in my storyboard). Nonetheless, I came up with some code that did the job for the creation of one of my line graphs.

As you can see, when running the code a simple graph is constructed showing the heights of the waves at Emu Park during several timepoints of the day. The idea is to construct these graphs for all sites…

To visualize the data, I make use of the p5 language in javascript, which is quite user friendly. But, since I’m more comfortable with R, I used R to prepare the dataset in a way that I only keep the information necessary to make my sketch. Otherwise, the dataset would be to big which will take more time to load.

To construct the first window of my design, I made use of the mappa.js package which needed to be loaded in html first, before it could be implemented in p5. To set this up, I followed the ‘simple map’…

In previous post, I presented and discussed all my ideas of presenting the data in a way useful for surfers to consult before surfing. In the end, I decided on working with the different sites presented on the map of Australia in combination with the size of the ocean swell as an indicator of which moments during the day it is best to surf at a certain spot, and the height of it waves to indicate the level experience. …

Remember last weeks story about the ocean data collected by waverider buoys? This week I spend some time on visualising its most important dimensions and would like to share those with you. I decided to focus only on those dimension which are important in a surfer’s perspective, since I want to end up with a visualisation, surfers can consult for information about the different surf spots. The table below will already give you an overview on which dimensions are used in which sketch.

Now let’s take a look at the sketches. In the first sketch, I visualised ocean swell (height…

For the course data “Data Visualisation in Data Science”, we were supposed to find ourself a dataset in order to make visualisation of it afterwards. Hence, I ended up looking at this interesting dataset provided by the Australian Ocean Data Network. The data consists of measurements of 52 waverider buoys stationed around the Australian shore. These measurements were started on the 3th of May 1975 and collected every 20 minutes. The last measurement sated in the national wave archive is at the 27th of July 2018. Thus, a time series analysis would be an interesting tool to perform.

Now, what…

For this assignment, we were asked to sketch at least 10 possible visualisations of the Park Dataset, and to describe them. Afterwards a classification of the sketches is done by their shown dimensions. But first, let’s take a look at my sketches.

Data is provided about an amusement park, hosting thousand of visitors each day, consisting the movements of those visitors. The park is most popular on a Sunday, the number of visitors then equaled 7471. On Saturday 6411 people visited the park, and on Friday 3557.

On Friday, a total of 6,010,914 movements were obtained, from all 3557 visitors on that day. By plotting these movements in a simple scatterplot, we can reconstruct the paths within the park.