Homework 10

Visualization 1

For this visualization, I decided to visualize the relationship between the high temperature and the wind bearing for each bigfoot case. For the encoding data types, I decided to have the high temperature and the wind bearing be quantitative, as they are both represented by continuous numbers. I decided to use the point mark to create this visualization, as that is how one would create a scatterplot in vega-lite, which is what I wanted to do. I wanted to create a scatterplot because I am visualizing the relationship between two quantitative variables. I also decided to make the points purple to make the points more visually appealing for the viewer. I did not use a color scheme as I felt that it was not relevant to my visualization. I did not perform any data transformations in the analysis side of things for this visualization because I used the raw data and plotted those points accordingly. There is no overlap in data or plots from Homework 9, as I used a different dataset in this homework. I decided to make this visualization more interactive by a tooltip to show the description of the bigfoot sighting associated with that data point. I felt that this makes the visualization more interesting as it provides some context for each datapoint. Since there are a lot of data points, I decided to add some zoom selection functionality to make the points easier to hover over for the tooltip to show up.

Visualization 2

For this visualization, I decided to visualize the amount of bigfoot cases there are by specific year. For the encoding data types, I had the date be temporal because the date is represented by a date value in the dataset. I also had the count aggregation per year be quantitative, as it would be easier to get the correct count with that data type. I decided to use the bar mark to create this visualization, as I want to visualize the count of something. I also decided to make the bars green to make the points more visually appealing for the viewer. I did not use a color scheme as I felt that it was not relevant to my visualization. For data transformations, I did extract the year portion of the date column in my analysis jupyter notebook. There is no overlap in data or plots from Homework 9, as I used a different dataset in this homework. I decided to make this visualization more interactive by a tooltip to show the year and count for each bar, as these aspects can be hard to visualize by looking at the bar chart alone.