IS428 AY2019-20T1 Assign Tommy Johnson Visualization

Visualization Analysis on Always Safe nuclear power plant

Problem & Motivation

Data Analysis & Transformation

Interactive Visualization

Observation & Anomalies

The interactive visualization can be accessed here:

Radiation Level by Map Chart

I am using map chart to visualize the radiation measurements for both static and mobile sensors. With this chart, analysts will be able to see which areas are prone to high or low radiation levels from each sensor types. Adding on, they are able to visualize the track that one mobile sensor took throughout the day and how these affect the readings.

Static sensor radiation level

Mobile sensor radiation level

To enhance the visualization of the data, implementing interactive elements would help users in analyzing the data intuitively. The following elements are used in this graph.

Interactive Features	Rationale	Brief Implementation Steps
Highlight Mobile sensor ID	To provide a better insight for the analyst to understand how one particular mobile sensor ID move and how it can affect the readings	Click on the arrow button on the Sensor ID filter and choose "Show Highlighter
Filter dates with the use of checkboxes	To provide flexibility for analysts to choose the dates that they are interested to analyse. They can choose only one or multiple dates.	Drag the Timestamp to the Filter Change the format of the timestamp to custom date Month/Date/Year with Date Part option
Animate the radiation level throughout the day	To allow for greater analysis and aesthetics of the data. Analysts will be able to view the movement of mobile sensor and changes of the static sensor clearly.	Put the Timestamp in Pages section of the Tableau Change the format of the timestamp to Minute with a custom option of Date Value
Trail Mark for Mobile Sensor	To visualize the movement of a particular mobile sensor clearly and analyse which places did it go through throughout the day.	Change the setting of the Timestamp pages as per following image

Readings Level by sensor type

For further breakdown, I visualize how the readings changes over time according to the sensor type.This visualization allows the analyst to look at the pattern of the readings at one glance. to ensure that the distribution is less skewed, i used the logarithm on the value readings.

Static reading level

Mobile reading level

The snapshot below is not limited. Analysts are able to scroll down to view more reading levels from different sensor Id

Major features included	Rationale	Brief Implementation Steps
Differentiate sensor ID by the colour	To provide easy readability and improve on aesthetics.	Drag the sensor ID to the color Marks
Differentiate the timestamp by days	To provide easy readability and improve on aesthetics	Drag the Timestamp in the Columns and custom the date to Month/Date/Year with Date Part option
Log Transformation of the value	Log transformation will make the distribution to be less skewed. This will make the pattern to be more interpretable and inferential statistics are met	Create a calculated field of log value. Then, put it at the Rows