ISSS608 2016-17 T3 Assign CHEN YINJUE Data Preparation
VAST Challenge 2017 : Help Rose-Crested Blue Pipit
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Dataset Description
The data visualization and analysis will be done based on a raw dataset named ‘Lekagul Sensor Data’ providing information of timestamp, car-id, car-type and gate-name (gates that a vehicle entered/passed by/exited in order). Below is the screenshot of the dataset:
For car-type, when vehicles enter the Preserve, they must proceed through a gate and obtain a pass. The gate categorizes vehicles as follows:
• 2 axle car (or motorcycle)
• 2 axle truck
• 3 axle truck
• 4 axle (and above) truck
• 2 axle bus
• 3 axle bus
Vehicles receiving a pass with a “P” appended to their category are park service vehicles that have access to all parts of the Preserve. Currently, the Preserve vehicles are only 2 axle trucks, so they are designated as “2P” vehicle type.
There are five types of sensors recording data.
• Entrances. All vehicles pass through an Entrance when entering or leaving the Preserve.
• General-gates. All vehicles may pass through these gates. These sensors provide valuable information for the Preserve Rangers trying to understand the flow of traffic through the Preserve.
• Gates. These are gates that prevent general traffic from passing. Preserve Ranger vehicles have tags that allow them to pass through these gates to inspect or perform work on the roadway beyond.
• Ranger-stops. These sensors represent working areas for the Rangers, so you will often see a Ranger-stop sensor at the end of a road managed by a Gate. Some Ranger-stops are in other locations however, so these sensors record all traffic passing by.
• Camping. These sensors record visitors to the Preserve camping areas. Visitors pass by these entering and exiting a campground.
Data Preparation
So, what can we do using this sensor data? Basically, the records of gate names should be combined to create one or multiple entire paths for each car-id so that a sunburst diagram can be drawn to check whether there are particular routes having relatively heavier traffic volumes compared to other paths.
However, there is specific data structure requirements if we want to draw sunburst chart in tableau and hence we need to restructure data first. Therefore, R-studio is applied. Below are the R codes used to reshape our raw data:
After running the code above, we can get the reshaped data as below shows:
Next, the restructured dataset should be loaded into JMP so that the weight (number of records) of each row of ‘Route’ can be computed. To achieve that, after importing dataset into JMP, select: Table > Summary. In the pop-up window, drag ‘Route’ to ‘Group’ and then click ‘OK’ so that we can get the desired weights mentioned above. After that, the original table this new table generated for weights can be joined together. So far, we are ready to do sunburst diagram using tableau. Below demos how the dataset looks like by now:
