IS428 AY2019-20T1 Assign Ng Kai Ling Bernice

Problem & Motivation

After major events like earthquakes hits St. Himark, Always Safe nuclear power plant suffers damage resulting in a leak of radioactive contamination. Further, a coolant leak sprayed employees’ cars and contaminated them at varying levels. Therefore, the need to help St. Himark’s emergency management team combine data from the government-operated stationary monitors with data from citizen-operated mobile sensors and data visualizations to help them better understand conditions in the city and identify likely locations that will require further monitoring, cleanup, or even evacuation.

With data visualization in place, it can help analyze the following:

• Gives users an overview of radiation levels in the area over a period of time.
• Identifying uncertainties and anomalies in the dataset provided.
• Allow users to identify location of concern and car contamination.

Dataset Transformation

Before proceeding to explain how data is transformed to prepare for data visualization, we were given 3 datasets in csv format as follows:

• MobileSensorReadings.csv: contains radiation level measurements from various mobile sensors over a period of time.
• StaticSensorReadings.csv: contains radiation level measurements from various static sensors placed in specific locations over a period of time.
• StaticSensorLocations.csv: contains location information on where the static sensors are placed at.

In addition to that, shapefiles which are used for creating custom choropleth map were provided.

Removing Redundant Data

However, in the given data (both MobileSensorReadings and StaticSensorReadings), the "Units" column has "cpm" for all the values as shown below which is not in use.

Mobile Sensors Data

Static Sensors Data

Solution: Hence, by using Tableau Prep Builder, the "Units" column are removed by following the steps shown below.

Dataset Import Structure & Process

With the dataset analysis and transformation phase completed, the following files will have to be imported into Tableau for analysis:

• MobileSensorReadings.csv (formatted from data Transformation)
• StaticSensorReadings.csv (formatted from data Transformation)
• StaticSensorLocations.csv (formatted from data Transformation)
• StHimark.shp (shapefile)

Data Connections

• MobileSensorReadings.csv

  MobileSensorReadings.csv is added as a data connection without any further processing.

• StaticSensorLocations.csv

  StaticSensorLocations.csv is added as another source inner joined with StaticSensorReadings.csv. Below are the steps to further process the data (refer to the image show below):

• Import StaticSensorLocations.csv as a data source.
• Drag StaticSensorReadings.csv from the left panel to th top right panel.
• Add inner join between these 2 data, mapping "Sensor-id" from StaticSensorLocations.csv connection to "=" with "Sensor-id" from StaticSensorReadings.csv connection

• StHimark.shp

  StHimark.shp is added as another data source inner joined with the MobileSensorReadings.csv. Below are the steps to further process the data (refer to the image show below):

• Import StHimark.shp as a data source.
• Import another connection using MobileSensorReadings.csv file.
• Add inner join between these 2 data source, mapping "Geometry" from StHimark.shp connection to "Intersects" with "MAKEPOINT([Lat],[Long])" from MobileSensorReadings.csv connection.