IS428 AY2019-20T1 Assign Ngoh Yi Long Data

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MC2: St. HiMark Radiation Monitor System

Overview

Data Preparation

Interactive Visualisation

Tasks

References

Data Provided

The data below are the ones used to create this dashboard.

Dataset Data Attributes
StaticSensorLocations.csv
  • Sensor-id: unique identifier of sensor
  • Lat
  • Long
StaticSensorReadings.csv
  • Timestamp: 5 second intervals
  • Sensor-id
  • Value: measurement of radiation
  • Units: counts per minute
MobileSensorReadings.csv
  • Timestamp: 5 second intervals
  • Sensor-id
  • Value: measurement of radiation
  • Units: counts per minute
  • User-id
StHimarkNeighbourhoodShapefile
  • Shapefile for the city of St. Himark


Data Preparation

Steps Description
Data Prep 1.png

The static sensors location and its reading were split into 2 different csv files. To make sense of the data and better manipulation of it, I used Python Pandas library to merge both files together. As the static sensors and mobile sensors have similar sensor-id, I created a new column of data called “Sensor-type” to be able to differentiate the static and mobile sensors later. After manipulating the data into a merged data frame, I exported it into a new file called “AllStatic.csv” to be used.
I also created the new “Sensor-type” column for the mobile sensors csv file and exported it into a new filed called “AllMobile.csv”.

Data Prep 2.png
Data Prep 3.png

After that, I used the “AllStatic.csv” file and the given StHimark shape file to map the points of each row of data to the neighbourhood by using Tableau’s Makepoint function. I did a left join on the static sensors’ latitude and longitude coordinate and by using the Makepoint function to do an intersect on the Geometry of the shapefile. I extracted the data from these manipulated datasets into a new file called “AllStatic (Geometry).csv”. I also did the same for the mobile sensors file to create “AllMobile (Geometry).csv” so that both of the data now has the point of each sensor reading mapped to its respective neighbourhood.