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
	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”.
	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.