Difference between revisions of "IS428 AY2019-20T1 Assign Foo Yong Long RiskAnalysis"

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== Question 3 ==  
 
== Question 3 ==  
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<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd><center>'''Identifying Contamination Under Uncertainty'''</center></font></div>
 
<div style="background: #364558; padding: 15px; font-weight: bold; line-height: 0.3em; text-indent: 0px;font-size:20px"><font face="Arial" color=#fbfcfd><center>'''Identifying Contamination Under Uncertainty'''</center></font></div>
 
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! style="font-weight: bold;background: #536a87;color:#fbfcfd;width: 10%;" | Step
 
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[[File:StaticCombining.png|700px|center]]
 
[[File:StaticCombined.png|900px|center]]
 
 
The static sensor data consist of two files. One of the files contains the longitudes and latitudes of the Sensors while the other file contains the timestamp of the sensors. Both files were combined utilizing an inner join by matching their Sensor IDs.
 
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The column "Value" was removed from both the sensors file as it is redundant. A column "Entity" was created for all data to identify the respective data points."
 
 
"MobileSensor" value in column "Entity" represents the data points belonging to mobile sensors.
 
 
"StaticSensor" value in column "Entity" represents the data points belonging to static sensors.
 
 
"Hospital" value in column "Entity" represents the data points belonging to hospitals.
 
 
"Always Safe Nuclear" value in column "Entity" represents the data points belonging to Always Safe nuclear plant.
 
 
These were used to differentiate the data points when combing the various data into one single data table which will be explained later.
 
 
 
[[File:CombinedClean.png|900px|center]]
 
 
 
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[[File:Union1.png|900px|center]]
 
[[File:Union2.png|900px|center]]
 
[[File:Union3.png|900px|center]]
 
 
Next, we utilize the common field, "Entity",  and the similarity in format and columns to create a union for the different datasets.
 
 
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[[File:finaloutput.png|900px|center]]
 
 
The data is output as an extract and imported into Tableau.
 
 
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Revision as of 17:12, 13 October 2019

Cover.png


OVERVIEW

DATA TRANSFORMATION

RISKS

RECOMMENDATION AND RATIONALE

VISUALIZATION


Question 1

Visualize radiation measurements over time from both static and mobile sensors to identify areas where radiation over background is detected. Characterize changes over time.



Step Description

1

MobileSensorValue.png
MobileSensorValueMap.png
                                                           Mobile Sensors

2

StaticSensorValue.png
                                                           Static Sensors

3

For the visualization of sensors over time, a Gantt chart view was created to analyze the values of radiation of both sensors. In general, places of concern include Old Town, Jade Bridge, and the southeast region which include Terrapin Springs, Wilson Forest and Scenic Vista. The time pills are set to continuous with "minutes" as the variable as the values are measured in counts per minute.

The readings from mobile sensors support Static Sensors, which shows a spike in radiation on areas like Old Town, Jade Bridge, and the southeast region.

Question 2

Use visual analytics to represent and analyze uncertainty in the measurement of radiation across the city. Compare uncertainty of the static sensors to the mobile sensors. What anomalies can you see? Are there sensors that are too uncertain to trust?



Missing Data

Uncertainties

Question 3

Given the uncertainty you observed in question 2, are the radiation measurements reliable enough to locate areas of concern? Highlight potential locations of contamination, including the locations of contaminated cars. Should St. Himark officials be worried about contaminated cars moving around the city?



Identifying Contamination Under Uncertainty
Step Description

1

MissingMobile.png
                                                           Mobile
MissingStatic.png
                                                           Static

There were missing data for both the Static and Mobile Sensors.

2

EarthQuake.png
Sensor9Pickup.png
Sensor10Pickup.png

The earthquake might have happened around 7.10am on 8th April. A bunch of mobile sensors lost connectivity on the morning of 8th April around 7.16 am - 7.30 am. This might have caused a radiation leak at the nuclear plant. Shortly afterward from 1.26 pm to 4.27 pm, Mobile Sensor 9 which is driving beside the nuclear plant (Distance from plant = 0.62 miles), detected an increase in radiation level to 1301. At 5 pm, there was a surge in radiation levels at the entrance of the Jade bridge (Distance from plant = 6.26 miles).

Subsequently, there is an increase in radiation levels among radiation levels from 8th April onwards and more sensors stopped functioning.

class="wikitable" style="background-color:#FFFFFF;" width="100%"
Step Description

1

Static Sensors

The values from static sensors are more stable compread to mobile sensors due to the coverage.

Limitation: Scope of coverage

Static sensors only cover 8 out of 19 areas, missing out on crucial areas such as the southwest region which contain contaminated areas as reported by the mobile sensors.

StaticUncertainity.png

Furthermore, sensors 13 and sensor 15 are too abnormal to trust. Despite having reported high radiation levels after 8th April, these sensors remain stagnant with little or no spike despite being only 2.5 and 0.86 miles respectively from the radiator.

2

Mobile Sensors

Mobile sensors reported greater fluctuation in results as compared to static sensors, with values ranging from 0 to 1525.However, they cover more areas as compared to static sensors which explains the deviation. However, they too present certain risks and limitations despite being able to cover more areas.

Limitation: Scope of coverage

MobileSensorCoverage.png

As seen on the diagram above, the various counts of mobile sensors are denominated by blue. History Trails and Marks are set and you can see that the mobile sensors are clustered greatly into high activity towns such as Weston, Easton ,Southton , NorthTon and West Parton. The only ventured into the south western areas which contained high radiation values during Friday. With a lack of sample size in these areas, there is greater uncertainty of radiation measurement in these areas as values might be influenced by outliers caused by broken sensors.

Limitation: Inconsistency of data

Contanimatedcars.png

Mobile sensors presents greater loss of data compared to static sensors due to a higher chance of wear and tear. Furthermore, since they are stuck onto cars, data connectivity might be loss throughout the journey.

Furthermore, once a car is contaminated, the sensor will only reflect the value of radiation coming from the car and not the surrounding area. This can be seen from the graph above which shows mobile sensors 9,10,20,21,22,24,25,27,28,29 and 45 having lots of missing data or inflated radiation values after the earthquake.