Difference between revisions of "IS428 AY2019-20T1 Assign Foo Yong Long:R&R"

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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>'''Contamination Control'''</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>'''Contamination Control'''</center></font></div>
  
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[[File:LPXASG15.jpg| 900px |center]]
 
 
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The 8 contaminated cars left St.Himark and have not returned since. To prevent further contamination, the city should attempt to locate the cars as soon as possible.
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[[File:EndState.jpg| 900px |center]]
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Highly likely contaminated cars with sensor IDs 20,21,22,24,25,27,28,29 and 45 has left the country. The government should issue a warning to search for these 9 vehicles.  
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Sensors M2 and M42 are still transmitting high radiation readings, and have been doing so for some time. This could be indicative of a contaminated area/car and should be looked into as well.
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[[File:EndState6Hrs.jpg| 900px |center]]
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The graph above shows the standard deviation and average value of sensor readings for the last 6 hours that are still in the city. Sensor 1,26,35 and 47 has to be checked and replaced as they may be faulty with 0 standard deviations in readings for the last 6 hours. The government will have to Monitor mobile sensor 32 carefully as it has a high variability of sensor readings and its average value is reaching the warning level.
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===Use visual analytics to compare the static sensor network to the mobile sensor network. What are the strengths and weaknesses of each approach? How do they support each other?===
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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>'''Neighbourhoods of Concern'''</center></font></div>
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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>'''Areas with Coverage'''</center></font></div>
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[[File:Mobilesensorcoverage.png| 900px |center]]
  
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[[File:LPXASG16.jpg| 900px |center]]
 
 
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The number of cars going through Chapparal and Terrapin Springs suddenly decreased towards the end of the simulation. There could be infrastructural damage or other reasons leading to this decrease.  
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[[File:DropMobileSensor.png| 900px |center]]
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The mobile sensors are able to cover a wider area. The graph above shows a decrease in overall mobile sensors from Monday to Friday. This can be due to wear and tear and vehicles traveling out of the city.  
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[[File:Wednesday.png| 900px |center]]
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Furthermore, the area of coverage are dependent on the travel routes of the vehicles, which are unpredictable. For example, based on the graph above, there are no mobile sensors around the Safe Town, and thus we have limited coverage of the area around the Nuclear Plant.
  
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===Use visual analytics to compare the static sensor network to the mobile sensor network. What are the strengths and weaknesses of each approach? How do they support each other?===
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Nonetheless, they still provide much better coverage of the city compared to the static sensors.  
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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>'''Areas Lacking Coverage'''</center></font></div>
 
  
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[[File:LPXASG13.jpg| 900px |center]]
 
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! style="font-weight: bold;background: #536a87;color:#fbfcfd;width: 10%;" | No.
 
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! style="font-weight: bold;background: #536a87;color:#fbfcfd;width: 90%;" | Description
 
 
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The 9 static sensors are only cover 7 out of the 19 neighbourhoods, leaving many neighbourhoods in Central and Southeast St.Himark uncovered. The range of the static sensors also do not seem to be sufficient to detect radiation over an entire neighbourhood given their relatively low readings, with the occasional spike.  
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[[File:StaticSensorCoverage.png| 900px |center]]
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The static sensor coverage is weaker compared to Mobile Sensors, covering only 9 out of 19 locations. Furthermore, it might be unreliable at close range distances. However, it is still easier to manage and implement compared to mobile sensors.  
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The mobile sensors provided much better coverage of the city, with the 50 sensors covering pretty much the entire island on a day to day basis. However, in the aftermath of an earthquake or major event (April 9 – 10) the mobility of cars can become restricted due to infrastructure damage. Citizens may also avoid travelling in the aftermath, resulting in lesser ground covered.
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All in all, there is a need to have two types of sensors on the ground. The mobile sensors help to achieve coverage which static sensors can't while static sensors help to make up for the inconsistency that is present in mobile sensors.  
 
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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>'''Visualizing Time Series'''</center></font></div>
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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>'''Analyzing the data as a stream'''</center></font></div>
  
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[[File:LPXASG17.jpg| 900px |center]]
 
 
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In the process of designing the dashboard, I ensured that all the visualizations could be filtered by date. This is so that static analysis can be done on the entire dataset, as well as dynamic analysis by working on a sliding window of data.  
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The dataset given consist of real-time data. It is where analytics is performed on a moving / scrolling data to unconver patterns or insights. As the timeframe given is continuous, the environment will be constantly be changing every second.
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In the process of analyzing and designing the dashboard, I ensure that Timestamp is set to continuous and incorporated into my filters to undergo a more thorough analysis of the data.  
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===How do you think this choice affected your analysis?===
 
===How do you think this choice affected your analysis?===
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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>'''Dynamic versus Static Analysis'''</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>'''Dynamic versus Static Analysis'''</center></font></div>
  
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[[File:LPXASG18.jpg| 900px |center]]
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While most of the analysis could be done dynamically, static analysis is especially helpful when trying to identify clusters or patterns in the data. When viewing a stream of dynamic sensor data, it is very difficult to immediately identify patterns or gaps at a glance. However, when viewed statically, it is possible to sort them in a manner where visible clusters and patterns can be found to make meaningful analysis.  
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[[File:StaticVsDynamic2.png| 900px |center]]
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For example in the graph above, instead of utilizing time-stamp as a dimension, it should be converted to continuous data to uncover missing gaps and find patterns within the time frame.
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! style="font-weight: bold;background: #536a87;color:#fbfcfd;width: 10%;" | No.
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! style="font-weight: bold;background: #536a87;color:#fbfcfd;width: 90%;" | Description
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[[File:StaticVsDynamic1.png| 900px |center]]
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Similarly to the previous example, by analyzing data as a stream of data, I can see the areas being covered at each particular instance instead of having an average count of sensors within a given time frame.  
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Throughout my analysis, all data points have been analyzed as a continuous stream of data.  
 
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Latest revision as of 22:44, 13 October 2019

Cover.png


OVERVIEW

DATA TRANSFORMATION

RISKS

RECOMMENDATION AND RATIONALE

VISUALIZATION


Question 4

Summarize the state of radiation measurements at the end of the available period. Use your novel visualizations and analysis approaches to suggest a course of action for the city.


Contamination Control
No. Description

1

EndState.jpg

Highly likely contaminated cars with sensor IDs 20,21,22,24,25,27,28,29 and 45 has left the country. The government should issue a warning to search for these 9 vehicles.

2

EndState6Hrs.jpg

The graph above shows the standard deviation and average value of sensor readings for the last 6 hours that are still in the city. Sensor 1,26,35 and 47 has to be checked and replaced as they may be faulty with 0 standard deviations in readings for the last 6 hours. The government will have to Monitor mobile sensor 32 carefully as it has a high variability of sensor readings and its average value is reaching the warning level.

Use visual analytics to compare the static sensor network to the mobile sensor network. What are the strengths and weaknesses of each approach? How do they support each other?


Areas with Coverage
Mobilesensorcoverage.png
No. Description

3

DropMobileSensor.png

The mobile sensors are able to cover a wider area. The graph above shows a decrease in overall mobile sensors from Monday to Friday. This can be due to wear and tear and vehicles traveling out of the city.


Wednesday.png

Furthermore, the area of coverage are dependent on the travel routes of the vehicles, which are unpredictable. For example, based on the graph above, there are no mobile sensors around the Safe Town, and thus we have limited coverage of the area around the Nuclear Plant.

Nonetheless, they still provide much better coverage of the city compared to the static sensors.

4

StaticSensorCoverage.png

The static sensor coverage is weaker compared to Mobile Sensors, covering only 9 out of 19 locations. Furthermore, it might be unreliable at close range distances. However, it is still easier to manage and implement compared to mobile sensors.

5

All in all, there is a need to have two types of sensors on the ground. The mobile sensors help to achieve coverage which static sensors can't while static sensors help to make up for the inconsistency that is present in mobile sensors.

Question 5

The data for this challenge can be analyzed either as a static collection or as a dynamic stream of data, as it would occur in a real emergency. Describe how you analyzed the data - as a static collection or a stream.


Analyzing the data as a stream
No. Description

1

The dataset given consist of real-time data. It is where analytics is performed on a moving / scrolling data to unconver patterns or insights. As the timeframe given is continuous, the environment will be constantly be changing every second.

In the process of analyzing and designing the dashboard, I ensure that Timestamp is set to continuous and incorporated into my filters to undergo a more thorough analysis of the data.

How do you think this choice affected your analysis?


Dynamic versus Static Analysis


No. Description

2

StaticVsDynamic2.png

For example in the graph above, instead of utilizing time-stamp as a dimension, it should be converted to continuous data to uncover missing gaps and find patterns within the time frame.

No. Description

3

StaticVsDynamic1.png

Similarly to the previous example, by analyzing data as a stream of data, I can see the areas being covered at each particular instance instead of having an average count of sensors within a given time frame.

Throughout my analysis, all data points have been analyzed as a continuous stream of data.

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