Difference between revisions of "IS428 AY2019-20T1 Assign Ngoh Yi Long Tasks"
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+ | ''' 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. 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? ''' | ||
+ | |||
+ | <center> | ||
+ | [[File:Task 4.1 (YL).png|600px|frameless]] <br> | ||
+ | These are highlight tables for both the static and mobile sensors. It shows the maximum value recorded over each minute. As we can see from the whole duration of the data of the given period, we can see the increase in the maximum value of the recorded readings. With the mobile sensor’s highlight table with 2 significant period with high volume of maximum recordings measured. I would say that every time an event occurs, the mobile sensors will be more prompt to pick up the changes in radiation level. However, this might be because we are looking at only 9 static sensors to 50 mobile sensors. <br> | ||
+ | </center> | ||
+ | |||
+ | <br> | ||
+ | |||
+ | ''' Comparison of static and mobile sensor ''' | ||
+ | * Mobility <br> | ||
+ | <center> | ||
+ | [[File:Task 1.3 (YL).png|600px|frameless]] <br> | ||
+ | - Static locations are fixed to one location and might lack to detect radiation over large areas <br> | ||
+ | - Mobile sensors are attached to vehicle which can detect the radiation level of the location as it moves from place to place, allowing it to cover more areas over time <br> | ||
+ | - Mobile sensors can also be submitted by different users which increase the number of readings submitted to people who care about this issue <br> | ||
+ | </center> | ||
+ | * Durability <br> | ||
+ | <center> | ||
+ | [[File:Task 2.5 (YL).png|600px|frameless]] <br> | ||
+ | - Static sensors are seen to have better results over time despite of major events occurring <br> | ||
+ | - Mobile sensors are seen to have missing values which results in block of time frame of missing data which might be crucial to be analysed <br> | ||
+ | </center> | ||
+ | |||
+ | <br> | ||
+ | |||
+ | ''' Usage of both static and mobile sensors ''' | ||
+ | * For mobility wise, the mobile sensor can support the static sensors to cover more areas to detect for radiation | ||
+ | * For durability wise, the static sensors would still return more fruitful readings as compared to the mobile sensors where at least more than half of them have missing periods of data in them | ||
+ | * Having mobile sensors, we can look at vehicles moving along the roads and in case they detect a contaminated car, they are able to detect it earlier | ||
+ | |||
+ | <br> | ||
+ | |||
+ | ''' 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. How do you think this choice affected your analysis? Limit your response to 200 words and 3 images. ''' | ||
+ | |||
+ | <center> | ||
+ | [[File:Task 5 (YL).png|600px|frameless]] <br> | ||
+ | From the diagram above, it would have seemed as I analysed the data as in dynamic stream of data. In any event that occurs, the additional data input would have changed how the graph is populated and allowing the relevant authority to make fast and sensible responds based on the input data coming in. Like the cumulative radiation readings of both static and mobile sensors, when there is a sudden increase or jump in the reading, that would mean that something might have been detected by the respective sensor and the authority can immediately go on the ground to confirm it. This direction that I have analysed the data would not have affected my analysis, in my opinion, I think it was a better way to analyse a stream of data which would allow better detection of anomalies to allow relevant authorities to act on it as quickly as they can. |
Latest revision as of 23:37, 13 October 2019
MC2: St. HiMark Radiation Monitor System
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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.
2. Use visual analytics to represent and analyze uncertainty in the measurement of radiation across the city.
a. Compare uncertainty of the static sensors to the mobile sensors. What anomalies can you see? Are there sensors that are too uncertain to trust?
b. Which regions of the city have greater uncertainty of radiation measurement? Use visual analytics to explain your rationale.
c. What effects do you see in the sensor readings after the earthquake and other major events? What effect do these events have on uncertainty?
3. Given the uncertainty you observed in question 2, are the radiation measurements reliable enough to locate areas of concern?
a. Highlight potential locations of contamination, including the locations of contaminated cars. Should St. Himark officials be worried about contaminated cars moving around the city?
b. Estimate how many cars may have been contaminated when coolant leaked from the Always Safe plant. Use visual analysis of radiation measurements to determine if any have left the area.
c. Indicated where you would deploy more sensors to improve radiation monitoring in the city. Would you recommend more static sensors or more mobile sensors or both? Use your visualization of radiation measurement uncertainty to justify your recommendation.
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. 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?
These are highlight tables for both the static and mobile sensors. It shows the maximum value recorded over each minute. As we can see from the whole duration of the data of the given period, we can see the increase in the maximum value of the recorded readings. With the mobile sensor’s highlight table with 2 significant period with high volume of maximum recordings measured. I would say that every time an event occurs, the mobile sensors will be more prompt to pick up the changes in radiation level. However, this might be because we are looking at only 9 static sensors to 50 mobile sensors.
Comparison of static and mobile sensor
- Mobility
- Static locations are fixed to one location and might lack to detect radiation over large areas
- Mobile sensors are attached to vehicle which can detect the radiation level of the location as it moves from place to place, allowing it to cover more areas over time
- Mobile sensors can also be submitted by different users which increase the number of readings submitted to people who care about this issue
- Durability
- Static sensors are seen to have better results over time despite of major events occurring
- Mobile sensors are seen to have missing values which results in block of time frame of missing data which might be crucial to be analysed
Usage of both static and mobile sensors
- For mobility wise, the mobile sensor can support the static sensors to cover more areas to detect for radiation
- For durability wise, the static sensors would still return more fruitful readings as compared to the mobile sensors where at least more than half of them have missing periods of data in them
- Having mobile sensors, we can look at vehicles moving along the roads and in case they detect a contaminated car, they are able to detect it earlier
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. How do you think this choice affected your analysis? Limit your response to 200 words and 3 images.
From the diagram above, it would have seemed as I analysed the data as in dynamic stream of data. In any event that occurs, the additional data input would have changed how the graph is populated and allowing the relevant authority to make fast and sensible responds based on the input data coming in. Like the cumulative radiation readings of both static and mobile sensors, when there is a sudden increase or jump in the reading, that would mean that something might have been detected by the respective sensor and the authority can immediately go on the ground to confirm it. This direction that I have analysed the data would not have affected my analysis, in my opinion, I think it was a better way to analyse a stream of data which would allow better detection of anomalies to allow relevant authorities to act on it as quickly as they can.