Difference between revisions of "IS428 AY2019-20T1 Assign Ngoh Yi Long Tasks"
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With the addition of the scatter plot of the mobile sensor readings mapped by its respective neighbourhood along the time frame, we can see certain areas and time which has an increase in radiation level. We can identify some of the pocket of high recorded readings such as Old Town around the start of April 9 and Wilson Forest around the start of April 10 (which is the central and south-east of the city). <br> | With the addition of the scatter plot of the mobile sensor readings mapped by its respective neighbourhood along the time frame, we can see certain areas and time which has an increase in radiation level. We can identify some of the pocket of high recorded readings such as Old Town around the start of April 9 and Wilson Forest around the start of April 10 (which is the central and south-east of the city). <br> | ||
From both the static and mobile sensor readings, we can also definitely see an increase in radiation level after April 8 (shown by the blue line on the graphs) which could be the cause of the earthquake which may have affected the nuclear power plant to suffer damage and causing an increase in radiation level throughout the city. | From both the static and mobile sensor readings, we can also definitely see an increase in radiation level after April 8 (shown by the blue line on the graphs) which could be the cause of the earthquake which may have affected the nuclear power plant to suffer damage and causing an increase in radiation level throughout the city. | ||
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+ | ''' 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? | ||
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+ | {| class="wikitable" | ||
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+ | |[[File:Task 2.1 (YL).png|400px|frameless]] <br> ''' Static Sensors '''|| | ||
+ | The scatter plot shows the points of each recording over the 5-day time frame. As we can see from the plot above, for sensor-id 15 which is found at the nuclear power plant, there was a time from around April 9 to April 11 there was no radiation level recorded. This could show some uncertainty on the sensor where it was not able to detect any readings for a 2-day period. | ||
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+ | |[[File:PreDashboard 1.png|400px|frameless]] <br> ''' Mobile Sensors ''' || | ||
+ | We have outliers like this for mobile sensor-id 12 with such a high value of 57,345. It is difficult for us not to dismiss the sensor when it returns such an abnormal large value. | ||
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+ | [[File:Task 2.2 (YL).png|400px|frameless]] || | ||
+ | This diagram shows the number of recorded readings for some of the mobile sensors which I have picked out. These 28 mobile sensors have pockets of missing readings along the 5-day period, which creates uncertainty on these sensors to whether are they working in good condition. | ||
+ | Static sensors are having 1 out of 9 with missing recorded values whereas mobile sensors have 28 out of 50 mobile sensors with missing recorded values. Mobile sensors have more than half of its total sensors with missing values which shows more uncertainties over the mobile than the static sensors. It might be due to the data connectivity of the user who are uploading the data. However, this would just cause pour more uncertainty over data being uploaded by users themselves. | ||
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Revision as of 23:11, 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?