Difference between revisions of "ISSS608 2016-17 T3 Assign HUANG LIWEI"

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<td>[[File:Vast1.jpg|250px]]</td>
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<td><font size=5; color="#FFFFFF">&nbsp;&nbsp;VAST Challenge 2017 - Mini Challenge 2</font></td>
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<td><font size=5 color="#FFFFFF">VAST Challenge 2017 - Mini Challenge 2</font></td>
 
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'''Presented by: HUANG Liwei'''&nbsp;&nbsp;([mailto:liwei.huang.2016@mitb.smu.edu.sg liwei.huang.2016@mitb.smu.edu.sg])<br>
 
'''Presented by: HUANG Liwei'''&nbsp;&nbsp;([mailto:liwei.huang.2016@mitb.smu.edu.sg liwei.huang.2016@mitb.smu.edu.sg])<br>
 
Visualization tool: Tableau
 
Visualization tool: Tableau
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[[ISSS608_2016-17_T3_Assign_HUANG_LIWEI|<font color="#FFFFFF">Introduction</font>]]
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[[lw-questions|<font color="#FFFFFF">Questions and Reports</font>]]
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[[lw-preparation|<font color="#FFFFFF">Data Preparations</font>]]
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== Overview ==
 
== Overview ==
 
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Please visit [http://vacommunity.org/VAST+Challenge+2017 VAST Challenge 2017] for more details.
 
Please visit [http://vacommunity.org/VAST+Challenge+2017 VAST Challenge 2017] for more details.
 
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<td valign='top'>&nbsp;&nbsp;[[File:Factory.jpg|500px]]</td>
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<td valign='top'>&nbsp;&nbsp;[[File:Factory2.jpg|500px]]</td>
 
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== Task ==
 
== Task ==
 
The primary job is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).<br>
 
The primary job is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).<br>
 
Please visit [http://vacommunity.org/VAST+Challenge+2017+MC2 Mini Challenge 2] for more details.
 
Please visit [http://vacommunity.org/VAST+Challenge+2017+MC2 Mini Challenge 2] for more details.
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== Questions and Solutions ==
 
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=== Question 1 ===
 
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Characterize the sensors’ performance and operation. Are they all working properly at all times? Can you detect any unexpected behaviors of the sensors through analyzing the readings they capture?Limit your response to no more than 9 images and 1000 words.
 
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<table width=100% border=1><tr bgcolor=#81D4FA><th>'''Visualizations'''</th><th>'''Interpretations'''</th></tr>
 
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<tr><td width=60% align='center'>[[File:Trellis-Reading.png|500px]]</td>
 
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The sensors may not working properly at all times as there are some missing records for particular sensors/chemicals/time periods, which implies that there might be some issue happening during those time points.<br>
 
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For instance, the graph shows the daily reading records of monitor 4 for chemical Methylosmolene in April 2016, revealing several record gaps on day 12th ,17th and 22nd.
 
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<tr><td width=60% align='center'>[[File:Trellis-Count.png|500px]]</td>
 
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Theoretically, each sensor should have reading records in every hour within the monitored month, if we group the records by the hour of day, the count of records in each hour should be the same as the number of days in that month. For example, there are 30 days in April, the count of records by hour of day in April should be 30 as well.<br>
 
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In the graph, we set 30 as the benchmark of record count, if the count is less than 30, there will be represent as a dipped red bar; if the count is more than 30, there will be a spiked green bar instead.<br>
 
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The finding is that for chemical AGOC-3A and Methylosmolene, sensors 3-9 all show some inversion change of counts, which means in some time points, the sensor would likley have additional readings for AGOC-3A and corresponding reading outages for Methylosmolene, which also proves that the sensor may have some issues during the operation such as confounding the chemicals occasionally.
 
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=== Question 2 ===
 
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Now turn your attention to the chemicals themselves. Which chemicals are being detected by the sensor group? What patterns of chemical releases do you see, as being reported in the data? Limit your response to no more than 6 images and 500 words.
 
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<table width=100% border=1><tr bgcolor=#81D4FA><th>'''Visualizations'''</th><th>'''Interpretations'''</th></tr>
 
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<tr><td width=60% align='center'>[[File:Calendar-Days1.png|500px]]<br>[[File:Calendar-Days2.png|500px]]</td>
 
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Some of the chemicals may have a significant increasing in releases as the daily average readings through the days have formed a obvious gradiant from April to December.<br>
 
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The two graphs indicate that the releases of chemical Appluimonia and Chlorodinine are in the trend of growing from April to December.
 
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<tr><td width=60% align='center'>[[File:Calendar-Hours.png|500px]]<br>[[File:Calendar-Hours2.png|500px]]</td>
 
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The releases of chemicals may also have some patterns in a day.<br>
 
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The graphs use the monthly average readings of each chemical as benchmark, then present the percentage of deviation from the benchmark in every hour of the day (average readings). It is evident that chemical AGOC-3A is likely to be released from 6am to 9pm everyday as the readings are much higher beyond the average in that period, on the contrary, chemical Methylosmolene might be released during the late night 10pm to 5am.
 
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Latest revision as of 20:49, 16 July 2017

Lw-VAST.jpg VAST Challenge 2017 - Mini Challenge 2

Presented by: HUANG Liwei  (liwei.huang.2016@mitb.smu.edu.sg)
Visualization tool: Tableau

Introduction

Questions and Reports

Data Preparations

Comments


Overview

Mistford is a mid-size city is located to the southwest of a large nature preserve. The city has a small industrial area with four light-manufacturing endeavors. Mitch Vogel is a post-doc student studying ornithology at Mistford College and has been discovering signs that the number of nesting pairs of the Rose-Crested Blue Pipit, a popular local bird due to its attractive plumage and pleasant songs, is decreasing! The decrease is sufficiently significant that the Pangera Ornithology Conservation Society is sponsoring Mitch to undertake additional studies to identify the possible reasons. Mitch is gaining access to several datasets that may help him in his work, and he has asked you (and your colleagues) as experts in visual analytics to help him analyze these datasets.
The four factories in the industrial area are subjected to higher-than-usual environmental assessment, due to their proximity to both the city and the preserve. Gaseous effluent data from several sampling stations has been collected over several months, along with meteorological data (wind speed and direction), that could help Mitch understand what impact these factories may be having on the Rose-Crested Blue Pipit. These factories are supposed to be quite compliant with recent years’ environmental regulations, but Mitch has his doubts that the actual data has been closely reviewed. Could visual analytics help him understand the real situation?
Please visit VAST Challenge 2017 for more details.

  Factory2.jpg

Task

The primary job is to determine which (if any) of the factories may be contributing to the problems of the Rose-crested Blue Pipit. Often, air sampling analysis deals with a single chemical being emitted by a single factory. In this case, though, there are four factories, potentially each emitting four chemicals, being monitored by nine different sensors. Further, some chemicals being emitted are more hazardous than others. Your task, as supported by visual analytics that you apply, is to detangle the data to help Mitch determine where problems may be. Use visual analytics to analyze the available data and develop responses to the questions below. In addition, prepare a video that shows how you used visual analytics to solve this challenge. Novel visualizations and analysis approaches are especially interesting for this mini-challenge. Please do not use any other data in your work (including other Internet-based sources or other mini-challenge data).
Please visit Mini Challenge 2 for more details.

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