ISSS608 2016-17 T3 Assign HUANG LIWEI

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.

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.

Questions and Solutions

Question 1

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.

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. 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.

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. 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. 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.

Question 2

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.

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. The two graphs indicate that the releases of chemical Appluimonia and Chlorodinine are in the trend of growing from April to December.

The releases of chemicals may also have some patterns in a day. 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.

Question 3

Which factories are responsible for which chemical releases? Carefully describe how you determined this using all the data you have available. For the factories you identified, describe any observed patterns of operation revealed in the data. Limit your response to no more than 8 images and 1000 words.

This map graph shows the locations of all monitors and factories.

	The wind speed fluctuates though the hours. As the severe wind would impact the accuracy of the sensor, the days and hours with the mild wind would likely provide relatively convincible readings. As for instance, 14th to 18th of April would be appropriate for detecting the chemical release with sensor readings.
With a line chart focusing on the targeted period, we may have a close look of the sensor readings. It is staightforward to detect the group spikes of chemical AGOC-3A among monitors 3-6 in 17th April. Our next step is to find out which factory may be the main reason that causes the spikes with the reference of wind direction.

Comments

Comment 1:

Overall, very interesting analysis and easy to understand visuals.

Please explore if you can improve on both aesthetics and clarity by having larger graphs for 2nd and last figures. Do consider light shades of grey for your non-graphic portions.

From: Chua Gim Hong

Comment 2:

Well-done! I like your representation of the wind analysis in Qn 3. It is very clear to view the wind speed and direction at the hourly basis. That said, a minor point regarding the y-axis of the chart. Would it be possible to add the y-axis label (scale), so that we can view the wind speed? For now, we can only view the relative wind speed on the chart (i.e. hourly comparison) but not the actual wind speed.

Also, for the map graph in Q3, it would be clearer if a larger size image can be displayed on the wiki-page :)

From: Ngo Siew Hui

Comment 3:

Hi Liwei,

The way you representing the wind speed and direction is very creative! But I'm not clear about the meaning of colours, and how you derive factory responsibility. It would be very much appreciated if you could explain more about it in your report.

Thanks, Jiaqi