Difference between revisions of "ISSS608 2017-18 T3 Assign Saurav Jhajharia (Q2)"

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[[File:Q2(1).png|700px]]
 
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Similarly, there is some anomaly location wise as well. For the chemical Atrazine, Busrakahan, Kannika, Sakda, and Chai were seen to have the exact same trend in average values.
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[[File:Location_anomalies.png|1100px]]
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==Anomalies (Location wise)==

Revision as of 20:13, 9 July 2018

VAST Challenge 2018 MC2: Like a Duck to Water

Problem Statement

Data Overview & Preparation

Q1

Q2

Q3

Conclusion

 

Q2

What anomalies do you find in the waterway samples dataset? How do these affect your analysis of potential problems to the environment? Is the Hydrology Department collecting sufficient data to understand the comprehensive situation across the Preserve? What changes would you propose to make in the sampling approach to best understand the situation?

Anomalies (Chemical wise)

There was a chemical wise filtration done to remove chemicals that showed no major unconventional change in values over the years or had no unusual spike in their dataset. Any spike, as shown below, as per the location it is present in, was considered to be worth noting down for investigators to take a deeper look into.

The image below clearly indicates that Ammonium showed a sharp increase in value in Tansanee and therefore only that dataset is of prime value for the investigators.

Q2(1).png

Similarly, there is some anomaly location wise as well. For the chemical Atrazine, Busrakahan, Kannika, Sakda, and Chai were seen to have the exact same trend in average values.

Location anomalies.png

Anomalies (Location wise)