Difference between revisions of "ISSS608 2017-18 T3 Assign Saurav Jhajharia (Conclusion)"
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2. There are some strange location wise anomalies visible for certain chemicals as they show the exact same pattern of increase, decrease, and constant values over a period of time. It might be interesting to figure out why that is happening and whether it is due to the same cause at all locations or if it is some kind of technical glitch in data collection. | 2. There are some strange location wise anomalies visible for certain chemicals as they show the exact same pattern of increase, decrease, and constant values over a period of time. It might be interesting to figure out why that is happening and whether it is due to the same cause at all locations or if it is some kind of technical glitch in data collection. | ||
+ | |||
+ | 3. The direction of the streams (upstream or downstream) is not mentioned in the dataset. Information on this front would be very useful as it would tell us about the spots where concentration might be higher and where it might be lower, assuming a dumping site was found. | ||
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+ | ==Q3== | ||
+ | 1. Pipit or wildlife are being affected by the increase in the value of toxic chemicals over time. | ||
+ | |||
+ | 2. Methylosmolene, Arsenic, and other such toxic chemicals have been increasing in value exponentially over the last few years. | ||
+ | |||
+ | 3. In August 2016, there was a sudden peak in the value of Methylosmolene. If this data could be compared with the soil data at Kohsoom, we could narrow down Kasios Furniture Company into saying that they were involved in dumping chemical wastes along these areas. | ||
+ | |||
+ | 4. Achara and Chai are locations where not much rise in the average value of the chemicals was visible over time. Therefore, it might be less useful to look at data from these locations at this point in time as compared to other locations. | ||
+ | |||
+ | 5. There were many chemicals for whom enough data wasn't available for understanding the exact nature of their behavior. They were either missing data at critical points of time or were not taken consistently over a time period long enough to be judged on. | ||
+ | |||
+ | 6. The value of total hardness in water was seen to be increasing from 2014 onwards. This might be due to the addition of harmful chemicals to the water as such a sharp rising trend was not visible earlier. More data from the recent years for this chemical would have helped the analysis. |
Latest revision as of 23:50, 9 July 2018
VAST Challenge 2018 MC2: Like a Duck to Water
Q1
1. The number of readings has increased over the years. Not sure if there is a direct correlation between the number of readings collected and the increase in contamination as the method of data collection is not known (manual or automatic).
2. There is a strange coincidence of visible patterns between some chemicals across all locations as their range of values, mean, median, and upper and lower quantile values are almost exactly the same.
3. There are two sets of locations where the range of chemicals and its values are almost 100% identical. This is extremely strange considering these values are taken over a period of 17 years and two locations showing the exact same pattern of chemical distribution is bizarre.
4. The collection of data at different locations can be divided into three segments: One, where data was collected from 2009 onwards only, two, where data range is extremely narrow, and three, where data range is extremely wide.
Q2
1. There are many chemicals which show peaks at random times of the year, every year. There isn't a directly visible pattern in these peaks and they occur for different chemicals at different locations. This indicates that these changes in chemical increase/decrease might not be due to weather patterns but other reasons (such as chemical contamination).
2. There are some strange location wise anomalies visible for certain chemicals as they show the exact same pattern of increase, decrease, and constant values over a period of time. It might be interesting to figure out why that is happening and whether it is due to the same cause at all locations or if it is some kind of technical glitch in data collection.
3. The direction of the streams (upstream or downstream) is not mentioned in the dataset. Information on this front would be very useful as it would tell us about the spots where concentration might be higher and where it might be lower, assuming a dumping site was found.
Q3
1. Pipit or wildlife are being affected by the increase in the value of toxic chemicals over time.
2. Methylosmolene, Arsenic, and other such toxic chemicals have been increasing in value exponentially over the last few years.
3. In August 2016, there was a sudden peak in the value of Methylosmolene. If this data could be compared with the soil data at Kohsoom, we could narrow down Kasios Furniture Company into saying that they were involved in dumping chemical wastes along these areas.
4. Achara and Chai are locations where not much rise in the average value of the chemicals was visible over time. Therefore, it might be less useful to look at data from these locations at this point in time as compared to other locations.
5. There were many chemicals for whom enough data wasn't available for understanding the exact nature of their behavior. They were either missing data at critical points of time or were not taken consistently over a time period long enough to be judged on.
6. The value of total hardness in water was seen to be increasing from 2014 onwards. This might be due to the addition of harmful chemicals to the water as such a sharp rising trend was not visible earlier. More data from the recent years for this chemical would have helped the analysis.