IS428 AY2019-20T1 Assign Pham Anh Kim Chau

  VAST Challenge MC2 - Citizen Science to the Rescue

Problem Statement

One of St. Himark’s largest employers is the Always Safe nuclear power plant. The pride of the city, it produces power for St. Himark’s needs and exports the excess to the mainland providing a steady revenue stream. However, the plant was not compliant with international standards when it was constructed and is now aging. As part of its outreach to the broader community, Always Safe agreed to provide funding for a set of carefully calibrated professional radiation monitors at fixed locations throughout the city. Additionally, a group of citizen scientists led by the members of the Himark Science Society started an education initiative to build and deploy lower cost homemade sensors, which people can attach to their cars. The sensors upload data to the web by connecting through the user’s cell phone. The goal of the project was to engage the community and demonstrate that the nuclear plant’s operations were not significantly changing the region’s natural background levels of radiation.

When an earthquake strikes St. Himark, the nuclear power plant suffers damage resulting in a leak of radioactive contamination. Further, a coolant leak sprayed employees’ cars and contaminated them at varying levels. Now, the city’s government and emergency management officials are trying to understand if there is a risk to the public while also responding to other emerging crises related to the earthquake as well as satisfying the public’s concern over radiation

Data Analysis and Preparation

1. Aggregate the data

• Tool and Packages: Python (Numpy, Pandas, Datetime)
• General

Step	Code
Create a new list of data & time ranges within the start time and end time of the data set	Real start date & time d1 = '2020-04-06 00:00:00' d1= pd.to_datetime(d1) Dummy end date & time d2 = '2020-04-11 00:00:00' d2= pd.to_datetime(d2) Real end date & time d3 = '2020-04-10 23:59:45' d3= pd.to_datetime(d3) Create a date range between start date and end date with 5 minutes interval dd = pd.date_range(d1, d2, freq='5min')
Create a list that contains the IDs of all static sensors	Create a list that contains all unique values in column "Sensor-id" id_static = value['Sensor-id'].unique().tolist() id_static.sort()
Create an list that contains the IDs of all mobile sensors	Create a list that contains all unique values in column "Sensor-id" id_mobile = mobile['Sensor-id'].unique().tolist() id_mobile.sort()

• Static Data

• Mobile Data

1. Merge mobile data and static data
2. Add neighborhood information to the data set

Observation & Insight

Question 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.

Question 2

Use visual analytics to represent and analyze uncertainty in the measurement of radiation across the city

• Compare uncertainty of the static sensors to the mobile sensors. What anomalies can you see? Are there sensors that are too uncertain to trust?
• Which regions of the city have greater uncertainty of radiation measurement? Use visual analytics to explain your rationale.
• What effects do you see in the sensor readings after the earthquake and other major events? What effect do these events have on uncertainty?

Question 3

Given the uncertainty you observed in question 2, are the radiation measurements reliable enough to locate areas of concern?

• Highlight potential locations of contamination, including the locations of contaminated cars. Should St. Himark officials be worried about contaminated cars moving around the city?
• Estimate how many cars may have been contaminated when coolant leaked from the Always Safe plant. Use visual analysis of radiation measurements to determine if any have left the area.
• Indicated where you would deploy more sensors to improve radiation monitoring in the city. Would you recommend more static sensors or more mobile sensors or both? Use your visualization of radiation measurement uncertainty to justify your recommendation.

Question 4

Summarize the state of radiation measurements at the end of the available period. Use your novel visualizations and analysis approaches to suggest a course of action for the city. Use visual analytics to compare the static sensor network to the mobile sensor network. What are the strengths and weaknesses of each approach? How do they support each other?