IS428 AY2019-20T1 Assign Lim Pei Xuan

Overview

St. Himark is a vibrant community located in the Oceanus Sea. Home to the world-renowned St. Himark Museum, beautiful beaches, and the Wilson Forest Nature Preserve, St. Himark is one of the region’s best cities for raising a family and provides employment across a number of industries including the Always Safe Nuclear Power Plant. Well, all that was true before the disastrous earthquake that hits the area during the course of this year’s challenge. Mayor Jordan, city officials, and emergency services are overwhelmed and are desperate for assistance in understanding the true situation on the ground and how best to deploy the limited resources available to this relatively small community.

Mini-Challenge 1 : Crowdsourcing for Situational Awareness

St. Himark has been hit by an earthquake, leaving officials scrambling to determine the extent of the damage and dispatch limited resources to the areas in most need. They quickly receive seismic readings and use those for an initial deployment but realize they need more information to make sure they have a realistic understanding of the true conditions throughout the city.

In a prescient move of community engagement, the city had released a new damage reporting mobile application shortly before the earthquake. This app allows citizens to provide more timely information to the city to help them understand damage and prioritize their response. In this mini-challenge, use app responses in conjunction with shake maps of the earthquake strength to identify areas of concern and advise emergency planners. Note: the shake maps are from April 6 and April 8 respectively.

With emergency services stretched thin, officials are relying on citizens to provide them with much needed information about the effects of the quake to help focus recovery efforts.

By combining seismic readings of the quake, responses from the app, and background knowledge of the city, help the city triage their efforts for rescue and recovery.

The Questions

Your task, as supported by visual analytics that you apply, is to help St. Himark’s emergency management team combine data from the government-operated stationary monitors with data from citizen-operated mobile sensors to help them better understand conditions in the city and identify likely locations that will require further monitoring, cleanup, or even evacuation. Will data from citizen scientists clarify the situation or make it more uncertain? Use visual analytics to develop responses to the questions below. Novel visualizations of uncertainty are especially interesting for this mini-challenge.

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. Limit your response to 6 images and 500 words.
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?

   Limit your responses to 12 images and 1000 words.

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.

   Limit your responses to 12 images and 1000 words

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?

   Limit your response to 6 images and 800 words.

5. The data for this challenge can be analyzed either as a static collection or as a dynamic stream of data, as it would occur in a real emergency. Describe how you analyzed the data - as a static collection or a stream. How do you think this choice affected your analysis?

   Limit your response to 200 words and 3 images.

The Data

The data for MC1 includes one (CSV) file spanning the entire length of the event, containing (categorical) individual reports of shaking/damage by neighborhood over time. Reports are made by citizens at any time, however, they are only recorded in 5-minute batches/increments due to the server configuration. Furthermore, delays in the receipt of reports may occur during power outages.

mc1-reports-data.csv fields:

• time: timestamp of incoming report/record, in the format YYYY-MM-DD hh:mm:ss
• location: id of neighborhood where person reporting is feeling the shaking and/or seeing the damage
• {shake_intensity, sewer_and_water, power, roads_and_bridges, medical, buildings}: reported categorical value of how violent the shaking was/how bad the damage was (0 - lowest, 10 - highest; missing data allowed)

Also included are two shakemap (PNG) files which indicate where the corresponding earthquakes' epicenters originate as well as how much shaking can be felt across the city.