IS428 AY2019-20T1 Parth Goda Rajesh

Background

Welcome to St. Himark! A fictional city that will is being used in this visual case study. It is a city of 19 neighborhoods, all of which have their unique characteristics and amenities. St. Himark has a population of 246,839 people and it's located in the Oceanus Sea. It is also home to the world-renowned St. Himark Museum, beautiful beaches, and the Wilson Forest Nature Preserve. It is one of the best cities to raise a family and work. Always Safe Nuclear Power Plant provides the majority of the power in the city and jobs in the Safe Town. Mayor Jordan and the city council current govern the city.

The runs in the following utilities:

1. Water and Sewage
2. Road and Bridge
3. Gas
4. Garbage
5. Power

There is always construction going on in the above utilities.

St. Himark is segregated into 19 neighborhoods:

1. PALACE HILLS
2. NORTHWEST
3. OLD TOWN
4. SAFE TOWN
5. SOUTHWEST
6. DOWNTOWN
7. WILSON FOREST
8. SCENIC VISTA
9. BROADVIEW
10. CHAPPARAL
11. TERRAPIN SPRINGS
12. PEPPER MILL
13. CHEDDARFORD
14. EASTON
15. WESTON
16. SOUTHTOWN
17. OAK WILLOW
18. EAST PARTON
19. WEST PARTON

Problem

There was an earthquake northwest of St. Himark. It occurred between 6 April 2020 and 8 April 2020. The city's officials needed to collect data immediately to understand the extent of the damage. Then can then allocate resources efficiently to the areas of town where it's needed and dispatch their emergency services.

At first, they only have the seismic readings of the earthquake and used that for their first round of dispatch. Now, however, they need more information to get a better gauge of what is going on on the ground level.

Purpose

To gather the information the city official's need. They launched an app where the citizens can report the intensity of shake and level of damage done to utility infrastructure. The officials can use this tool to record data provided by citizens. The citizens use the app to note down the level of damage seen on a utility/infrastructure building in a Neighbourhood. They can also record the shake intensity in the neighborhood. The data is stored every 5 mins. They may also be some data loss or delay due to power shortages.

With all this data, visualizations were created to understand the data faster. Recommendations and decisions can be churned out faster to get help to people faster.

The following questions also have to be answered:

1. Emergency responders will base their initial response on the earthquake shake map. Use visual analytics to determine how their response should change based on damage reports from citizens on the ground. How would you prioritize neighborhoods for response? Which parts of the city are hardest hit?
2. Use visual analytics to show uncertainty in the data. Compare the reliability of neighborhood reports. Which neighborhoods are providing reliable reports? Provide a rationale for your response.
3. How do conditions change over time? How does uncertainty in change over time? Describe the key changes you see.

Data Gathering and Clean up

The data provided in a CSV file with the following data:

The first few rows of the data provided in the CSV file

The headers were:

1. Time: A timestamp of the report made by a citizen. The format is in DD/MM/YY HH:MM:SS
2. sewer_and_water: Damage recorded on the sewer and water systems in the neighborhood and at the timestamp. 0 is the lowest level of damage while 10 is the highest
3. power: Damage recorded on the power generation systems in the neighborhood and at the timestamp. 0 is the lowest level of damage while 10 is the highest
4. roads_and_bridges: Damage recorded on the roads and bridges in the neighborhood and at the timestamp. 0 is the lowest level of damage while 10 is the highest
5. medical: Damage recorded on the medical facilities in the neighborhood and at the timestamp. 0 is the lowest level of damage while 10 is the highest
6. buildings: Damage recorded on the buildings in the neighborhood and at the timestamp. 0 is the lowest level of damage while 10 is the highest
7. shake_intensity: How violent the shaking was in the neighborhood and at the timestamp.
8. location: Id of the neighborhoods the citizen is reporting his readings for. (This will be matched to the neighborhood data in the map file)

Cleaning up the data

Using Tableau Prep Builder, data from the CSV file was moved around and changed a little to make visualizations better.

Pivoting

To start, I first pivot the medical, power, road_and_bridges, sewer_and_water, buildings and shake_intensity on the dashboard. The utilities are called "Source of reading" and values are called "Readings"

Step 1: Pivoting the dashboard

Cleaning up names

Next, I renamed the following sources of reading and capitalized the rest:

1. road_and_bridges into "Road and Bridges"
2. sewer_and_water into "Sewer and Water"
3. shake_intensity into "Shake Intensity"

Step 2: Name clean up