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:

Water and Sewage
Road and Bridge
Gas
Garbage
Power

There is always construction going on in the above utilities.

St. Himark is segregated into 19 neighborhoods:

PALACE HILLS
NORTHWEST
OLD TOWN
SAFE TOWN
SOUTHWEST
DOWNTOWN
WILSON FOREST
SCENIC VISTA
BROADVIEW
CHAPPARAL
TERRAPIN SPRINGS
PEPPER MILL
CHEDDARFORD
EASTON
WESTON
SOUTHTOWN
OAK WILLOW
EAST PARTON
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 more quickly to get help to people faster.

The following questions also have to be answered:

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 the reaction? Which parts of the city are the hardest hit?
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.
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 an mc1-reports-data.csv file with the following data:

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

The headers were:

Time: A timestamp of the report made by a citizen. The format is in DD/MM/YY HH:MM:SS
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
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
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
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
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
shake_intensity: How violent the shaking was in the neighborhood and at the timestamp.
location: Id of the neighborhood for which the citizen is reporting his readings. (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:

road_and_bridges into "Road and Bridges"
sewer_and_water into "Sewer and Water"
shake_intensity into "Shake Intensity"

Step 2: Name clean up

Setting up Tableau

To start my visualization journey, I first added a file called StHimark.shp taken form the MC2 VAST Data challenge 2's data files to create the interactive map on a tableau workbook. This file has the following fields:

ID: Id of the neighborhood
location: Name of the neighborhood
Longitude: Longitude coordinate of the neighborhood
Latitude: Latitude coordinate of the neighborhood

I then dragged and dropped the output file form tableau prep into tableau. I used ID from StHimark.shp and location from mc1-reports-data.csv and inner joined them:

Step 3: Inner Join the two worksheets

This was the result.

Final Result of Data Transformation

Visualization and Interactive techniques

The visualizations I created was all connected from a simple main page that let the user choose if they want to see either:

Based on each neighborhood in St. Himark
Based on each reading source. E.g., Building or Medical damage
Based on the Map of St. Himark progression through the six days

This design has implemented the idea that when city officials turn to this dashboard to look for data on how to allocate resources, they can start to form their decisions based on either neighborhood, a utility that they want to work on, or see the timeline of the whole incident.

Main Page
Purpose / Description This is the landing page of the applicant. On this page the user gets a quick summary of what is the purpose of this application and an option to dive into three areas of reporting. I have created this page as a starting point where the user can keep coming back to and navigating away from. i Main Page of Application
Interactive Technique I also use interesting interactive techniques for the dashboard to be more user friendly. Select : Button Redirection Available in the tableau dashboard catalog of objects: Button. When clicked on in the tableau public website, it redirects the user to specific pages that are mapped by me Button used to redirect Select : Hover For Extra information about the button and what it does or where it leads to Button Tooltip

Neighborhood Dashboard
Purpose / Description This dashboard is designed to understand what is going on in each neighbourhood. When the user first lands on the page, all the charts show data for all the neighborhoods and sources of readings. But when the user filters the data by selecting an area on the map or one of the buttons on the list, the data changes to show the real picture in each neighborhood. The purpose of this is for the use case where city officials need to know what is going on in each neighborhood. They can see what the most reported utility and what is the most reading that is being reported. They can also see all the filtered data on a timeline, to understand when and how much was reported. The top half of the neighborhoods dashboard The bottom half of the neighborhoods dashboard
Interactive Technique Tooltips A user can put his cursor over any data point or visual elements in the charts to find out specific details about that data point. A small box will appear with relevant information. Tooltips that appear when cursor is placed on an element Filter There are two filters on this dashboard: Neighborhood Source of reading Using these two filters, the users can find specific information and make decisions about things they are interested in. On this dashboard, the main filter is the map filter on the right called "Map reference." By choosing one area, they can see the timeline, bar graphs, and heat maps about everything reported in that area. He can also apply a second filter from the "Source of reading" to get more information. The two filters available on the neighborhood dashboard Connect Since both the heat map and the total readings collected bar graphs are time-based visualizations. When a user hovers over one of the two charts, the same data point will be highlighted in the other chart too. It's more user-friendly because it helps to keep track of the data points. Two connected graphs highlight together when hovered over
Types of Charts used There are three types of charts used: Bar graphs Heat Maps Bar graphs over a timeline All the charts used in the neighbourhood dashboard

Utility Dashboard
Purpose / Description This dashboard is designed to understand how each utility affects each neighbourhood. When the user first lands on the page, all the charts show data for all sources of readings and ranks how badly damaged the neighborhoods are. When the user filters the data by selecting one of the buttons on the Source of the readings list, clearer data is shown where the ranking is now based on the selected source of reading. The purpose of this is for the use case where city officials need to allocate specific resources like emergency power or building repairs. They can see what the most reported neighborhoods and what is the total/average readings that are being reported. They can also see all the filtered data on a timeline, to understand when and how much was reported. The Utility Dashboard
Interactive Technique Tooltips A user can put his cursor over any data point or visual elements in the charts to find out specific details about that data point. A small box will appear with relevant information. Tool tip observed in the utility dashboard Filter There are two filters on this dashboard: Neighborhood Source of reading Using these two filters the users can find specific information and make decisions about things they are interested in. On this dashboard, the main filter is the source of readings filter on the right called "Select Source of Reading". By choosing one reading type, they can see the timeline, bar graphs and choropleth map report information on that source of reading. They can also apply a second filter from the "Select Neighbourhood(s)" to get more area-specific Map and utility filter Connect These four graphs are all connected by hovering the cursor over them. The neighborhood that is under your cursor will be highlighted in all four charts. The users can now track the neighborhood place on all four charts. They can know its rank in both total and avg in the comparison charts and the maps 4 Connected charts
Types of Charts used There are three types of charts used: Bar graphs A sample bar graph in the Dashboard Bar graphs over a timeline A timeline based bar graph Choropleth map A choropleth map chart

Time Dashboard
Purpose / Description This dashboard was designed to understand what kind of data was reported during the earthquake. Users can see how each neighbourhood was affected in terms of the damage done to infrastructure and shake intensity over a period of time. They can specify a period of time in the maps or look at the overall picture on the heat maps. Top Half of the time Dashboard Bottom Half of the time Dashboard
Interactive Technique Tooltips A user can put his cursor over any data point or visual elements in the charts to find out specific details about that data point. A small box will appear with relevant information. Tooltip in the time Dashboard Filter There are two filters on this dashboard: Source of reading Time pages A user can use the source of readings filter to get charts on each area of utility or the shake intensity. This filters the data in all four charts. However the time pages filter the colorpleth maps for the specific moment selected. Time Dashboard Filters Connect The two heat maps are connected as the share the same timelines and data structure. By hovering over any heatmap point with the cursor, the same time period of the point will be highlighted in the other heat map. Hovering over one Heatmap
Types of Charts used There are two types of charts used: Choropleth map Time based Colorpleth Map Heat Map Heat Maps