A road lined by palm trees leading into an uncertain future

Data Visualization with Tableau I

The three interactive dashboards in this post are as follow:

Geo-spatial Analytics Dashboard
Self Service Analytics Dashboard
Flight Route Display Dashboard

1. Geo-spatial Analytics - Boston Crimes

This is a dashboard that allow me to observe when and where do all the incidence of crimes happen in Boston across different years. In this case, I put my focus on incidence of larceny, but users can always select different categories using the filter.

Note:

Working dataset can be found on Kaggle. (Link)
The Tableau workbook: BostonCrime_Dashboard is also uploaded to the same repository(GitHub)

Figure 1 Interactive Dashboard of Larceny in Boston

2. Self Service Analytics Dashboard

This is a dashboard in which I can easily spot all correlated indices and learn to what extent are the two indices correlated. By choosing different variables from the dropdown lists, X-axis, and Y-axis, the correlation plot in the top-left would change correspondingly.

Working dataset: WDI.csv can be found in the repository
The Tableau workbook: Self_Service_Dashboard is also uploaded to the same repository (GitHub)

Figure 2. The correlation of one set of indices

Figure 3. Choose another set of X-axis and Y-axis indices

3. Flight Route Display Dashboard

This is a dashboard that displays routes and calculates distances for the top 10 cities with the highest numbers of flights in the USA. During the project, I extracted longitude and latitude data from Tableau, used that information in conjunction with trigonometry to calculate the mileage between various points, and visualized the result on a Tableau dashboard.

Working dataset: Airline_newID.csv can be found in the same repository
The Tableau workbook: mapping_Dashboard is also included in the repository

Figure 4. Interactive dashboard that shows the greatest flight route