Today’s challenge was to find data that we were interested in and create a data story that would help make a certain decision. For my project, I chose to focus on trees in the Melbourne CBD area and examine the percentage of allergenic trees in comparison to the total number of trees.

Melbourne is a city known for its beautiful parks and trees. These trees provide shade and improve air quality, making the city a more pleasant place to live. However, certain types of trees can cause problems for people who suffer from allergies. This can be a source of discomfort and even health problems. In this blog post, we’ll explore how to create an interactive dashboard that displays information about allergenic trees in Melbourne.

Step 1: Gathering Data

The first step in creating an interactive dashboard is to gather data. In this case, we need information about the types of trees that are known to be allergenic in Melbourne. The City of Melbourne website provides a list of trees that may cause allergies, including oak, plane, and pine trees. We can use this list as a starting point for our dashboard.

As we were working with spatial data this week, I downloaded the SAL level boundaries to allocate each tree to a specific suburb.

I also created a separate list of allergenic trees and created a column that would indicate whether the tree is allergenic or not. After that, I combined the two datasets and ended up with one table that had all the trees, allergenic or not, and the suburb it is located in.

Step 2: Creating a Map

The next step is to create a map of Melbourne that displays the location of allergenic trees. We can import the list of allergenic trees and their locations into the mapping tool to create a custom map that shows where these trees are located.

Step 3: Adding Data Visualization

Once we have a map of allergenic trees in Melbourne, we can add data visualization to our dashboard to make it more informative and user-friendly. One way to do this is to use a chart or graph that displays the number of allergenic trees in different areas of the city. We can also include information about the types of allergens that these trees produce and the times of year when they are most active.

Step 4: Adding Interactive Features

Finally, we can add interactive features to our dashboard to make it more engaging and useful. For example, we can allow users to filter the data by different criteria, such as by type of tree or by location. We can also include a search function that allows users to find specific information about a particular tree or location.

Conclusion

Creating an interactive dashboard about allergenic trees in Melbourne can provide valuable information for people who suffer from allergies. By displaying the location of allergenic trees and providing information about their types and allergens, the dashboard can help users avoid exposure to these trees and reduce the severity of their symptoms. With the addition of data visualization and interactive features, the dashboard can be a powerful tool for promoting public health and well-being in Melbourne. This project is important because it highlights the need for more awareness about the dangers of allergenic trees and the importance of public health measures to protect people who suffer from allergies.

Vis link: https://public.tableau.com/views/DashboardWeekDay3-AllergenictreesinMelbourne/V1dashboard?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

Veronika Varaksina
Author: Veronika Varaksina

Meet Veronika, a dynamic and adaptable individual with a diverse background in economics, accounting, finance, and data analytics. Veronika pursued a Bachelor’s degree in Economics and gained valuable experience in financial analysis, budgeting, and forecasting while working for five years in accounting and finance. However, she soon realized her passion for data analytics and decided to pursue a postgraduate degree in Analytics at Victoria University. Throughout her academic journey, Veronika honed her skills in data visualization, statistical modeling, and machine learning. Her expertise earned her a spot in the highly competitive Data School program, where she further continues to expand her skills in data analysis.