Day 3 of dashboard week was Power BI day. We were challenged to create a dashboard with a focus on storytelling. We were limited in our data choice to only Victorian crime statistics from the government. Follow along as I take you through how I tackled this challenge.
After browsing the data, I chose to look at the different property that was stolen in different areas of Victoria, Australia. Within this Excel file was multiple tables but only three tables piqued my interest:
- Property stolen by LGA
- Offences at farm location by LGA
- Types of Property Stolen by Item Type (for farm locations)
The first challenge I had to face here was when I exported the data into Power BI, an error came up for the postcode. Due to the constraint of time, I ultimately decided that the best way to resolve this was to clean and prepare the data in Alteryx instead of Power Query. After plugging the three tables I chose to look at into Alteryx and outputting them as .csv files, I no longer had errors coming up in Power BI.
The second challenge I ran into was whether it would be appropriate to merge these tables together because each table had a different level of detail. After preparing, cleaning and exploring the data in Alteryx more, I subsequently decided that it would be best to create different charts that have limited interactivity between each other, which lead to my third challenge.
To further explore the data, I utilised my insights matrix, which you can read about in this blog post I wrote. This insights matrix helped me expand on the insights I can find from a limited number of fields in a dataset. I created an insights matrix for each of the three tables to ask questions about the relationship between two fields.
Property Stolen by LGA:
Offences at Farm Locations by LGA:
Types of Property Stolen by Item Type (for farmland):
From my the insight matrix I made for each table, I found common questions I wanted to ask which included:
- Is particular property types being stolen more over time, is theft happening more often, and is the value of stolen property increasing over time?
- Are particular items stolen more from particular LGAs and are particular items stolen more times?
- Does the type of location area (residential, commercial, and other) have an impact on crime?
Which a clear understanding of the questions I wanted to answer, I moved onto Power BI to start creating charts for the dashboard.
When creating a dashboard in Power BI, I had to consider how I could create a good dashboard for great story telling despite limited interactivity between the three tables that I was not able to join together. I initially decided to create three different views of the tables to resolve the join issue but upon exploring the data more, I realised that it would be difficult to find a story at a high level aggregate detail. Ultimately, I decided to rescope and hone in on looking at property-related crimes in farmland areas of Victoria. I also decided to include another table related to what kind of items were stolen, how many items were stolen and the value of the items. Within this dataset, I noticed a few interesting things such as solar panels, llamas and camels being some of the property that were stolen from the victims. I believed that this would be an interesting story to tell so I created a second view of the dashboard using this table.
Power BI is different from Tableau in that charts for dashboard can be create easily and quickly. However, Tableau offers a higher ceiling for customisation and dashboard interactivity. As a result, for this challenge, I decided that the best course of action would be to focus on what limited functionality I could add to Power BI as well as dashboard design. Due to limitations on publishing the power BI dashboard, I included a screenshot of the final dashboard below. My dashboard also contains two pages which can be reached by clicking on the bookmark tab.
