Hello and welcome to my dashboard journey 3.0!

 

Unlike the previous two challenges, we were given a set topic this time rather than having the freedom of choosing a topic this time. Our challenge today is to make a Power BI dashboard about crimes analysis in Victoria.

 

The website we used have four different types of datasets. The dataset I chose was about criminal incidents recorded in Victoria. This is how the dataset looks like: example here

 

After selecting the dataset, I started to explore all the tables in the dataset. I tried to figure out the relationship between all the tables to get a better understanding of the dataset. I then built an ER diagram in Power BI.  However, the only column we could use to connect all tables is “Local Government Areas”, and it will create a many to many (both) relationship.

 

However, this kind of relationship can be very ambiguous which might lead to errors in your analysis and you will not notice. With that in mind, I realised that the ER diagram wouldn’t work and I had to use each table separately. I also noticed the data is very aggregated and we cannot do much about it.

 

After exploring the dataset, I then started to build a dashboard about it.  My idea was to find the safest place to live in Melbourne. I planned to build a map as filter, and also a donut chart to see the structure of offense type. I also planned to create some KPIs to measure the performance of each Local Government Area and suburb.

 

In addition to the above, I thought a line chart to show historical trend of criminal incidents and a bar chart to show the top suburbs by criminal incidents would be good indications when finding the safest suburbs.

 

During the process of making theses charts, I also found the distribution of Local Government Areas might be very interesting, so I created a scatterplot based on criminal incidents recorded and LGA rate. I then added two reference lines to indicate the state average and used Power BI’s AI function to analyse the clustering. I have also planned to create a drill-down donut chart illustrating offense division, enabling further exploration at the offense subdivision and subgroup levels. This will facilitate a detailed analysis of specific crimes in specific areas. Additionally, I’ve included KPIs such as LGA rate by population and percentages for Charges Laid, No Charges Laid, and Unsolved Cases to assess the performance of each LGA.

This is how my final Power BI dashboard looks like:

 

The Dashboard Week is definitely a big challenge to me.  Yet, I found the joy of conquering a difficult task during such a short time and I really enjoyed the challenge.

 

 

 

The Data School
Author: The Data School