Today is the last day of the dashboard week! The task for today was to develop a dashboard using the Melbourne Census of Land Use and Employment information, which was the interview dataset for the newest recruits, DSAU12. It is a big data set that includes a spatial file, bar and pub capacity, car parking, residential building information, CLUE by ANZSIC by block by census year, and an aggregate file of CLUE details by block id and census year.
The purpose of Dashboard
The dashboard I want to create will visualize all of the Census data that has been provided. My challenge is to understand and process all the data in such a short amount of time and find a way to visualize it.
The data connection
First, I use the relationship to connect the spatial file with the aggregated CLUE block data. Then, instead of joining the data or using the relationship for the other files, I import each file into a separate database and use the parameter as a bridge to communicate between the databases to avoid many-to-many relationships in the model. Here, I establish two parameters: one for the Block ID and another for the Census year.
The dashboard
The dashboard I create can display the census data for the different years by the filter. I build the BANs for each region based on the Census data and demonstrate the KPI in a different dimension (Town houses，Residential Apartment，Student Apartments，Cafe/Restaurant Indoor seating capacity，Cafe/Restaurant Outdoor seating capacity，Bar/Pub patron capacity，Commercial Carpark Spaces，Private Carpark Spaces，Residential Carpark Spaces) for the selected census year and a percentage change comparing previous year. When the user selects a particular region on the BANs, the map below the BANs will zoom in to block within this region. If the user clicks on the specific block, the map will drill down and show its KPI over time. The four maps on the right indicate the number of residential units, the capacity of bars and pubs, cafes and restaurants, and car parks for a selected block. The user can quickly access census information for this dashboard by choosing the KPI, area, block, and census year.
