It was Friday and it was the last day of Dashboard week! Today’s Challenge was all about Spatial data from the Census of Land Use and Employment (CLUE) for Melbourne. I was looking forward to brushing up on spatial analysis. This time we only had 6 hours from the start of our briefing at 9am until our presentation at 3pm, while on the previous days we were able to keep working at night.
The Angle
The CLUE Data had information on restaurants, cafes, bars, car parks, residents including location points and shape files dividing up Melbourne in various ways. My first idea was to look at the ratio of restaurants, cafes and bars to car parks, and look at it from the perspective of ‘where do we need more car parks?’ In addition I thought it would be great to link the points so that you could find the nearest car park for a given venue.
I ended up going with the 2nd option as we’d done some projects using polygons previously and wanted to work with geopoints.
Data Preparation
To prepare the data in Alteryx I created points from the longitude and latitudes provided for each venue and car park. I then used the find nearest tool. What this did was for each venue, found the nearest car park point and place it on the same data row along with additional fields including distance.
From there I outputted it to a Tableau hyper file and made my dashboard.
The Dashboard
On the dashboard there is a map with the geopoints for cafes, restaurants, bars and car parks. To filter the points, you can look for a specific suburb, choose a venue type, and can also search for a specific venue. Once you click the venue, the map will show you the nearest car park along with the distance from the venue and the capacity of the car park.
