The following cube is an example of a quick piece of analytical work that we can undertake to look at differences between particular suburbs. In the screenshot below I’ve ordered suburbs by average sale price and it shows the most expensive suburbs (5). There are some differences between the suburbs that are present in this list:
Some have a small building area and land size but are close to the city centre (e.g. Middle Park and Albert Park)
Some have a lot of land (e.g. Balwyn North)
Some are bigger or have a larger number of rooms (e.g. Canterbury, Deepdene or Kooyong).
The Apteco Datathon: 4. The property market in Melbourne
This exploratory analysis enables cyprus mobile number example us to understand more about the data set and will help us when we come to undertake more advanced analytics on the data.
Proximity to public transport may be an important factor in influencing property prices, so this would be good to add to the data provided. There are three main forms of public transport within Melbourne – trains, trams, and buses (6). In this analysis we’ve used just the train and tram stop information.
Once we have this information we can then formulate expressions to work out the distance to the nearest stop/station and also the name of the nearest public transport stop/station. To do this we use the GeoDistMin() and GeoNearest() expression functions.
These functions take a single latitude/longitude (latlong) pair and work out the distance to a set of other latlong pairs and return the smallest of those values. The screenshot below shows the start of the expression which contains over 200 location pairs to calculate the distance to.