The busiest parking places in Melbourne

The city of Melbourne has provided a 1.8GB dataset containing the exact times when cars were parked and the times when they left. This allows us to check which parking places were most busy, by summing up the total number of seconds they were occupied by different cars.

MarkerTotal secondsStreetBetweenAnd
C777286268QUEENSBERRY STREETLOTHIAN STREETABBOTSFORD STREET
5216E86263COBDEN STREETO'CONNELL STREETVICTORIA STREET
2383S86228BOURKE STREETRUSSELL STREETEXHIBITION STREET
4744E86222ELIZABETH STREETPELHAM STREETQUEENSBERRY STREET
C778660789QUEENSBERRY STREETLOTHIAN STREETABBOTSFORD STREET
5218E57421COBDEN STREETO'CONNELL STREETVICTORIA STREET
5672E55793CAPEL STREETVICTORIA STREETWILLIAM STREET
8663W55792KAVANAGH STREETBALSTON STREETPOWER STREET
5694E55792CAPEL STREETVICTORIA STREETWILLIAM STREET
5686E55792CAPEL STREETVICTORIA STREETWILLIAM STREET
11507S55790JEFFCOTT STREETKING STREETSPENCER STREET
11502N55790JEFFCOTT STREETKING STREETSPENCER STREET
4961W48593QUEEN STREETVICTORIA STREETTHERRY STREET
C692646793FRANKLIN STREETWILLAM STREETQUEEN STREET
C671446793FRANKLIN STREETQUEEN STREETELIZABETH STREET
C657846793FRANKLIN STREETQUEEN STREETELIZABETH STREET
C650446793FRANKLIN STREETSWANSTON STREETVICTORIA STREET
7064N46793DUDLEY STREETKING STREETWILLIAM STREET
3515S46793Lt LONSDALE STREETKING STREETWILLIAM STREET
1556E46793SPENCER STREETBATMAN STREETJEFFCOTT STREET
C686246792FRANKLIN STREETWILLAM STREETQUEEN STREET
C646446792FRANKLIN STREETSWANSTON STREETVICTORIA STREET
C60046792EXHIBITION STREETLA TROBE STREETLt LONSDALE STREET
7091S46792DUDLEY STREETKING STREETWILLIAM STREET
4856E46792QUEEN STREETFRANKLIN STREETA'BECKETT STREET
3591S46792Lt LONSDALE STREETSPENCER STREETKING STREET
3577S46792Lt LONSDALE STREETSPENCER STREETKING STREET
3543S46792Lt LONSDALE STREETKING STREETWILLIAM STREET
1562E46792SPENCER STREETBATMAN STREETJEFFCOTT STREET
C665846791FRANKLIN STREETQUEEN STREETELIZABETH STREET
C666646789FRANKLIN STREETQUEEN STREETELIZABETH STREET
5705W46060CAPEL STREETVICTORIA STREETWILLIAM STREET
5009W44993QUEEN STREETVICTORIA STREETTHERRY STREET
C72243193RUSSELL STREETBOURKE STREETLt COLLINS STREET
C67643193RUSSELL STREETLt COLLINS STREETCOLLINS STREET
C115243193QUEEN STREETLONSDALE STREETLt BOURKE STREET
683W43193RUSSELL STREETLt COLLINS STREETCOLLINS STREET
6680N43193FRANKLIN STREETQUEEN STREETELIZABETH STREET
C281043192LONSDALE STREETRUSSELL STREETEXHIBITION STREET
6561W43192ABBOTSFORD STREETELM STREETVICTORIA STREET
2376N43192BOURKE STREETRUSSELL STREETEXHIBITION STREET
C671043003FRANKLIN STREETQUEEN STREETELIZABETH STREET
2574N42941Lt BOURKE STREETWILLIAM STREETQUEEN STREET
6330E39600LEVESON STREETQUEENSBERRY STREETVICTORIA STREET
C711639593WALSH STREETMILTON STREETWILLIAM STREET
C613839593CHETWYND STREETQUEENSBERRY STREETVICTORIA STREET
C613039593CHETWYND STREETQUEENSBERRY STREETVICTORIA STREET
C608439593CHETWYND STREETQUEENSBERRY STREETVICTORIA STREET
C606039593CHETWYND STREETVICTORIA STREETSTANLEY STREET
C603839593CHETWYND STREETVICTORIA STREETSTANLEY STREET

From this table we can see that the first four of the busiest parking places were occupied over 25000 seconds longer than the parking place ranked on the fifth place in the list. According to Google Maps, Queensberry Street is located in North Melbourne, Cobden Street is very close to Queensberry Street, but closer to Queen Victoria Market (where lots of dining places are shown). Bourke Street is close to a hair salon, tobacco store, the office of state administration, the electronics store JB Hi-Fi, hotel "Ibis" and Chinatown Melbourne. Elizabeth Street is close to the "Last Jar" Irish pub, Arabesque Melbourne, Chemist Warehouse and Toyota and Lexus car traders.

We don't have the information whether the cars parked at the same place truly belonged to different owners. The same car might have been moved around and then parked at the same spot. Further, we don't know whether all parking places were paid and if so, whether according to the payment rate, the car owners were influenced to keep their cars for different intervals of time. This would have made the results incomparable.

It would have been nice if we had the coordinates of the streets to be able to discern more pronounced parking patterns. Yet, this could have increased the dataset size to over 2GB. But as you can see, it is possible to work with datasets of such size even on a slow single-core machine. It may take some time (&approx.;15-20mins and 2-3GB RAM) until the analysis is complete, but at the end we get unique and useful results.

The street names remained in capital letters, which I noticed after the analysis was complete.