Table 4 Details of the spatial and temporal filtering parameters for each city.
From: A collection of public transport network data sets for 25 cities
City | Latitude | Longitude | R (km) | Download date | Extract date |
|---|---|---|---|---|---|
Adelaide | −34.9213 | 138.5775 | 40 | 2016-12-07 | 2016-12-12 |
Belfast | 54.6001 | −5.9304 | 30 | 2017-10-30 | 2016-09-05 |
Berlin | 52.5190 | 13.4029 | 30 | 2016-12-07 | 2016-04-25 |
Bordeaux | 44.8412 | −0.5751 | 30 | 2016-12-07 | 2016-12-12 |
Brisbane | −27.4580 | 153.0226 | 40 | 2016-12-07 | 2016-12-12 |
Canberra | −35.2767 | 149.1254 | 30 | 2016-12-14 | 2017-01-09 |
Detroit | 42.3700 | −83.0807 | 30 | 2016-12-07 | 2016-12-12 |
Dublin | 53.3497 | −6.2566 | 20 | 2016-12-07 | 2016-12-12 |
Grenoble | 45.1772 | 5.7228 | 20 | 2016-12-07 | 2016-11-14 |
Helsinki | 60.1733 | 24.9409 | 30 | 2016-12-07 | 2016-12-12 |
Kuopio | 62.8945 | 27.6807 | 10 | 2016-12-07 | 2016-12-12 |
Lisbon | 38.7096 | −9.1420 | 30 | 2017-01-30 | 2016-11-21 |
Luxembourg | 49.6111 | 6.1329 | 20 | 2016-12-07 | 2016-11-28 |
Melbourne | −37.8493 | 145.0793 | 50 | 2016-12-07 | 2016-12-12 |
Nantes | 47.2133 | −1.5516 | 20 | 2016-12-07 | 2016-12-12 |
Palermo | 38.1186 | 13.3598 | 20 | 2016-12-07 | 2014-09-22 |
Paris | 48.8619 | 2.3519 | 35 | 2016-12-07 | 2016-12-12 |
Prague | 50.0846 | 14.4311 | 30 | 2016-12-07 | 2016-12-12 |
Rennes | 48.1079 | −1.6749 | 20 | 2016-12-07 | 2016-12-19 |
Rome | 41.8963 | 12.4853 | 20 | 2017-10-25 | 2017-11-06 |
Sydney | −33.8269 | 151.0643 | 50 | 2016-12-14 | 2016-12-19 |
Toulouse | 43.6021 | 1.4428 | 20 | 2016-12-07 | 2016-12-12 |
Turku | 60.4491 | 22.2671 | 10 | 2016-12-07 | 2016-12-12 |
Venice | 45.4882 | 12.2416 | 20 | 2016-12-07 | 2016-12-12 |
Winnipeg | 49.8819 | −97.1352 | 30 | 2016-12-07 | 2016-12-12 |
