
Martin
@martinez7
3 replies
5 recasts
21 reactions
2 replies
0 recast
6 reactions
20 replies
8 recasts
138 reactions
126 replies
2433 recasts
4670 reactions
4 replies
17 recasts
35 reactions
16 replies
16 recasts
67 reactions
8 replies
19 recasts
119 reactions
12 replies
28 recasts
192 reactions
5 replies
22 recasts
177 reactions
0 reply
0 recast
0 reaction
25 replies
85 recasts
427 reactions
37 replies
42 recasts
306 reactions
2 replies
5 recasts
14 reactions
43 replies
146 recasts
652 reactions
6 replies
1 recast
50 reactions
I did adjacent analysis for a catchment area study last year using anonymized mobile geolocation data, which can be readily purchased from several providers. The geolocation data are sourced from the thousands of free/freemium apps that make money reporting their GPS coordinates to aggregators.
The data is sold in de-identified form but each mobile is given a unique hash, so it is possible to track how far and how fast a mobile travels along any arbitrary time series. This includes people walking, cycling, riding public transport, driving, flying, taking a ferry, etc. for both daily commutes, business travel, holidays, etc.
It would be fascinating to replicate your analysis on a larger sample with worldwide coverage and confirm this linear relationship of distance and time 1 reply
2 recasts
16 reactions
22 replies
84 recasts
456 reactions
18 replies
21 recasts
183 reactions
2 replies
8 recasts
23 reactions
0 reply
1 recast
3 reactions