fblamanna/most-frequented-locations

Extract Most Frequented Locations from individual spatio-temporal trajectories

Extract Most Frequented Locations from individual spatio-temporal trajectories

Copyright 2015 Maxime Lenormand. All rights reserved. Code under License GPLv3.

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This script returns the location most frequented by an individual (called MFL) during daytime and nighttime according to a certain time window. The MFL during a given time windows is defined as the location in which the individual has spent most of his/her time. In this algorithm, two scales are considered, hours and days. Hopefully, for a given individual, the MFLs detected at the two scales should be the same.

Input

The algorithm takes as input a 6 columns csv file with column names, the value separator is a semicolon ";". Each row of the file represents a spatio-temporal position of an individual's trajectory. The time is given by the columns 2 to 5 (year, month, day and hour). It is important to note that the table should be SORTED by individual ID and time.

1. ID of the individual
2. Year
3. Month
4. Day
5. Hour
6. ID of the geographical location

Parameters

The algorithm has 6 parameters:

1. wdinput: Path of the input file (ex: "input.csv")
2. wdoutput: Path of the output file (ex: "outputoftheawsomemaximelenormandsalgorithm.csv")
3. minH: Lower bound (included) of the night time window (ex: 20h)
4. maxH: Upper bound (included) of the night time window (ex: 7h)
5. minW: Lower bound (included) of the day time window (ex: 8h)
6. maxW: Upper bound (included) of the day time window (ex: 19h)

Output

The algorithm returns a 15 columns csv file with column names, the value separator is a semicolon ";". Each row represents an individual:

1. ID: ID of the individual
2. NbMonths: Number of distinct months covered by the trajectory
3. NbConsMonths: Maximum number of consecutive months covered by the trajectory
4. MFLHomeDays: MFL during nighttime (day scale), 'NoMFL' if NbDaysHome=0
5. MFLHomeDays2: Second MFL during nighttime (day scale) if ex aqueo, 'NoMFL' otherwise
6. NbDaysHomeMFL: Number of distinct days (during nighttime) spent in the MFL
7. NbDaysHome: Number of distinct days covered by the trajectory (during nighttime)
8. MFLHomeHours: MFL during nighttime (hour scale), 'NoMFL' if NbHoursHome=0
9. MFLHomeHours2: Second MFL during nighttime (hour scale) if ex aqueo, 'NoMFL' otherwise
10. NbHoursHomeMFL: Number of distinct hours spent in the MFL (during nighttime)
11. NbHoursHome: Number of distinct hours covered by the trajectory (during nighttime)
12. MFLWorkDays: MFL during daytime (day scale), 'NoMFL' if NbDaysWork=0
13. MFLWorkDays2: Second MFL during daytime (day scale) if ex aqueo, 'NoMFL' otherwise
14. NbDaysWorkMFL: Number of distinct days spent in the MFL (during daytime)
15. NbDaysWork: Number of distinct days covered by the trajectory (during daytime)
16. MFLWorkHours: MFL during daytime (hour scale), 'NoMFL' if NbHoursWork=0
17. MFLWorkHours2: Second MFL during daytime (hour scale) if ex aqueo, 'NoMFL' otherwise
18. NbHoursWorkMFL: Number of distinct days spent in the MFL (during daytime)
19. NbHoursWork: Number of distinct hours covered by the trajectory (during daytime)

Execution

You can run the code using the command:

python MFL.py 'input.csv' 'output.csv' 20 7 8 19

Citation

If you use this code, please cite:

Louail T, Lenormand M, Murillo Arias M & Ramasco JJ (2015) Crowdsourcing the Robin Hood effect in the city. Arxiv e-print, arXiv: .

If you need help, find a bug, want to give me advice or feedback, please contact me! You can reach me at maxime[at]ifisc.uib-csic.es

References

[1] Louail et al. (2015) Crowdsourcing the Robin Hood effect in the city.. Arxiv e-print, arXiv: .