Preprocessing¶
The preprocessing module contains a variety of functions to transform mobility and tracking data into richer data sources.
Staypoints¶
-
trackintel.preprocessing.positionfixes.extract_staypoints(positionfixes, method='sliding', dist_threshold=100, time_threshold=300, epsilon=100, dist_func=<function haversine_dist>)¶ Extract staypoints from positionfixes.
Parameters: - positionfixes : GeoDataFrame
The positionfixes have to follow the standard definition for positionfixes DataFrames.
- method : {‘sliding’ or ‘dbscan’}
The following methods are available to extract staypoints from positionfixes:
‘sliding’ : Applies a sliding window over the data. ‘dbscan’ : Uses the DBSCAN algorithm to find clusters of staypoints.
- dist_threshold : float
The distance threshold for the ‘sliding’ method, i.e., how far someone has to travel to generate a new staypoint.
- time_threshold : float
The time threshold for the ‘sliding’ method in seconds, i.e., how long someone has to stay within an area to consider it as a staypoint.
- epsilon : float
The epsilon for the ‘dbscan’ method.
- dist_func : function
A function that expects (lon_1, lat_1, lon_2, lat_2) and computes a distance in meters.
Returns: - GeoDataFrame
A new GeoDataFrame containing points where a person spent some time.
References
Zheng, Y. (2015). Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), 6(3), 29.
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., & Ma, W. Y. (2008, November). Mining user similarity based on location history. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (p. 34). ACM.
Examples
>>> extract_staypoints(...)
