mobilipy.privacy

Functions

`add_noise`(point[, radius, offset])	Adds random uniform noise to the given point, in a radius = radius - offset
`aggregate`(waypoints_df[, cell_size, delta])	Aggregates users in timedeltas and cells on the map.
`assign_cell_center`(latitude, longitude, ...)	Returns the closest cell center for the given coordinates, when the map is divided into a lattice with the supplied cell_size
`dt_floor`(dt, delta)	Performs the floor operation on datetime values, with given timedelta as the base, e.g.
`get_obfuscation_utility`(w_prepared, ...)	Calculates the ratio of legs affected by obfuscation to total legs
`km_to_lat`(km_north)	Expresses latitude given in kilometers to the east in degrees
`km_to_lon`(km_east, latitude)	Expresses given kilometers to the east in longitude degrees.
`lat_to_km`(latitude)	Expresses given latitude in kilometers to the north
`lon_to_km`(latitude, longitude)	Expresses given longitude in kilometers to the east
`obfuscate`(df, locations[, radius, offset, mode])	Obfuscates the regions of points given in 'locations' parameter by either removing all the points in their proximity, or changing the location of these points to one, noisy location in the proximity circle.
`shift_point`(point, latitude_shift_km, ...)	Shifts the coordinates of a given point.

mobilipy.privacy.add_noise(point, radius=100, offset=30)

Adds random uniform noise to the given point, in a radius = radius - offset

Parameters

point (tuple) – Points as a tuple (latitude, longitude) in degrees.
radius (int, optional) – Radius of the point neighbourhood. Defaults to 100.
offset (int, optional) – Offset from the perimeter of the neighbourhood circle, where the shifted point can’t be located. Defaults to 30.

Returns

tuple – Shifted point expressed as (latitude, longitude)

mobilipy.privacy.aggregate(waypoints_df, cell_size=0.2, delta=datetime.timedelta(seconds=900)) → pandas.core.frame.DataFrame

Aggregates users in timedeltas and cells on the map. Returns a DataFrame with the count of users in a given timedelta and cell.

Parameters

waypoints_df (pandas.DataFrame) – DataFrame with ‘latitude’, ‘longitude’, ‘user_id’ and ‘tracked_at’ columns.
cell_size (float) – Size of the square cells on the map, in kilometers.
delta (datetime.timedelta) – Frequency for the time aggregation, e.g. 15 minutes.

Returns

pandas.DataFrame – DataFrame with ‘tracked_at’, ‘cell_latitude’, ‘cell_longitude’ and ‘count’ columns. The ‘cell_latitude’ and ‘cell_longitude’ columns give coordinates of the centers of cells on the map.

mobilipy.privacy.assign_cell_center(latitude, longitude, cell_size)

Returns the closest cell center for the given coordinates, when the map is divided into a lattice with the supplied cell_size

Parameters

latitude (float) – Latitude as degrees
longitude (float) – Longitude as degrees
cell_size (float) – Cell size in kilometers

Returns

tuple(float, float) – Coordinates of the closest cell center on the map

mobilipy.privacy.dt_floor(dt, delta) → datetime.datetime

Performs the floor operation on datetime values, with given timedelta as the base, e.g. 2021-01-01 12:21:47 with timedelta of 15s returns 2021-01-01 12:21:45.

Parameters

dt (datetime.datetime) – Datetime value to be floored.
delta (datetime.timedelta) – Timedelta used for the floor operation.

Returns

datetime.datetime – Floored datetime.

mobilipy.privacy.get_obfuscation_utility(w_prepared, w_obfuscated, legs) → float

Calculates the ratio of legs affected by obfuscation to total legs

Parameters

w_prepared (pandas.DataFrame) – Smoothed and cleaned waypoints DataFrame
w_obfuscated (pandas.DataFrame) – Smoothed, cleaned, and obfuscated waypoints DataFrame
legs (pandas.DataFrame) – DataFrame that contains assembled legs

Returns

[float] – Ratio of legs affected by obfuscation to total legs

mobilipy.privacy.km_to_lat(km_north) → float

Expresses latitude given in kilometers to the east in degrees

Parameters: km_north (float) – Latitude expressed in kilometers to the north
Returns: float – Latitude in degrees

mobilipy.privacy.km_to_lon(km_east, latitude) → float

Expresses given kilometers to the east in longitude degrees.

Parameters

km_east (float) – Longitude expressed in km going east from longitude 0, at the given latitude
latitude (float) – Latitude in degrees

Returns

float – Longitude in degrees

mobilipy.privacy.lat_to_km(latitude) → float

Expresses given latitude in kilometers to the north

Parameters: latitude (float) – Latitude in degrees.
Returns: float – Latitude expressed in kilometers to the north

mobilipy.privacy.lon_to_km(latitude, longitude) → float

Expresses given longitude in kilometers to the east

Parameters

latitude (float) – Latitude expressed in degrees
longitude (float) – Longitude expressed in degrees

Returns

float – Longitude as kilometers to the east

mobilipy.privacy.obfuscate(df, locations, radius=100, offset=30, mode='remove') → pandas.core.frame.DataFrame

Obfuscates the regions of points given in ‘locations’ parameter by either removing all the points in their proximity, or changing the location of these points to one, noisy location in the proximity circle.

Parameters

df (pandas.DataFrame) – DataFrame with ‘latitude’ and ‘longitude’ columns.
locations (list) – List of locations given as (latitude, longitude) tuples
radius (int, optional) – Radius of the obfuscation circle. Defaults to 100.
offset (int, optional) – Smallest distance from the perimeter of the obfuscation circle at which the location of interest must be located. Defaults to 30.
mode (str, optional) – Obfuscation mode, can be either ‘remove’ or ‘assign’. Defaults to ‘remove’.

Returns

pandas.DataFrame – DataFrame with obfuscated regions

mobilipy.privacy.shift_point(point, latitude_shift_km, longitude_shift_km)

Shifts the coordinates of a given point.

Parameters

point (tuple) – Starting point as (latitude, longitude), in degrees
latitude_shift_km (float) – Latitude offset in kilometers
longitude_shift_km (float) – Longitude offset in kilometers

Returns

tuple – Shifted point expressed as (latitude, longitude)