infostop 0.0.13

Detect stop locations in time-ordered (lat, lon) location data

Infostop

Python package for detecting stop locations in mobility data

This package implements the algorithm described in (paper not written yet), for detecting stop locations in time-ordered location data.

Usage

Given a location trace such as:

>>> data 
array([[ 55.75259295,  12.34353885 ],
       [ 55.7525908 ,  12.34353145 ],
       [ 55.7525876 ,  12.3435386  ],
       ...,
       [ 63.40379175,  10.40477095 ],
       [ 63.4037841 ,  10.40480265 ],
       [ 63.403787  ,  10.4047871  ]])

A stop location solution can be obtained using:

>>> import infostop
>>> labels = infostop.label_trace(data)

Here, labels matches data in size, and can easily be mapped back onto data:

>>> np.hstack([data, labels.reshape(-1, 1)])
array([[ 55.75259295,  12.34353885,   0.        ],
       [ 55.7525908 ,  12.34353145,   0.        ],
       [ 55.7525876 ,  12.3435386 ,   0.        ],
       ...,
       [ 63.40379175,  10.40477095, 164.        ],
       [ 63.4037841 ,  10.40480265, 164.        ],
       [ 63.403787  ,  10.4047871 , 164.        ]])

Plotting this onto a map:

Advantages

• Simplicity: At its core, the method works by two steps. (1) Reducing the location trace to the medians of each stationary event and (2) embedding the resulting locations into a network that connects locations that are within a user-defined distance and clustering that network.
• Flow based: Spatial clusters correspond to collections of location points that contain large amounts of flow when represented as a network. This enables the recovery of locations where traces slightly overlap.
• Speed: First the point space is reduced to the median of stationary points, then pairwise distances between these medians are computed using a vectorized implementation of the haversine function, and finally the resulting network at some distance threshold is clustered using the C++ based Infomap implementation. For example, clustering 70.000 location points takes aroung 16 seconds.

Installation

pip install infostop