Detect stop locations in time-ordered (lat, lon) location data
Project description
# 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:
![img](https://ulfaslak.com/files/infostop_example_code1.png)
A stop location solution can be obtained like:
![img](https://ulfaslak.com/files/infostop_example_code2.png)
Plotting it onto a map:
![img](https://ulfaslak.com/files/infostop_example_map.png)
## 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
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