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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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