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pymove 3.0.1

Use PyMove and go much further

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What is PyMove
PyMove is a Python library for processing and visualization
of trajectories and other spatial-temporal data.
We will also release wrappers to some useful Java libraries
frequently used in the mobility domain.
Read the full documentation on ReadTheDocs

Main Features
PyMove proposes:

A familiar and similar syntax to Pandas;

Clear documentation;

Extensibility, since you can implement your main data structure by
manipulating other data structures such as
Dask DataFrame, numpy arrays, etc., in addition to adding new modules;

Flexibility, as the user can switch between different data structures;

Operations for data preprocessing, pattern mining and data visualization.

Creating a Virtual Environment
It is recommended to create a virtual environment to use pymove.
Requirements: Anaconda Python distribution installed and accessible

In the terminal client enter the following where env_name is the name
you want to call your environment, and replace x.x with the Python version
you wish to use. (To see a list of available python versions first,
type conda search "^python$" and press enter.)

conda create -n <env_name> python=x.x

Press y to proceed. This will install the Python version and all the
associated anaconda packaged libraries at path_to_your_anaconda_location/anaconda/envs/env_name

Activate your virtual environment. To activate or switch into your
virtual environment, simply type the following where yourenvname is the
name you gave to your environment at creation.

conda activate <env_name>

Now install the package from either conda, pip or github

Conda instalation

conda install -c conda-forge pymove

Pip installation

pip install pymove

Github installation

Clone this repository

git clone https://github.com/InsightLab/PyMove

Switch to folder PyMove

cd PyMove

Switch to a new branch

git checkout -b developer

Make a pull of branch

git pull origin developer

Install pymove in developer mode

make dev

For windows users
If you installed from pip or github, you may encounter an error related to
shapely due to some dll dependencies. To fix this, run conda install shapely.

Examples
You can see examples of how to use PyMove here

Mapping PyMove methods with the Paradigms of Trajectory Data Mining

ZHENG 2015.

1: Spatial Trajectories → pymove.core

MoveDataFrame
DiscreteMoveDataFrame

2: Stay Point Detection → pymove.preprocessing.stay_point_detection

create_or_update_move_stop_by_dist_time
create_or_update_move_and_stop_by_radius

3: Map-Matching → pymove-osmnx

4: Noise Filtering → pymove.preprocessing.filters

by_bbox
by_datetime
by_label
by_id
by_tid
clean_consecutive_duplicates
clean_gps_jumps_by_distance
clean_gps_nearby_points_by_distances
clean_gps_nearby_points_by_speed
clean_gps_speed_max_radius
clean_trajectories_with_few_points
clean_trajectories_short_and_few_points
clean_id_by_time_max

5: Compression → pymove.preprocessing.compression

compress_segment_stop_to_point

6: Segmentation → pymove.preprocessing.segmentation

bbox_split
by_dist_time_speed
by_max_dist
by_max_time
by_max_speed

7: Distance Measures → pymove.distances

medp
medt
euclidean_distance_in_meters
haversine

8: Query Historical Trajectories → pymove.query.query

range_query
knn_query

9: Managing Recent Trajectories

10: Privacy Preserving

11: Reducing Uncertainty

12: Moving Together Patterns

13: Clustering → pymove.models.pattern_mining.clustering

elbow_method
gap_statistics
dbscan_clustering

14: Freq. Seq. Patterns

15: Periodic Patterns

16: Trajectory Classification

17: Trajectory Outlier / Anomaly Detection → pymove.semantic.semantic

outliers
create_or_update_out_of_the_bbox
create_or_update_gps_deactivated_signal
create_or_update_gps_jump
create_or_update_short_trajectory
create_or_update_gps_block_signal
filter_block_signal_by_repeated_amount_of_points
filter_block_signal_by_time
filter_longer_time_to_stop_segment_by_id

Cite
The library was originally created during the bachelor's thesis of 2 students from the Federal University of Ceará, so you can cite using both works.
@mastersthesis{arina2019,
title = {Uma arquitetura e implementação do módulo de pré-processamento para biblioteca PyMove},
author = {Arina De Jesus Amador Monteiro Sanches},
year = 2019,
school = {Universidade Federal Do Ceará},
type = {Bachelor's thesis}
}
@mastersthesis{andreza2019,
title = {Uma arquitetura e implementação do módulo de visualização para biblioteca PyMove},
author = {Andreza Fernandes De Oliveira},
year = 2019,
school = {Universidade Federal Do Ceará},
type = {Bachelor's thesis}
}

Publications

Uma arquitetura e implementação do módulo de pré-processamento para biblioteca PyMove
Uma arquitetura e implementação do módulo de visualização para biblioteca PyMove
Avaliação de técnicas de aumento de dados para trajetórias
Implementação de algoritmos para análise de similaridade de trajetória na biblioteca PyMove

Useful list of related libraries and links

Handling GPS Data with Python
mplleaflet - Easily convert matplotlib plots from Python into interactive Leaflet web maps
Pykalman
Ramer-Douglas-Peucker algorithm
Knee point detection in Python
TrajSuite Java Library
GraphHopper Map-Matching Java Library