title: 'MovingPandas: A Python package for the analysis of movement data' tags:
- Python
- spatial data
- movement data
- mobility authors:
- name: Anita Graser orcid: 0000-0001-5361-2885 affiliation: "1, 2" affiliations:
- name: "AIT Austrian Institute of Technology, Vienna, Austria" index: 1
- name: "University of Salzburg, Austria" index: 2 date: 3 March 2020 bibliography: paper.bib
MovingPandas is a package for dealing with movement data. Movement data appear in many different scientific domains, including biology, ecology, chemistry, transport and logistics, astrophysics, remote sensing, and more.
MovingPandas implements a Trajectory class and corresponding methods based on GeoPandas. A trajectory has a time-ordered series of point geometries. These points and associated attributes are stored in a GeoDataFrame. MovingPandas implements spatial and temporal data access and analysis functions (covered in [@graser:2019]) as well as plotting functions.
When researchers and data analysts are working with movement data, there are a suite of data exploration activities that are often performed including:
- Creating and visualizing trajectories from consecutive time-stamped point location records
- Computing and visualizing movement characteristics, such as time between records, speed, and direction
- Extracting trip start and end locations, for example to generate origin destination matrices or filter by these location
- Clipping trajectories to an area of interest, thus limiting the geographic extent of the analysis
- Summarizing movements using aggregations, such as flow maps
The above operations are crucial to understanding a dataset and identifying issues that may need to be addressed with further data processing when beginning an analysis. However, there isn't a tool that makes these tasks easy for users in the Python open source package landscape.
In addition to detailed API documentation and example code executed by pytest, MovingPandas documentation includes multiple tutorial notebooks that demonstrate its functionality using case studies.
These notebooks can be executed locally or online using MyBinder.
These tutorial notebooks document how to use MovingPandas to explore different types of movement datasets (including, for example, a day of vessel movements with high temporal resolution and multiple years of bird migration data with low temporal resolution).
MovingPandas's goal is to make working with movment data easier for scientists and data analysist who want to use open source Python tools for analysis and visualization.
MovingPandas depends upon GeoPandas [@kelsey_jordahl_2019_3545747], which facilitates reading and writing movement data from common spatial data formats. It also requires Matplotlib [@Hunter:2007] for static plots and hvplot [@philipp_rudiger_2020_3634720] for interactive plots.
To simplify dependency management and installation for non-experts, we maintain a version of MovingPandas on the conda-forge channel, which installs the system libraries upon which MovingPandas depends. This lowers the barrier to entry for people, particularly scientists, who are learning how to work with movement data in Python.
scikit-mobility [@pappalardo:2019] is a similar Python package which is in an early development stage and also deals with movement data. They implement TrajectoryDataFrames and FlowDataFrames on top of Pandas instead of GeoPandas. There is little overlap in the covered use cases and implemented functionality. MovingPandas focuses on spatio-temporal data exploration with corresponding functions for data manipulation and analysis. scikit-mobility on the other hand focuses on computing human mobility metrics, generating synthetic trajectories and assessing privacy risks.
There have been many contributors to MovingPandas that we are thankful for. We are also thankful for the feedback that we recieved through the software review implemented by pyOpenSci. Specifically we thank Jenny Palomino who has served as an editor for this review and the two reviewers: Martin Fleischmann and Ivan Ogasawara.