Trackintel: An open-source Python library for human mobility analysis

Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducib...

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Bibliographic Details
Published in:Computers, environment and urban systems Vol. 101; p. 101938
Main Authors: Martin, Henry, Hong, Ye, Wiedemann, Nina, Bucher, Dominik, Raubal, Martin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2023
Subjects:
Data mining
Human mobility analysis
Open-source software
Python
Tracking studies
Transport planning
Transport planning
Tracking studies
Open-source software
Data mining
Human mobility analysis
Python
ISSN:0198-9715, 1873-7587
Online Access:Get full text
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Summary:Over the past decade, scientific studies have used the growing availability of large tracking datasets to enhance our understanding of human mobility behavior. However, so far data processing pipelines for the varying data collection methods are not standardized and consequently limit the reproducibility, comparability, and transferability of methods and results in quantitative human mobility analysis. This paper presents Trackintel, an open-source Python library for human mobility analysis. Trackintel is built on a standard data model for human mobility used in transport planning that is compatible with different types of tracking data. We introduce the main functionalities of the library that covers the full life-cycle of human mobility analysis, including processing steps according to the conceptual data model, read and write interfaces, as well as analysis functions (e.g., data quality assessment, travel mode prediction, and location labeling). We showcase the effectiveness of the Trackintel library through a case study with four different tracking datasets. Trackintel can serve as an essential tool to standardize mobility data analysis and increase the transparency and comparability of novel research on human mobility. The library is available open-source at https://github.com/mie-lab/trackintel. •Trackintel offers rich functionality for preprocessing and analyzing mobility data.•Implementation of standard model for movement data.•Using trackintel increases reproducibility and transferability of methods.•We conduct a comparative case study using four different tracking data sets.
ISSN:0198-9715
1873-7587
DOI:10.1016/j.compenvurbsys.2023.101938