MovingPandas: Efficient structures for movement data in Python

61Citations
Citations of this article
58Readers
Mendeley users who have this article in their library.

Abstract

Movement data analysis is a high-interest topic in many scientific domains. Even though Python is the scripting language of choice in the GIS world, currently there is no Python library that would enable researchers and practitioners to interact with and analyse movement data efficiently. To close this gap, we present MovingPandas, a new Python library for dealing with movement data. Its development is based on an analysis of state-of-the-art conceptual frameworks and existing implementations (in PostGIS, Hermes, and the R package trajectories). We describe how MovingPandas avoids limitations of Simple Feature-based movement data models commonly used to handle trajectories in the GIS world. Finally, we present the current state of the MovingPandas implementation and demonstrate its use in stand-alone Python scripts, as well as within the context of the desktop GIS application QGIS. This work represents the first step towards a general-purpose Python library that enables researchers and practitioners in the GIS field and beyond to handle and analyse movement data more efficiently.

Cite

CITATION STYLE

APA

Graser, A. (2019). MovingPandas: Efficient structures for movement data in Python. GI_Forum, 7(1), 54–68. https://doi.org/10.1553/GISCIENCE2019_01_S54

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free