Anita Graser: Analyzing movement data
Channel: Tomislav Hengl (OpenGeoHub Foundation)
Date: 2019-10-16

The analysis of movement is an important research topic in, for example, geography, ecology, visual analytics, GIScience as well as in application domains such as urban, maritime, and aviation research. Movement data analysis requires tools for the manipulation and visualization of movement or trajectory data. Essential trajectory manipulation tools include, for example: clipping trajectories to an area of interest, annotating trajectories with context information, splitting raw location streams into meaningful trajectories and stops, as well as for downsampling and generalizing trajectory data. While Geographic Information Systems (GIS) may appear like a natural fit for these kinds of analysis, GIS data structures and functions generally suffer from a lack of support for dynamic phenomena like movement.

This workshop introduces MovingPandas for analyzing movement data in Python. MovingPandas is based on the established Pandas Python Data Analysis Library and its spatial extension GeoPandas. 
Participants will follow the tutorial notebook and then (optional) test algorithms using their own data.

Installation instructions are provided at https://github.com/anitagraser/movingpandas

References:
1. Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum ‒ Journal of Geographic Information Science 2019, 1-2019, 54-68. https://doi.org/10.1553/giscience2019_01_s54