Movement data analysis is of high relevance in various application domains of data science. However, movement data is rarely collected in lab settings. Many datasets are created for different purposes than the (scientific) analysis they are used for. Therefore, data quality (i.e. fitness for use in analyses) is rarely ideal. Understanding of data quality is essential for choosing suitable analysis methods and interpreting their results but gaining a proper understanding takes time. Indeed, data exploration can take up to 50% of the time spent on analysis. Graphical data exploration tools, in particularly, are needed to support analysts. This talk covers the challenges of movement data exploration and the current advances in tool development.
Anita Graser is a researcher, open source GIS developer, and author. She works at the Austrian Institute of Technology in Vienna, teaches Python for QGIS at UNIGIS Salzburg and serves on the QGIS project steering committee. She has published several books about QGIS, including “Learning QGIS” and “QGIS Map Design”. In 2020, she was awarded the OSGeo Sol Katz award. Her latest project is MovingPandas, a Python library for analyzing movement data. You can find out more about her work on anitagraser.com and follow her on Twitter @underdarkGIS.