Treffer: Anita Graser 'Exploring Movement Data' at the University of Liverpool Geographic Data Science Lab (GDSL) Brown Bag Seminar Series

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”. Her latest project is MovingPandas, a Python library for analyzing movement data. You can follow her on Twitter @underdarkGIS. --- Reference List [Andrienko et al. 2016] Andrienko, G., Andrienko, N., & Fuchs, G. (2016). Understanding movement data quality. Journal of location Based services, 10(1), 31-46. [Demsar et al. 2015] Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., Weiskopf, D., & Weibel, R. (2015). Analysis and visualisation of movement: an interdisciplinary review. Movement ecology, 3(1), 5. [Graser et al. 2020] Graser. A., Widhalm, P., & Dragaschnig, M. (2020). The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science. doi:10.1080/13658816.2020.1776293. [Patroumpas et al. 2017] Patroumpas, K., Alevizos, E., Artikis, A., Vodas, M., Pelekis, N., & Theodoridis, Y. (2017). Online event recognition ...