The “Movement data in GIS” series discusses all things related to working with movement data aka. trajectories in GIS, covering concepts, libraries, desktop GIS, databases, and big data technology.

Here are all posts in chronological order:

1. Issues & ideas 
2. Visualizing individual trajectories
3. Visualizing massive trajectory datasets 
   Update 2017: Trajectory aggregation code & sample data
   Update 2020: Trajectory aggregation in MovingPandas
4. Variations in movement over time
5. Current research topics
6. Updates from AGILE2017
7. Animated trajectories with TimeManager
   Extra: GPS Trajectories with error ellipses
8. Edge bundling for flow maps
9. Trajectory data models
   Update 2020: From Simple Features to Moving Features and Beyond?
10. Open tools for AIS tracks from MarineCadastre.gov
11. FOSS4G2017 talk recordings
12. Why you should be using PostGIS trajectories
13. Timestamp labels for trajectories
14. Updates from GI_Forum 2018
15. Writing a PL/pgSQL stop detection function for PostGIS trajectories
16. Towards pure Python trajectories using GeoPandas
17. Spatial analysis of GeoPandas trajectories
18. Creating evaluation data for trajectory predictions
19. Splitting trajectories by date
20. Trajectools v1 released!
21. New interactive notebook to get started with MovingPandas
22. Movement data in GIS and the AI hype
23. Trajectories in context
24. MovingPandas hands-on tutorials
25. Moving object databases
26. Towards a template for exploring movement data
27. Extracting trip origin clusters from MovingPandas trajectories
28. Open geospatial tools for movement data exploration
29. Web apps with movement data using mobilitydb-sqlalchemy
30. Synchronized trajectory animations with QGIS temporal controller
31. Exploring massive movement datasets
    Part 1: M³ Massive Movement Model: aggregating movement data using prototypes
    Part 2: Generating trajectories from massive movement datasets
    Part 3: Extracting trajectory-based flows between M³ prototypes
32. “Exploring movement data” 🎬
33. “Exploratory analysis of massive movement data” 🎬
34. A protocol for exploring movement data
35. Stop detection & analysis with MovingPandas
36. Trucks from space
37. “Understanding Movement Data” 🎬
38. MovingPandas now supports local coordinates
39. New OGC Moving Features JSON support in MovingPandas
40. Detecting close encounters using MobilityDB 1.0
41. Visualizing trajectories with QGIS & MobilityDB
42. Deep learning from trajectory data
43. How to use Kaggle’s Taxi Trajectory Data in MovingPandas

Relevant source code repositories:

• https://github.com/movingpandas/movingpandas
• https://github.com/movingpandas/movingpandas-examples
• https://github.com/anitagraser/EDA-protocol-movement-data
• https://github.com/anitagraser/qgis-processing-trajectory
• https://github.com/dts-ait/qgis-edge-bundling
• https://bitbucket.org/anitagraser/pg-trajectory

My scientific publications on this topic:

• Dissertation
  • Graser, A. (2021) Exploratory analysis of massive movement data. PhD thesis. University of Salzburg, Salzburg, Austria.
• Book chapters
  • Graser, A., Dragaschnig, M., & Koller, H. (2021). Exploratory analysis of massive movement data. In: Handbook of Big Geospatial Data, Editors: Werner, Martin, Chiang, Yao-Yi (pp. 285-319). Springer.
  • Graser, A. & Dragaschnig, M. (2020). Potentiale entdecken ‒  Explorative räumliche Analyse von Bewegungsdaten. In Geo-IT in Mobilität und Verkehr: Geoinformatik als Grundlage für moderne Verkehrsplanung und Mobilitätsmanagement (pp. 137-154). Wichmann Verlag/VDE. ISBN 978-3-87907-682-6.
• Journal papers
  • Graser, A. (2022). The State of Trajectory Visualization in Notebook Environments. GI_Forum 2022, 10(2), 73-91. doi:10.1553/giscience2022_02_s73.
  • Graser, A. (2021). An exploratory data analysis protocol for identifying problems in continuous movement data. Journal of Location Based Services. doi:10.1080/17489725.2021.1900612.
  • Graser, A., Widhalm, P., & Dragaschnig, M. (2020). Extracting Patterns from Large Movement Datasets. GI_Forum – Journal of Geographic Information Science, 1-2020, 153-163. doi:10.1553/giscience2020_01_s153. 
  • 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.
  • Graser, A. & Dragaschnig, M. (2020). Open Geospatial Tools for Movement Data Exploration. KN ‒ Journal of Cartography and Geographic Information, 70(1), 3-10. doi:10.1007/s42489-020-00039-y.
  • Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum ‒ Journal of Geographic Information Science 2019, 1-2019, 54-68. doi:10.1553/giscience2019_01_s54.
  • Graser, A. (2018) Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases. GI_Forum ‒ Journal of Geographic Information Science, 1-2018, 16-33. doi: 10.1553/giscience2018_01_s16.
  • Graser, A., Schmidt, J., Roth, F., & Brändle, N. (2017) Untangling Origin-Destination Flows in Geographic Information Systems. Information Visualization 2019, Vol. 18(1) 153–172. doi:10.1177/1473871617738122.
  • Straub, M., & Graser, A. (2015). Learning from Experts: Inferring Road Popularity from GPS Trajectories. GI_Forum ‒ Journal for Geographic Information Science, 1-2015, 41-50, doi:10.1553/giscience2015s41.
• Other papers
  • Graser, A., Jalali, A., Lampert, J., Weißenfeld, A., & Janowicz, K. (2023). Deep Learning From Trajectory Data: a Review of Neural Networks and the Trajectory Data Representations to Train Them. Workshop on Big Mobility Data Analysis BMDA2023 in conjunction with EDBT/ICDT 2023.
  • Jonietz, D., Sester, M., Stewart, K., Winter, S, Tomko, M., & Xin, Y. (eds). Urban Mobility Analytics (Dagstuhl Seminar 22162). Dagstuhl Reports, 12(4), 26–53. https://doi.org/10.4230/DagRep.12.4.26
  • Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Anderson, T., Aref, W., Andrienko, G., Andrienko, N., Cao, Y., Chawla, S., Cheng, R., Chrysanthis, P., Fei, X., Ghinita, G., Graser, A., Gunopulos, D., Jensen, C., Kim, J.-S., … Zimányi, E. (2022). Mobility Data Science (Dagstuhl Seminar 22021). Dagstuhl Reports, 12(1), 1–34. https://doi.org/10.4230/DagRep.12.1.1
  • Graser, A. (2021). Notebook-based Visual Analysis of Large Tracking Datasets. Workshop on Big Mobility Data Analysis BMDA2021 in conjunction with EDBT/ICDT 2021. – 🎬 video
  • Graser, A., & Dragaschnig. M. (2020). Exploring movement data in notebook environments. Workshop on Information Visualization of Geospatial Networks, Flows and Movement (MoVis) in conjunction with IEEE VIS 2020. – 🎬 video
  • Graser, A., Dragaschnig, M., Widhalm, P., Koller, H., & Brändle, N. (2020). Exploratory Trajectory Analysis for Massive Historical AIS Datasets. In: 21st IEEE International Conference on Mobile Data Management (MDM) 2020. doi:10.1109/MDM48529.2020.00059
  • Graser, A., Schmidt, J., Dragaschnig, M., & Widhalm, P. (2019). Data-driven Trajectory Prediction and Spatial Variability of Prediction Performance in Maritime Location Based Services, LBS 2019, 11-13 November 2019, Vienna, Austria.
  • Graser, A., & Widhalm, P. (2018). Modelling Massive AIS Streams with Quad Trees and Gaussian Mixtures. In: Mansourian, A. et al. (Eds.), 2018. Geospatial Technologies for All: short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science. Lund, Sweden.
  • Graser, A., Schmidt, J., & Widhalm, P. (2018). Predicting trajectories with probabilistic time geography and massive unconstrained movement data. Workshop on Analysis of Movement Data AMD2018 in conjunction with GIScience 2018, Melbourne, Australia.
  • Koller, H., Widhalm, P., Dragaschnig, M., & Graser, A. (2015). Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories. In Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on (pp. 2557-2561). IEEE.