Visualizing Trajectories in Notebook Environments
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Gaining insights from trajectory datasets is a challenging task that requires suitable visual data representations. There is a considerable gap between the state of the art cartographic techniques presented in the literature and currently available spatial data science toolboxes. This talk presents the current state of geospatial visualization tools for trajectory data, focusing on the Python and Jupyter notebooks ecosystem. The identified shortcomings provide pointers for further scientific software development, as well as a reference for data scientists that helps to pick the best fitting tool for a specific job.
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Visualizing Trajectories in Notebook Environments
- 1. THE STATE OF TRAJECTORY VISUALIZATION IN NOTEBOOK ENVIRONMENTS GI_Salzburg, 2022-07-07 Anita Graser @underdarkGIS
- 3. Accept messiness in data Need to understand Causes of bias & messiness Consequences of using such data in analyses Data visualization & exploratory approaches CHALLENGES Brunsdon & Comber (2020) Big issues for big data Graser & Dragaschnig (2020) Open Geospatial Tools for Movement Data Exploration
- 4. REALITY CHECK Graser et al. (2020) Exploratory Trajectory Analysis for Massive Historical AIS Datasets
- 5. REALITY CHECK Andrienko et al. (2016) Understanding movement data quality. Journal of Location Based Services.
- 6. VISUAL ANALYTICS RESEARCH Andrienko, G., & Andrienko, N. (2008). Exploration of massive movement data: a visual analytics approach. AGILE’08.
- 7. Python PyMove: Python library to simplify queries and visualization of trajectories and other spatial-temporal data. (first GH commit: 2018- 09-09) MovingPandas: Trajectory classes and functions built on top of GeoPandas. (first GH commit: 2018-12-16) Traja: Python tools for 2D spatial trajectory data. (first GH commit: 2019-01-13) trackintel: a framework for spatio-temporal analysis of movement trajectory and mobility data. (first GH commit: 2019-01-20) scikit-mobility: Mobility analysis in Python. (first GH commit: 2019- 04-28) MovinPy: Process and analyze mobility data. (first GH commit: 2020-07-23) HuMobi: a library for human mobility analyses implemented in Python. (first GH commit: 2021-06-02) PTRAIL: parallel computation library for Mobility Data Preprocessing and feature generation. (first GH commit: 2021-05-31) TransBigData: transportation spatio-temporal big data processing, analysis and visualization. (first GH commit: 2021-10-17) MOVEMENT ANALYSIS TOOLS C++ Tracktable (with Python API): moving object trajectory analysis in C++ and Python. (first GH commit: 2016- 04-10) MEOS (with Python API): Mobility Engine, Open Source is a C++ library which makes it easy to work with temporal and spatio-temporal data. (first GH commit: 2020-04-19) MoveTK: a library for computational movement analysis written in C++. (first GH commit: 2020-09-09) R Review paper: Joo, R., Boone, M. E., Clay, T. A., Patrick, S. C., Clusella‐Trullas, S., & Basille, M. (2020). Navigating through the R packages for movement. Journal of Animal Ecology, 89(1), 248-267. Databases MobilityDB: A geospatial trajectory data management & analysis platform, built on PostgreSQL and PostGIS. (first GH commit: 2019-02-17) mobilitydb-python: Python driver for MobilityDB. mobilitydb-sqlalchemy: MobilityDB extensions for SQLAlchemy https://github.com/anitagraser/movement-analysis-tools
- 8. Pandas … developed in the context of financial modeling extensive set of tools for working with dates, times, and time-indexed data GeoPandas … extends the datatypes used by Pandas to allow spatial operations on geometric types WHY PANDAS? https://anitagraser.com/2018/11/18/movement-data-in-gis-16-towards-pure-python-trajectories-using-geopandas/
- 9. Visualization library PyMove Folium MovingPandas GeoViews & Matplotlib Traja Matplotlib Trackintel Matplotlib Scikit-mobility Folium PTRAIL Folium TransBigData Kepler.GL WHAT ABOUT VISUALIZATIONS? Folium
- 10. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). ✅ Interactive plot ✅ Background maps ✅ Object identity ⏹⏹ Direction ⏹⏹ Speed 🆘 Time
- 11. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). ✅ Interactive plot ✅ Background maps ⏹⏹ Object identity ⏹⏹ Direction ✅ Speed 🆘 Time
- 12. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). ✅ Interactive plot ✅ Background maps ✅ Object identity ⏹⏹ Direction 🆘 Speed 🆘 Time Folium
- 13. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). 🆘 Static plot ⏹⏹ Only OSM network plot ✅ Object identity 🆘 Direction 🆘 Speed 🆘 Time
- 14. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). 🆘 Static plot 🆘 No background maps 🆘 Object identity ⏹⏹ Direction 🆘 Speed ✅ Time
- 15. Data: “Heterogeneous Integrated Dataset for Maritime Intelligence, Surveillance, and Reconnaissance" (Ray et al. 2018). 🆘 Static plot 🆘 No background maps ✅ Object identity 🆘 Direction 🆘 Speed 🆘 Time
- 16. ✅ Interactive plots ✅ Background maps 🆘 Object identity ✅ Direction ✅ Speed ✅ Time
- 18. Standardization efforts OGC MovingFeatures Scientific software development MovingPandas, Scikit-mobility et al. Spatial data apps RECENT DEVELOPMENTS 18
Editor's Notes
- Brunsdon, C., & Comber, A. (2020). Big issues for big data: challenges for critical spatial data analytics. Journal of Spatial Information Science, 2020(21), 89-98. doi:10.5311/JOSIS.2020.21.625
- Andrienko, G., & Andrienko, N. (2008). Exploration of massive movement data: a visual analytics approach. AGILE’08.
- MovingPandas provides static plots using Matplotlib and interactive maps using HoloViews GeoViews (based on Bokeh). The default plot for a set of trajectories (TrajectoryCollection) draws each trajectory in a different color. As shown in Figure 2, background map tiles, as well as markers for trajectory start and end locations, can be readily added and customized. Alternatively, other DataFrame column names (or the keyword ‘speed’) can be specified to color the trajectory segments accordingly, as shown in Figure 3.
- Scikit-mobility provides interactive plots using Folium. The default plot for a TrajDataFrame draws each trajectory in a different color and automatically puts green and red markers at the start and end locations, respectively, as shown in Figure 4. To enhance rendering performance, the plot function by default does not plot all trajectories and generalizes the rendered trajectories. These preprocessing steps are communicated to the user in the form of UserWarnings “Only the trajectories of the first 10 users will be plotted. Use the argument `max_users` to specify the desired number of users, or filter the TrajDataFrame.” and “If necessary, trajectories will be down-sampled to have at most `max_points` points. To avoid this, specify `max_points=None`.”
- Trackintel provides static plots using Matplotlib. The default plot for trip legs created from position fixes draws each trip leg in a color indicating the moving object id (user). As Figure 5 show, in contrast to the previous libraries, the default plot function does not provide background maps. Instead, the function provides a plot_osm keyword that “will download an OSM street network and plot below the triplegs” (Trackintel documentation, 2022). This is certainly useful in the context of human movement in local urban environments but not suitable for the ship movement dataset used in this example.
- Given the fast advances of data science tools and workflows, what is GIScience doing to ensure that its knowhow contributes to spatial data science practices? And what should we do (more)?