presentation from the University of Zurich GIScience Colloquium on October 10th, 2017 (http://www.agenda.uzh.ch/record.php?id=35880&group=58)

1. Anita Graser
Scientist, Center for Mobility Systems – AIT Austrian Institute of Technology
GISCIENCE FOR DYNAMIC
TRANSPORTATION SYSTEMS

2. ABOUT
Anita Graser
Scientist @ AIT Austrian Institute of Technology
 QGIS user since 2008
 MSc in Geomatics 2010
 QGIS Project Steering Committee since 2013
 OSGeo Director since 2015
 Moderator on GIS.StackExchange.com
 Author of „Learning QGIS“ (1st ed 2013), „QGIS Map
Design“ (2016) & „QGIS 2 Cookbook“ (2016)
@underdarkGIS

3. ABOUT – AIT
MOBILITY SYSTEMS

4. Movement
ContextNetwork
GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS

5. VISUALIZE ALL THE THINGS!
Matejka, J., & Fitzmaurice, G. (2017, May). Same stats, different graphs: Generating datasets with varied appearance and identical statistics
through simulated annealing. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1290-1294). ACM.

6. Network
GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS

7. ASSESSING NETWORK DATA QUALITY
Graser, A., Straub, M., & Dragaschnig, M. (2013). A comparative study of OpenStreetMap and the official Austrian reference graph for vehicle
routing. ICA Workshop on Street Networks and Transport, 26th International Cartographic Conference, Dresden, Germany.
OSM (black dashed) – GIP (grey solid)

8. ASSESSING NETWORK DATA QUALITY
Graser, A., Straub, M., & Dragaschnig, M. (2014). Towards an open source analysis toolbox for street network comparison: indicators, tools and
results of a comparison of OSM and the official Austrian reference graph. Transactions in GIS, 18(4), 510-526. doi:10.1111/tgis.12061.
Turn restrictions One-ways

9. ROUTING PEDESTRIANS
911.10.2017Graser, A. (2016) Integrating Open Spaces Into OpenStreetMap Routing Graphs for Realistic Crossing Behavior in Pedestrian Navigation. GI_Forum
‒ Journal for Geographic Information Science, 1-2016, 217-230, doi:10.1553/giscience2016_01_s217.

10. Pedestrian-centered navigation instructions
 using information from globally available OpenStreetMap database
 automatic selection of most suitable landmark
PROVIDING LANDMARK-BASED INSTRUCTIONS
Graser, A. (2017). Towards landmark-based instructions for pedestrian navigation systems using OpenStreetMap, AGILE2017, Wageningen,
Netherlands.

11. http://bit.do/perron
DEMO WEBSITE

12. Context
GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS

13. Asamer, J., Graser, A., Heilmann, B., & Ruthmair, M. (2016) Sensitivity Analysis for Energy Demand Estimation of Electric Vehicles. Transportation
Research Part D: Transport and Environment, Volume 46, Pages 182-199
Graser, A., Asamer, J., & Ponweiser, W. (2015). The elevation factor: Digital elevation model quality and sampling impacts on electric vehicle energy
estimation errors. In Models and Technologies for Intelligent Transportation Systems (MT-ITS), 2015 International Conference on (pp. 81-86).
PREDICTING E-VEHICLE ENERGY CONSUMPTION
Example route profile
estimated difference:
+12.96kWh (EU-DEM)
+31.94kWh (SRTM3.0)

14. PROVIDING PLANNING INPUT
Graser, A. (2017). Tessellating Urban Space based on street intersections & movement barriers. GI_Forum ‒ Journal of Geographic Information
Science, 1-2017, 114-125,
Network Planning area Network nodes Movement barriers
Demand forecastShopping POIsPopulationTessellation cells

15. Movement
GISCIENCE FOR DYNAMIC TRANSPORTATION SYSTEMS

16. 16
MEASURING MOVEMENT
Widhalm, P., Nitsche, P., & Brändie, N. (2012, November). Transport mode detection with realistic smartphone sensor data. In Pattern Recognition
(ICPR), 2012 21st International Conference on (pp. 573-576). IEEE.

17. MEASURING MOVEMENT
17
Nitsche, P., Widhalm, P., Breuss, S., & Maurer, P. (2012). A strategy on how to utilize smartphones for automatically reconstructing trips in travel
surveys. Procedia-Social and Behavioral Sciences, 48, 1033-1046.

18. TRACKING ACTIVE MOBILITY

19. dts.ait.ac.at/projects/cycletripmap
MEASURING POPULARITY

20. Which routes are popular for commuting,
which for leisure trips?
How does actual bicycle traffic compare to
the official bicycle route network?
Do cyclists choose alternative routes or
avoid certain junctions / regions?
MEASURING POPULARITY
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.

21. ANALYZING URBAN TRAFFIC
Time Manager – https://anitagraser.com/projects/time-manager/

22. MONITORING & FORECASTING TRAFFICS
Ulm, M., Heilmann, B., Asamer, J., Graser, A., & Ponweiser, W. (2015). Identifying Congestion Patterns in Urban Road Networks Using Floating Car
Data. In Transportation Research Board 94th Annual Meeting (No. 15-1231).

23. Making sense of data – https://anitagraser.com/2016/09/18/movement-data-in-gis-issues-ideas/
MAKING SENSE OF DATA

24. MAKING SENSE OF DATA
https://github.com/dts-ait/qgis-edge-bundling
Graser, A., Schmidt, J., Roth, F. & Brändle, N. (accepted) Untangling Origin-Destination Flows in Geographic Information Systems. Information
Visualization ‒ Special Issue on Visual Movement Analytics.
Raw OD flows Edge bundling

25. ANALYZING MASSIVE MOVEMENT DATA
Widhalm, P., Yang, Y., Ulm, M., Athavale, S., & González, M. C. (2015). Discovering urban activity patterns in cell phone data. Transportation, 42(4),
597-623.
Length of stay Arrival time
Length of stay
Lengthofstay
Arrival time
Count
CountCummulative

26. Office & administrationSparse residential (mixed)
ANALYZING MASSIVE MOVEMENT DATA
Widhalm, P., Yang, Y., Ulm, M., Athavale, S., & González, M. C. (2015). Discovering urban activity patterns in cell phone data. Transportation, 42(4),
597-623.

27. ANALYZING MASSIVE MOVEMENT DATA

28. “future work on trajectory data mining should be scalable to handle massive data."
(Mazimpaka & Timpf 2016)
"we routinely come up against the limits of traditional mapbased overviews of big
data" (Robinson et al. 2017)
Research themes include: scalability of visualization solutions and data, data size and
multi-dimensionality, data filtering, visualizing time-dependent/temporal data, and
visualizing qualitative data. (Çöltekin et al. 2017)
ANALYZING MASSIVE MOVEMENT DATA
Mazimpaka, J. D., & Timpf, S. (2016). Trajectory data mining: A review of methods and applications. Journal of Spatial Information Science, 2016(13), 61-99.
Robinson, A. C., Demšar, U., Moore, A. B., Buckley, A., Jiang, B., Field, K., ... & Sluter, C. R. (2017). Geospatial big data and cartography: research challenges and
opportunities for making maps that matter. International Journal of Cartography, 1-29.
Arzu Çöltekin, Susanne Bleisch, Gennady Andrienko & Jason Dykes (2017). Persistent challenges in geovisualization – a community perspective, International
Journal of Cartography.

29. OSGeo Stack
ANALYZING MASSIVE MOVEMENT DATA
https://anitagraser.com/2017/08/27/getting-started-with-geomesa-using-geodocker/

30. CONTACT
Anita Graser – anita.graser@ait.ac.at
@underdarkGIS