Publications

This page provides a chronologically ordered list of my work by publication type. For a quick overview, please check my Google Scholar profile. My ORCID is 0000-0001-5361-2885. I also serve as a reviewer for multiple journals, for details see my Publons profile.

Books:

  1. Dorman, M., Graser, A., Nowosad, J. & Lovelace, R. (2025). Geocomputation with Python. Chapman & Hall. ISBN: 9781032460659.
  2. Cutts, A., & Graser, A. (2018). Learn QGIS – Fourth Edition. Packt Publishing Ltd. ISBN: 9781788997423.
  3. Graser, A., & Peterson, G. N. (2018). QGIS Map Design – Second Edition. Locate Press. ISBN: 9780998547749.
  4. Graser, A. (2016). Learning QGIS – Third Edition. Packt Publishing Ltd. ISBN: 9781785880339.
  5. Graser, A., & Peterson, G. N. (2016). QGIS Map Design. Locate Press. ISBN: 9780989421751.
  6. Mandel, A., Olaya Ferrero, V., Graser, A., & Bruy, A. (2016). QGIS 2 Cookbook. Packt Publishing Ltd. ISBN: 9781783984961.
  7. Graser, A. (2014). Learning QGIS – Second Edition. Packt Publishing Ltd. ISBN: 9781784392031.
  8. Graser, A. (2013). Learning QGIS 2.0. Packt Publishing Ltd. ISBN: 9781782167488.

Book chapters:

  1. Arribas-Bel, D. & Graser, A. (2024). Geographic Data Science. In: A Research Agenda for Spatial Analysis.
  2. 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.
  3. 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.
  4. Straub, M., Graser, A., Loidl, M., Zagel, B., & Witzmann-Müller, U. (2020). Effizient geteilt-räumliche Optimierung von Verleihsystemen. In Geo-IT in Mobilität und Verkehr: Geoinformatik als Grundlage für moderne Verkehrsplanung und Mobilitätsmanagement (pp. 175-190). Wichmann Verlag/VDE.

Journal papers:

  1. Graser, A., Sutton, T., & Bernasocchi, M. (2025) The QGIS project: Spatial without compromise. Cell Patterns. https://doi.org/10.1016/j.patter.2025.101265
  2. Schlögl, M., Graser, A., Spiekermann, R., Lampert, J., & Steger, S. (2025) Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping (Brief communication), Natural Hazards and Earth System Sciences.
  3. Graser, A., Jalali, A., Lampert, J., Weißenfeld, A., & Janowicz, K. (2024). MobilityDL: A Review of Deep Learning From Trajectory Data. Geoinformatica. https://doi.org/10.1007/s10707-024-00518-8
  4. 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. S., Kim, J.-S., … Zimányi, E. (2024). Mobility Data Science: Perspectives and Challenges. ACM Transactions on Spatial Algorithms and Systems, 3652158. https://doi.org/10.1145/3652158
  5. Graser, A. (2022). The State of Trajectory Visualization in Notebook Environments. GI_Forum 2022, 10(2), 73-91. doi:10.1553/giscience2022_02_s73.
  6. Graser, A. (2021). An exploratory data analysis protocol for identifying problems in continuous movement data. Journal of Location Based Services, 15(2), 89-117. doi:10.1080/17489725.2021.1900612.
  7. Graser, A., Widhalm, P., & Dragaschnig, M. (2020). Extracting Patterns from Large Movement Datasets. GI_Forum 2020, 8(1), 153-163. doi:10.1553/giscience2020_01_s153. 
  8. 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.
  9. 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, 34(12), 2517-2540. doi:10.1080/13658816.2020.1776293.
  10. Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum 2019, 7(1), 54-68. doi:10.1553/giscience2019_01_s54.
  11. Graser, A., Schmidt, J., Roth, F., & Brändle, N. (2019). Untangling origin-destination flows in geographic information systems. Information Visualization, 18(1), 153-172. doi:10.1177/1473871617738122. (Article first published online: November 30, 2017)
  12. Graser, A. (2018). Evaluating Spatio-temporal Data Models for Trajectories in PostGIS Databases. GI_Forum 6(1), 16-33. doi:10.1553/giscience2018_01_s16.
  13. Fritze, R., Graser, A., & Sinnl, M. (2018). Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria. International Journal of Medical Informatics. doi:10.1016/j.ijmedinf.2017.12.008.
  14. Brezina, T., Graser, A., & Leth, U. (2017). Geometric methods for estimating representative sidewalk widths applied to Vienna’s streetscape surfaces database. Journal of Geographical Systems, 19(2), 157-174, doi:10.1007/s10109-017-0245-2.
  15. Graser, A. (2017). Tessellating Urban Space Based on Street Intersections and Barriers to Movement. GI_Forum 5(1), 114-125, doi:10.1553/giscience2017_01_s112.
  16. Graser, A. (2016). Integrating open spaces into OpenStreetMap routing graphs for realistic crossing behavior in pedestrian navigation. GI_Forum 2016, 1-2016, 217-230, doi:10.1553/giscience2016_01_s217.
  17. Graser, A., Leodolter, M., Koller, H., & Brändle, N. (2016). Improving vehicle speed estimates using street network centrality. International Journal of Cartography. doi:10.1080/23729333.2016.1189298. – pre-print
  18. Loidl, M., Wallentin, G., Cyganski, R., Graser, A., Scholz, J., & Haslauer, E. (2016). GIS and transport modeling—Strengthening the spatial perspective. ISPRS Int. J. Geo-Inf. 2016, 5, 84. doi:10.3390/ijgi5060084.
  19. 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, doi:10.1016/j.trd.2016.03.017. – pre-print
  20. Graser, A., & Olaya, V. (2015). Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS. ISPRS Int. J. Geo-Inf. 2015, 4, 2219-2245. doi:10.3390/ijgi4042219.
  21. Graser, A., & Straub, M. (2015). Improving Navigation: Automated Name Extraction for Separately Mapped Pedestrian and Cycle Links. GI_Forum 2015, 3(1), 546-556, doi:10.1553/giscience2015s546.
  22. Straub, M., & Graser, A. (2015). Learning from Experts: Inferring Road Popularity from GPS Trajectories. GI_Forum 3(1), 41-50, doi:10.1553/giscience2015s41.
  23. 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. – Zenodo version – pre-print
  24. Graser, A., Asamer, J., & Dragaschnig, M. (2014). How to Reduce Range Anxiety? The Impact of Digital Elevation Model Quality on Energy Estimates for Electric Vehicles. GI_Forum (2), Salzburg, Austria. doi: 10.1553/giscience2014s165. – pre-print with color figures

Full papers in peer-reviewed conference proceedings:

  1. Weißenfeld, A., Vanerio, J. M., Wachsenegger, A., Graser, A. & Garos, A. (2025). Proactive Fault Detection in Wind Turbine Generators using SCADA Measurements, PHM 2025.
  2. Doulkeridis, C., Theodoridis, Y., Theodoropoulos, G., Graser, A., Heistracher, C. & Sakr, M. (2023). MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data, in IEEE Big Data 2023, Sorrento, Italy, 15-18 December 2023.
  3. Shi, M., Currier, K., Liu, Z., Janowicz, K., Wiedemann, N., Verstegen, J., McKenzie, G., Graser, A., Zhu, R., & Mai, G. (2023) Thinking Geographically about AI Sustainability, AGILE GIScience Ser., 4, 42, https://doi.org/10.5194/agile-giss-4-42-2023.
  4. Pruckovskaja, V., Weissenfeld, A., Heistracher, C., Graser, A., Schall, D., & Kemnitz, J. (2023). Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications. ICPHM 2023. IEEE.
  5. Straub, M., Rudloff, C., Graser, A., Kloimüllner, C., Raidl, G., Pajones, M., & Beyer, F. (2018). Semi-Automated Location Planning for Urban Bike-Sharing Systems. 7th Transport Research Arena TRA 2018, April 16-19, 2018, Vienna, Austria.
  6. 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.
  7. 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). IEEE. doi:10.1109/MTITS.2015.7223240
  8. Graser, A., Straub, M., & Dragaschnig, M. (2015). Is OSM Good Enough for Vehicle Routing? A Study Comparing Street Networks in Vienna. In Progress in Location-Based Services 2014 (pp. 3-17). Springer International Publishing. doi:10.1007/978-3-319-11879-6_1. – pre-print
  9. Graser, A., Aleksa, M., Straub, M., Saleh, P., Wittmann, S., & Lenz, G. (2014, April). Safety of urban cycling: A study on perceived and actual dangers. In Transport Research Arena (TRA) 5th Conference.
  10. Graser, A., Straub, M., & Dragaschnig, M. (2013). Ein systematischer Vergleich der Straßennetzwerke von GIP und OpenStreetMap im Großraum Wien. Angewandte Geoinformatik 2013, 424-433. – presentation
  11. Graser, A., Ponweiser, W., Dragaschnig, M., Brandle, N., & Widhalm, P. (2012, September). Assessing traffic performance using position density of sparse FCD. In Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on (pp. 1001-1005). IEEE.
  12. Graser, A., Koller, H., Ponweiser, W., Dragaschnig, M., & Pohl, F. (2012). Effiziente Bewertung verkehrlicher Maßnahmen auf Basis von Floating-Car-Daten. Angewandte Geoinformatik 2012. 450-458.
  13. Graser, A., Dragaschnig, M., & Rudel, B. (2010). Sensorwebs für die Terminallogistik. Angewandte Geoinformatik 2010. 348-353.

Short papers or abstracts in peer-reviewed conference or workshop proceedings:

  1. Graser, A., Lorencio Abril, J. A., Weißenfeld, A. & Wachsenegger, A. (2025) ST-SplitVFL: Spatio-Temporal Split Vertical Federated Learning. 6th Spatial Data Science Symposium (SDSS 2025). doi: 10.5281/zenodo.17661670
  2. Wachsenegger, A., Graser, A., Weißenfeld, A., Dragaschnig, M. (2025) CrowdSense: Interpretable and Efficient Multivariate Crowd Forecasting with Active Learning. Workshop on Leveraging SEmaNtics for Transparency in Industrial Systems (SENTIS) in conjunction with SEMANTiCS 2025, 3-5 Sept 2025, Vienna, Austria.
  3. Graser, A. & Dragaschnig, M. (2025). Learning From Trajectory Data With MobiML. Workshop on Big Mobility Data Analysis (BMDA2025) in conjunction with EDBT/ICDT 2025, 25-27 March 2025, Barcelona, Spain.
  4. Lorencio Abril, J. A., Graser, A., Weißenfeld, A., & Wachsenegger, A. (2024). Spatio-Temporal Vertical Federated Learning to Overcome Data Sharing Limitations. Abstracts of the ICA, 7, 95. presented at EuroCarto 2024, Vienna, Austria.
  5. Graser, A. & Dragaschnig, M. (2024) Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics. 25th IEEE International Conference on Mobile Data Management (MDM2024), 24 – 27 June 2024, Brussels, Belgium. doi:10.1109/MDM61037.2024.00030
  6. Graser, A. (2024) Exploratory Analysis of Massive Movement Data. PhD Dissertation Showcase. 25th IEEE International Conference on Mobile Data Management (MDM2024), 24 – 27 June 2024, Brussels, Belgium.
  7. Graser, A., Weißenfeld, A., Heistracher, C., Dragaschnig, M., & Widhalm, P. (2024) Federated Learning for Anomaly Detection in Maritime Movement Data. 25th IEEE International Conference on Mobile Data Management (MDM2024), 24 – 27 June 2024, Brussels, Belgium. doi:10.1109/MDM61037.2024.00030
  8. Liu, Z., Janowicz, K., Currier, K., Shi, M., Rao, J., Gao, S., … & Graser, A. (2023) Here is Not There: Measuring Entailment-based Trajectory Similarity for Location-Privacy Protection and Beyond. International Symposium on Platial Information Science (Platial’23), Dortmund, Germany, 19–21 September 2023.
  9. 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. – 🎬 video
  10. Graser, A., Heistracher, C., & Pruckovskaja, V. (2022). On the Role of Spatial Data Science for Federated Learning. In: Spatial Data Science Symposium (SDSS2022). https://doi.org/10.25436/E24K5T – 🎬 video
  11. Ignjatović, D., Liakhovets, D., Simon, R., Neubauer, G., Graser, A., Schütz, M., … & Resch, B. (2022). Combining Social Media and Open Source Data With Relevance Analysis and Expert Knowledge To Improve Situational Awareness in Crisis and Disaster Management–Concept. IDIMT-2022, 153.
  12. Ochoa-Ortiz, H., Gartner, G., and Graser, A. (2022). Pedestrian routing of periodically changing areas using Volunteered Geographical Information (OpenStreetMap), Abstr. Int. Cartogr. Assoc., 5, 92, https://doi.org/10.5194/ica-abs-5-92-2022.
  13. 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
  14. 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
  15. 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.
  16. Graser, A., & Widhalm, P. (2018). Modelling Massive AIS Streams with Quad Trees and Gaussian Mixtures. In: Mansourian, A., Pilesjö, P., Harrie, L., & von Lammeren, R. (Eds.), 2018. Geospatial Technologies for All : short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science. Lund University 12-15 June 2018, Lund, Sweden. ISBN 978-3-319-78208-9. doi:10.31223/osf.io/sz34w.
  17. Leodolter, M., & Graser, A. (2017). Improving Vehicle Speed Prediction Transferability With Network Centrality, MT-ITS 2017, Napoli, Italy.
  18. Graser, A. (2017). Towards landmark-based instructions for pedestrian navigation systems using OpenStreetMap. AGILE 2017, Wageningen, Netherlands.
  19. Graser, A. (2016). Recent Advances and Challenges in the Development of Landmark-based Pedestrian Navigation Systems using OSM. In Proceedings of the 13th International Conference on Location-Based Services, LBS 2016, 14-16 November 2016, Vienna, Austria.
  20. Asamer, J., Ruthmair, M., Graser, A., & Prandtstetter, M. (2016). Optimizing Checkpoints for Arrival Time Prediction.  In ICCL 2016 Book of Abstracts, S. 86 – 88.
  21. Graser, A., Leodolter, M., & Koller, H. (2015). Towards Better Urban Travel Time Estimates Using Street Network Centrality. In Proceedings of the 1st ICA European Symposium on Cartography, EuroCarto 2015, 10-12 November 2015, Vienna, Austria. – presentation
  22. Graser, A., Straub, M., Sellitsch, D., Schwarz, S., Olaverri Monreal, C. (2015). Why Pedestrians are Still Stuck with Navigation Tools Designed for Cars, Walk21, Vienna, Austria.
  23. 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).
  24. Graser, A., Dragaschnig, M., Ponweiser, W., Koller, H., Marcinek, M. S., & Widhalm, P. (2012). FCD in the Real World–System Capabilities and Applications. In 19th ITS World Congress, Vienna, Austria, 22/26 October 2012.
  25. Graser, A., Dragaschnig, M., Koller, H., & Piff, M. (2012). Kartenunabhängige Übertragung von Routenempfehlungen mittels offener Standards. Angewandte Geoinformatik 2012. 447-449.
  26. Graser, A. (2011). Visualisierung raum-zeitlicher Daten in Geoinformationssystemen am Beispiel von Quantum GIS mit „Time Manager“-Plug-In”. Proceedings of FOSSGIS2011, Heidelberg, Germany. ISBN: 978-3-00-034124-3. 73-75.

Preprints:

  1. Granell, C., Ostermann, F. O., Nüst, D., Kedron, P., Koukouraki, E., Matey-Sanz, M., Decoupes, R., Trilles, S., Graser, A. & Niers, T. (2025) Longitudinal assessment of research in GIScience domain shows a positive impact of reproducible research practices. EarthArXiv preprint. https://doi.org/10.31223/X5RJ3W 
  2. Graser, A. (2025). Timeseries Foundation Models for Mobility: A Benchmark Comparison with Traditional and Deep Learning Models. arXiv preprint arXiv:2504.03725.
  3. Pebesma, E., Fleischmann, M., Parry, J., Nowosad, J., Graser, A., Dunnington, D., Pronk, M., Schouten, R., Lovelace, R., Appel, M., & Abad, L. (2025). Spatial Data Science Languages: commonalities and needs. arXiv preprint arXiv:2503.16686.
  4. Schlögl, M., Graser, A., Spiekermann, R., Lampert, J., & Steger, S. (2024). Brief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping. Natural Hazards and Earth System Sciences Discussions, 2024, 1–17. https://doi.org/10.5194/nhess-2024-213
  5. Graser, A., Jalali, A., Lampert, J., Weißenfeld, A., & Janowicz, K. (2024). MobilityDL: A Review of Deep Learning From Trajectory Data. arXiv preprint arXiv:2402.00732.
  6. Jalali, A., Graser, A., & Heistracher, C. (2023). Towards eXplainable AI for Mobility Data Science. arXiv preprint arXiv:2307.08461.
  7. Mokbel, M., Sakr, M., Xiong, L., Züfle, A., Almeida, J., Aref, W., …, Graser, A., … & Zimányi, E. (2023). Towards Mobility Data Science (Vision Paper). arXiv preprint arXiv:2307.05717.
  8. Pruckovskaja, V., Weissenfeld, A., Heistracher, C., Graser, A., Kafka, J., Leputsch, P., … & Kemnitz, J. (2023). Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications. arXiv preprint arXiv:2304.11101.
  9. Graser, A., Zimányi, E., & Bommakanti, K.C. (2020). From Simple Features to Moving Features and Beyond? arXiv preprint arXiv:2006.16900.

Reports:

  1. Sakr, M., Ishimaru, N., Kim, K.-S., Simmons, S., Desruisseaux, M., Fu, C., Graser, A., Heazel, C., Kung, P., Lauer, J., Liang, S., Little, C., Mokbel, M., Percival, G., Ramage, A., Smith, R., Tillman, S., Zimanyi, E. (2024) OGC Mobility Data Science Discussion Paper. http://www.opengis.net/doc/dp/mobility-data-science
  2. Jonietz, D., Sester, M., Stewart, K., Winter, S, Tomko, M., & Xin, Y. (eds). (2022). Urban Mobility Analytics (Dagstuhl Seminar 22162). Dagstuhl Reports, 12(4), 26–53. https://doi.org/10.4230/DagRep.12.4.26
  3. 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

Conference presentations (without proceedings):

  1. Graser, A. (2025). Trajectools: analyzing anything that moves. QGIS User Conference 2025, 2-3 June 2025, Norrköping, Sweden. – 🎬 video
  2. Jalali, A., Graser, A., & Heistracher, C. (2023). Towards eXplainable AI for Mobility Data Science. International Symposium on Location-Based Big Data and GeoAI 2023 (LocBigDataAI 2023) in conjunction with ICC 2023, 12 August 2023, Cape Town, South Africa.
  3. Graser, A. (2022). The State of Trajectory Visualization in Notebook Environments. GI_Salzburg 2022, Salzburg, Austria.
  4. Graser, A. (2022). MovingPandas: general purpose visual movement data analytics. GeoPython 2022.
  5. Graser, A. (2021). Exploratory Movement Data Analysis. GeoPython 2021. – 🎬 video
  6. Graser, A. (2021). Spatial data exploration in Jupyter notebooks The power of interactive visualization with GeoPandas and HoloViews. FOSDEM 2021. – 🎬 video
  7. Graser, A. (2019). Big Spatial(!) Data Processing mit GeoMesa. AGIT 2019, Salzburg, Austria. – 🎬 video
  8. Graser, A. (2019). Neuigkeiten vom QGIS-Projekt. AGIT 2019, Salzburg, Austria. – 🎬 video
  9. Graser. A. (2019). MovingPandas: efficient trajectory data handling. PyDays Vienna, Austria.
  10. Graser, A. (2019). QGIS: Das OpenSource Geoinformationssystem. Linuxwochen Wien, Vienna, Austria.
  11. Graser, A. (2018). Haltung und Analyse großer Mengen räumlicher und zeitlicher Daten mit GeoMesa. AGIT 2018, Salzburg, Austria.
  12. Graser, A. (2018). Neuigkeiten vom QGIS-Projekt. AGIT 2018, Salzburg, Austria.
  13. Graser, A. (2017). Neuigkeiten vom QGIS-Projekt. AGIT 2017, Salzburg, Austria.
  14. Graser, A. (2017). A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS. PyDays, Vienna, Austria.
  15. Graser, A. (2016). Neues vom QGIS-Projekt. AGIT 2016, Salzburg, Austria.
  16. Graser, A. (2016). Developing an Open Pedestrian Landmark Navigation Model. Free and Open Source Software for Geospatial (FOSS4G). Bonn, Germany. – preprint – 🎬 video
  17. Naumann, S., Graser, A., Straub, M. (2016). Converting OSM data into a routable graph for pedestrians. EURO2016, Poznan, Poland.
  18. Graser, A. (2015). Kartendesign mit QGIS. AGIT 2015, Salzburg, Austria.
  19. Graser, A., Straub, M., & Dragaschnig, M. (2013). An Open Source Analysis Toolbox for Street Network Comparison: How OSM Compares to the Official Austrian Reference Graph. FOSS4G2013, Nottingham, UK. – 🎬 video
  20. Graser, A., & Macho, W. (2011). Neues vom Quantum GIS-Projekt. AGIT 2011, Salzburg, Austria.

Invited talks:

A playlist of recorded talks is available on Youtube.

  1. Graser, A. (2025-10-01). Building Data Science Tools for Sustainable Transformation. PyData Paris, France. – 🎬 video
  2. Graser, A. (2025-09-18). Spatial Data Science Libraries – Interfacing With Desktop & Browser. Spatial Data Science across Languages (SDSL) 2025, 17-19 September 2025, Salzburg, Austria.
  3. Graser, A. (2025-05-15). Mobility Data Analytics for the Cloud. SDSC2025 Spatial Data Science Conference, London, UK. – 🎬 video
  4. Graser, A. (2024-11-28). Mobility Data Science in the Age of AI. Keynote at AGILE PhD School 2024, Universitat Jaume I, Castellón de la Plana, Spain.
  5. Graser, A. (2024-09-09). Behind the scenes of QGIS.ORG. Keynote at QGIS International User Conference 2024, University of Bratislava, Slovakia.
  6. Graser, A. (2024-07-03). Geospatial Data Science – Alles neu oder was? Zukunftsforum Geoinformatik. University of Salzburg, Austria.
  7. Graser, A. (2024-06-19). Developments in Mobility Data Science. Collegium Helveticum. University of Zurich, Switzerland.
  8. Graser, A. (2023-11-15). Status of MovingPandas. GISDay. HERE. virtual/global.
  9. Graser, A. (2023-10-23). Data Engineering for Mobility Data Science. Z_GIS Kolloquium. University of Salzburg, Austria.
  10. Graser, A. (2023-08-31). Data engineering for Mobility Data Science (with Python and DVC). OpenGeoHub Summer School 2023, Poznan, Poland.
  11. Graser, A., Abad, L., Hengl, T., Pebesma, E., & Pronk, M. (2023-08-30). Discussion panel: What can R, Python, and Julia development communities do to combat the climate crisis? OpenGeoHub Summer School 2023, Poznan, Poland.
  12. Graser, A. (2023-04-14). Building Sustainable Tools for Mobility Data Science. CSH-ITU Copenhagen workshop: Sustainable mobility: Data, networks, and complexity. Vienna, Austria.
  13. Graser, A. (2022-11-17). Shaping Open Spatial Data Science. Keynote at FOSS4G:UK Local 2022. virtual/UK.
  14. Graser, A. (2022-11-16). Mobility Data Science. IAEA Workshop on the improvement of capabilities for Satellite Imagery and Geospatial Information, Vienna, Austria.
  15. Graser, A. (2022-09-23). Movement data science meets Archeology. Keynote at ARCHEO.FOSS 2022 Panel: Moving in the past.
  16. Graser, A. (2022-06-14). Intro to MovingPandas. University Piraeus. Department of Computer Science.
  17. Graser, A. (2022-02-11). University of Edinburgh Earth Observation – AGI Scotland Seminar Series
  18. Graser, A. (2021-11-24). Open LBS Research: Why (not)? Keynote at LBS2021, Glasgow, UK. – 🎬 video
  19. Graser, A. (2021-11-18). Bewegungsdaten besser verstehen & nutzen. Women in Mobility Winter School.
  20. Graser, A. (2021-10-25). Understanding Tracking Data (with MovingPandas). SDSC2021 Spatial Data Science Conference. – 🎬 video
  21. Graser, A. (2021-10-01). Open source for open spatial data science. Keynote at FOSS4G 2021, Buenos Aires, Argentina. – 🎬 video
  22. Graser, A. (2021-09-16). Understanding Movement Data. SystemX Seminar Series. – 🎬 video
  23. Graser, A. (2021-07-09). Urban Data Science – More than a buzzword? Keynote at GI_Forum 2021, Salzburg, Austria. – 🎬 video
  24. Graser, A. (2021-06-15). Exploratory Spatial Data Analysis. BEGIN Webinar. Bell Edwards Geographic Data Institute. Univ. of St. Andrews, UK.
  25. Graser, A. (2021-05-25). Geospatial Data Analysis. Keynote at Canadian Cartographic Association (CCA) Conference.
  26. Graser, A. (2021-05-18). Spatial Data Science Using QGIS. UN Open GIS QGIS Workshop.
  27. Graser, A. (2021-03-18). Movement Data in GIS. Lightning Talk at Geobeer Switzerland.
  28. Graser, A. (2021-03-10). Exploratory Analysis of Massive Movement Data. RGS-IBG GIScience Research Group Seminar, UK. – 🎬 video
  29. Graser, A. (2020-10-22). Exploring Movement Data. Geographic Data Science Lab Brown Bag Seminar. Univ. of Liverpool, UK. – 🎬 video
  30. Graser, A. (2020-10-21). Bewegungsdatenanalyse. HEiKA Urban Data Science Workshop. Univ. Heidelberg & Karlsruher Institut für Technologie, Germany.
  31. Graser, A. (2020-09-22). MovingPandas – open source movement data exploration library. CIHT Webinar.
  32. Graser, A., Zimányi, E., & Bommakanti, K.C. (2020-09-15). From Simple Features to Moving Features and Beyond? OGC Member Meeting Sept 2020, Moving Features SWG.
  33. Graser, A. (2019). Python for Movement Data Analysis. Vienna Data Science Group Meetup, Vienna, Austria.
  34. Graser, A. (2019). Towards an exploratory analysis of time in massive movement data. DASTU, Politecnico di Milano, Italy.
  35. Graser, A. (2019). Exploring movement data with open geospatial tools. Politecnico di Milano, Italy. – 🎬 video
  36. Graser, A. (2019). Exploring movement data with open geospatial tools. Urban Mobility Symposium, Citylab Berlin, Germany.
  37. Graser, A. (2019). Geospatial Data Analysis – Quo Vadis? CODE Conference, University of the Bundeswehr Munich, Germany.
  38. Graser, A. (2019). Geomarketing: offen & dynamisch. WIGeoGIS Knowledge Day, Wien, Austria.
  39. Graser, A. (2019). Einblicke vom Bazaar des QGIS-Projekts. Keynote at FOSSGIS 2019, Dresden, Germany. – 🎬 video
  40. Graser, A. (2019). Managing massive amounts of spatio-temporal data using GeoMesa, Bern, Switzerland.
  41. Graser, A. (2018). QGIS: open and dynamic. Keynote at QGIS User Group Meeting Mexico. (remote)
  42. Graser, A. (2018). Advanced Visualization in QGIS3. Workshop at Netzwerktreffen des DFG-Forschungsnetzwerks “Digitale Geographien”, TU Wien, Vienna, Austria.
  43. Graser, A. (2018). QGIS: open and dynamic. Keynote at FOSS4G Korea, Seoul, South Korea.
  44. Graser, A. (2018). Managing massive amounts of spatio-temporal data using GeoMesa. Vienna Data Science Group, Knowledgefeed, Vienna, Austria. – 🎬 video
  45. Graser, A. (2018). Neuigkeiten von QGIS3. 31. Open Government Plattform Wien, Vienna, Austria.
  46. Graser, A. (2018). GIScience for Dynamic Transportation Systems. GI-Forum, University of Muenster, Germany.
  47. Graser, A. (2018). Exploring Open Source: QGIS and its analysis tools. New York GIS Association webinar.
  48. Graser, A. (2018). Mobilitätsdatenanalyse mit OpenSource-GIS. Vienna Geo Meetup, Vienna, Austria.
  49. Graser, A. (2017). GIScience for Dynamic Transportation Systems. GIScience Colloquium, University of Zurich, Switzerland.
  50. Graser, A. (2017). QGIS – open & dynamic. Keynote at FOSS4G-FI, Helsinki, Finland.
  51. Graser, A. (2017). Einblicke vom Bazaar eines Open Source Projekts: Das Beispiel QGIS, 13. Netzpolitischer Abend AT, Wien, Austria. – 🎬 video
  52. Graser, A. (2016). QGIS – Community-powered GIS. Keynote at FOSS4G-NOR, Oslo, Norway. – 🎬 video
  53. Graser, A. (2016). Offen und dynamisch – OpenSource, OpenData & OpenScience, Keynote at AGIT2016, Salzburg, Austria. 
  54. Graser, A. (2016). QGIS for Cartography. CartoTalk, TU Wien, Austria.
  55. Graser, A. (2015). Die Time-Manager-Erweiterung. Anwendertreffen der QGIS Anwendergruppe Deutschland, Kassel, Germany.
  56. Graser, A. (2015). Freie und offene GIS-Software. OVG Vortragsreihe WS2015/16, TU Wien, Austria.
  57. Graser, A., & Straub, M. (2015). Routing mit OSM. OpenStreetMap Spezialforum AGIT 2015, Salzburg, Austria.
  58. Graser, A. (2015). Die dritte und vierte Dimension in QGIS. KAGIS-Fachtag 2015, Klagenfurt, Austria.
  59. Graser, A., & Alexiou, K. (2015). Visual exploration and presentation of spatial data using QGIS – Time Manager Workshop. QGIS Conference 2015, Nodebo, Denmark.
  60. Graser, A. (2014). GIScience for Dynamic Transportation Systems. Laboratory for Geocomputation, Ryerson University, Toronto, Canada.
  61. Graser, A. (2013). GIS in der Mobilitätsforschung. GIS-Day 2013, Linz, Austria.
  62. Graser, A. (2011). Time Manager Plugin. 2nd German-speaking QGIS user conference, Rapperswil, Switzerland. 
  63. Graser, A. (2011). Routing mit QGIS + pgRouting. 2nd German-speaking QGIS user conference, Rapperswil, Switzerland. 

Workshop presentations:

  1. 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
  2. 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.
  3. 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 in conjunction with the 26th International Cartographic Conference, Dresden, Germany.
  4. Graser, A., & Rudloff, Ch. (2012). On the portability of map-independent mode detection algorithms. Urbanism on Track Workshop 2012, Delft, The Netherlands.

Poster presentations:

  1. Graser, A., Twaroch, F., & Widhalm, P. (2019). Monitoring the impacts of urban construction works on city traffic. GI_Forum 2019, Salzburg, Austria.
  2. Graser, A. (2012). Where are we going? Spatial and Mobility Aspects of Twitter Streams. Summer School on Social Media Modeling and Search .

Articles in non-refereed media:

  1. Graser, A., Doulkeridis, C. & Theodoropoulos, G. (2023). Green Mobility Data Spaces. In: ERCIM News. 135.
  2. Graser, A. (2021). Bewegungsdaten in GIS – eine Bestandsaufnahme. UNIGIS offline. Newsletter für UNIGIS Studierende und Alumni. 86. 6-7. (invited article)
  3. Graser, A., Ponweiser, W., & Dragaschnig, M. (2013). Generierung detaillierter Geschwindigkeitsprofile aus grob aufgelösten Floating-Car-Daten. Österreichische Zeitschrift für Verkehrswissenschaft – ÖZV 1-2/2013.
  4. Graser, A. (2012). Introducing the Quantum GIS Ecosystem. GIS Lounge (invited article).
  5. Graser, A. (2011). Mit offenen Daten auf Wohnungssuche. Futurezone.at – Open Data Blog. (invited article)

Theses:

  1. Graser, A. (2021). Exploratory Analysis of Massive Movement Data. PhD thesis at the University of Salzburg, Austria.
  2. Graser, A. (2010). OGC-Sensorweb-Services für Fragestellungen in Straße-Schiene-Terminals. Master’s thesis at the University of Applied Sciences, Wiener Neustadt, Austria.
  3. Graser, A. (2008). Analyse von Map-Matching- und Routing-Algorithmen mithilfe eines GIS. 2nd Bachelor’s thesis at the University of Applied Sciences, Wiener Neustadt, Austria.
  4. Graser, A. (2007). Analyse und Darstellung von Mautdaten mithilfe einer Webapplikation. 1st Bachelor’s thesis at the University of Applied Sciences, Wiener Neustadt, Austria.

Co-supervised theses:

  1. Lorencio Abril, J.A. (2024). Spatio-Temporal Split Vertical Federated Learning. MSc thesis, CentraleSupélec, Paris-Saclay University. – Best Academic Record Award
  2. Schwebler, M. (2023). Spatiotemporal Hotspot Analysis for Movement Data. (original German title: Raum-zeitliche Hotspot Analyse für Bewegungsdaten). MSc thesis, Department for Mathematics and Geoinformation, Technical University of Vienna.
  3. Ochoa Ortiz, H. (2022). Pedestrian routing of dynamic areas using Volunteered Geographical Information (OpenStreetMap). MSc thesis, Department for Geodesy and Geoinformation, Technical University of Vienna.
  4. Enenkel, G. (2020). On the volatility of construction site entailed traffic deflections – A floating car data example from Vienna. MSc thesis, Faculty of Civil Engineering, Technical University of Vienna.
  5. Waldburger, T. (2020). Large-scale analysis of taxi drivers’ route choice behaviour in San Francisco, Shanghai, and Vienna. MSc thesis, Department of Geography, University of Zurich.
  6. Westermeier, E.M. (2018). Contextual Trajectory Modeling and Analysis. MSc thesis, Interfaculty Department of Geoinformatics, University of Salzburg. – AGEO Best Thesis Award
  7. Schumacher, D. (2018). Do Taxi Drivers Take the Shortes Routes? A Large-Scale Analysis Using GPS-based Floating Car Data in Vienna. MSc thesis, Department of Geography, University of Zurich.

Videos: https://www.youtube.com/anitagraser

Program committee memberships at scientific conferences:

  • AGILE 2021
  • AGIT 2025
  • COSIT 2022
  • FOSS4G 2022
  • FOSS4G-EU 2015, 2025
  • iDSC 2023
  • LBS 2020, 2023
  • MBDW 2020
  • MDM 2025 (PhD Dissertation Showcase Co-Chair)
  • MoVIS 2020
  • SIGSPATIAL 2022
  • SDSS 2023, 2024, 2025