Anita Graser ๐ช๐บ๐บ๐ฆ๐ฌ๐ชExcellent video tutorial on creating animated traces in #QGIS over on #reddit:
https://www.reddit.com/r/QGIS/comments/1rde0fm/comment/o7c29l7/
This is just a sneak peak of the results:
The #MobilityDB team just announced v1.3.0-alpha featuring
New temporal types:
๐ tgeometry & tgeography that can represent the temporal evolution of any geometry type (polygon, multipoint, etc.)
๐ temporal circular buffer (tcbuffer)
๐ temporal pose (tpose) type, storing the evolution of a pose
(point position + orientation)
and more ...
https://github.com/MobilityDB/MobilityDB/releases/tag/v1.3.0-alpha
MovingPandasAnd most recently in #Geopraphy:
Van Deursen, J., Creany, N., Smith, B., Freimund, W., Avgar, T., & Monz, C. A. (2024). Recreation specialization: Resource selection functions as a predictive tool for #ProtectedArea #RecreationManagement. Applied Geography, 167, 103276. - "#GeoPandas and #MovingPandas packages in #Python were used to analyze the GPS data collected and calculate the thirteen spatio-temporal metrics"
https://www.sciencedirect.com/science/article/abs/pii/S014362282400081X
So glad our #Dagstuhl seminar paper on #MobilityDataScience is finally published and open access ๐
Mobility Data Science: Perspectives and Challenges
https://doi.org/10.1145/3652158
Kudos to the leading author team for distilling all the inputs
Mobility Data Science: Perspectives and Challenges | ACM Transactions on Spatial Algorithms and Systems
Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, ...
Woa, just in time ๐
@movingpandas v0.18.0 just landed on pypi and conda-forge
For an overview of all new features and improvements, see
https://github.com/movingpandas/movingpandas/releases/tag/v0.18
Example notebooks will be updated later. Now it's time for #esc2024
#MobilityDataScience #MovementDataAnalytics #Mobility #GeoPandas #DataScience #GIScience #GISChat #OSGeo #MovementData #VisualAnalytics
Mobile Data Management 2024We're also proud to present to you the list of accepted papers: https://mdm2024.github.io/program.html
3 more days to submit your demo paper ๐
Topics of Interest include, but not limited to:
- #MachineLearning / #AI for #MobileData
- #HumanMobility Modelling
- Synthetic #MobilityData generation
- Novel #DataScience Applications on #MovementData
- Data Management in #MobileCloud and #EdgeComputing
...
TIL that #MoveApps https://www.moveapps.org have adopted @movingpandas TrajectoryCollections as their #Python default IO ๐ https://docs.moveapps.org/#/create_py_app
It's kind of similar to #QGIS Model Designer but in the browser and specializing in #MovementEcology
#MoveBank #MovementDataScience #MovementData #Ecology #AnimalBehavior
Joseph KempfThank you @underdarkGIS
Geospatial: where MovingPandas meets Leafmap https://anitagraser.com/2022/04/10/geospatial-where-movingpandas-meets-leafmap/ #MovementData, #Python, #Spatio-temporalData
If you liked our last year's short paper on #DeepLearning from #TrajectoryData, you'll love our new #preprint even more:
๐ "MobilityDL: A Review of Deep Learning From Trajectory Data"
https://arxiv.org/abs/2402.00732
#MobilityDataScience #SpatialDataScience #MovementData #Mobility #MachineLearning #arxiv #GISChat #OpenScience
MobilityDL: A Review of Deep Learning From Trajectory Data
Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and computing power have increased, so has the popularity of deep learning from trajectory data. This review paper provides the first comprehensive overview of deep learning approaches for trajectory data. We have identified eight specific mobility use cases which we analyze with regards to the deep learning models and the training data used. Besides a comprehensive quantitative review of the literature since 2018, the main contribution of our work is the data-centric analysis of recent work in this field, placing it along the mobility data continuum which ranges from detailed dense trajectories of individual movers (quasi-continuous tracking data), to sparse trajectories (such as check-in data), and aggregated trajectories (crowd information).