Blog d’Anita Graser

https://anitagraser.com

  • 25 septembre 2021Exploring Vienna’s street-level Lidar “Kappazunder” data sample
    Kappazunder is the city of Vienna’s database created during their recent mobile mapping campaign. Using vehicle-mounted Lidar and cameras, they collected street-level Lidar and street view images. Slide from the official announcement on Thursday, 23rd Sept 2021 Yesterday, they published a first sample dataset, containing one trajectory on data.gv.at. The download contains documentation, vector data (.shp), images (.jpg), and point clouds (.laz): Trajectory The shapefiles contain vehicle location updates, photo locations, and areas describing the extent of the point clouds. Since the shapefile lack .prj files, we need to manually specify the correct CRS (EPSG:31256 MGI / Austria GK East). The vehicle location updates and photo locations contain timestamps as epoch. However, the format is a little special: To display a human-readable timestamp, I therefore used the following label expression: format_date( datetime_from_epoch( « epoch_s »*1000), ‘HH:mm:ss’) Adding these labels also reveals that the whole trajectory is just 2 minutes long. This puts the download size of over 5GB into perspective. The whole dataset will be massive. Lidar The .laz files are between 100 and 200MB, each. The …
  • 15 septembre 2021Movement data in GIS #36: trucks from space
    Can we reliably measure truck traffic from space? Compared to private transport, spatiotemporal data on freight transport is even harder to come by. Detecting trucks using remote sensing has been a promising lead for many years but often required access to pretty specialized sensors, such as TerraSAR-X. That is why I was really excited to read about a new approach that detects trucks in commonly available Sentinel-2 imagery developed by Henrik Fisser (Julius-Maximilians-University Würzburg, Germany). So I reached out to him to learn more about the possibilities this new technology opens up.  Vehicles are visible and detectable in Sentinel-2 data if they are large and moving fast enough (image source: ESA) To verify his truck detection results. Henrik had already used data from truck counting stations along the German autobahn network. However, these counters are quite rare and thus cannot provide full spatial coverage. Therefore we started looking for more complete reference data. Fortunately, Nikolaus Kapser at the Austrian highway corporation ASFINAG offered his help. The Austrian autobahn toll system is gantry-based. It records when a truck passes a gantry. Using the timestamp o …
  • 28 juillet 2021Great label callout lines
    One of the new features in QGIS 3.20 is the option to trim the start and end of simple line symbols. This allows for the line rendering to trim off the first and last sections of a line at a user configured distance, as shown in the visual changelog entry.  This new feature makes it much easier to create decorative label callout (or leader) lines. If you know QGIS Map Design 2, the following map may look familiar – however – the following leader lines are even more intricate, making use of the new trimming capabilities: To demonstrate some of the possibilities, I’ve created a set of four black and four white leader line styles: You can download these symbols from the QGIS style sharing platform: https://plugins.qgis.org/styles/101/ to use them in your projects. Have fun mapping! …
  • 27 juin 2021QGIS Atlas on steroids
    Today’s post is a video recommendation. In the following video, Alexandre Neto demonstrates an exciting array of tips, tricks, and hacks to create an automated Atlas map series of the Azores islands. Highlights include: 1. A legend that includes automatically updating statistics 2. A way to support different page sizes 3. A solution for small areas overshooting the map border You’ll find the video on the QGIS Youtube channel: [youtube https://www.youtube.com/watch?v=NCsnTt6uxXo?version=3&rel=1&showsearch=0&showinfo=1&iv_load_policy=1&fs=1&hl=en&autohide=2&wmode=transparent&w=545&h=307] This video was recorded as part of the QGIS Open Day June edition. QGIS Open Days are organized monthly on the last Friday of the month. Anyone can take part and present their work for and with QGIS. For more details, see https://github.com/qgis/QGIS/wiki#qgis-open-day …
  • 4 juin 2021MovingPandas v0.7 released!
    The latest v0.7 release is now available from conda-forge. New features include: Functions to convert Trajectories to GeoDataFrames (points, line segments, or whole trajectories)Clip and intersection now return TrajectoryCollection objects As always, all tutorials are available from the movingpandas-examples repository and on MyBinder: …
  • 28 mars 2021Movement data in GIS #35: stop detection & analysis with MovingPandas
    In the last few days, there’s been a sharp rise in interest in vessel movements, and particularly, in understanding where and why vessels stop. Following the grounding of Ever Given in the Suez Canal, satellite images and vessel tracking data (AIS) visualizations are everywhere: The 224,000-ton shipping vessel, #EverGiven, seen here in this WorldView-2 #satellite image from March 26, 2021, blocking one of the world’s busiest waterways, the #SuezCanal, since Tuesday. pic.twitter.com/KDLoCqX1w8— Maxar Technologies (@Maxar) March 26, 2021https://platform.twitter.com/widgets.js Animation showing how the grounded container ship brought the Suez Canal to a standstill. Huge thanks to @VesselsValue for supplying the data.Read @OilSheppard @harrydemps & @hebamks story https://t.co/9s1oQXwOlH#gistribe #dataviz pic.twitter.com/JRkwmhG0KJ— Steven Bernard (@sdbernard) March 24, 2021https://platform.twitter.com/widgets.js Using movement data analytics tools, such as MovingPandas, we can dig deeper and explore patterns in the data. The MovingPandas.TrajectoryStopDetector is particularly useful in this situation. We can provide it with a Trajectory or TrajectoryCollection and let it detect all sto …
  • 17 mars 2021Movement data in GIS #34: a protocol for exploring movement data
    After writing “Towards a template for exploring movement data” last year, I spent a lot of time thinking about how to develop a solid approach for movement data exploration that would help analysts and scientists to better understand their datasets. Finally, my search led me to the excellent paper “A protocol for data exploration to avoid common statistical problems” by Zuur et al. (2010). What they had done for the analysis of common ecological datasets was very close to what I was trying to achieve for movement data. I followed Zuur et al.’s approach of a exploratory data analysis (EDA) protocol and combined it with a typology of movement data quality problems building on Andrienko et al. (2016). Finally, I brought it all together in a Jupyter notebook implementation which you can now find on Github. There are two options for running the notebook: The repo contains a Dockerfile you can use to spin up a container including all necessary datasets and a fitting Python environment. Alternatively, you can download the datasets manually and set up the Python environment using the provided environment.yml file. The dataset contains over 10 million location records. Most visualizations a …
  • 11 mars 2021Movement data in GIS #33: “Exploratory analysis of massive movement data” webinar

    Yesterday, I had the pleasure to speak at the RGS-IBG GIScience Research Group seminar. The talk presents methods for the exploration of movement patterns in massive quasi-continuous GPS tracking datasets containing billions of records using distributed computing approaches.

    Here’s the full recording of my talk and follow-up discussion:

    [youtube https://www.youtube.com/watch?v=dRE9Zl7jpUA?version=3&rel=1&showsearch=0&showinfo=1&iv_load_policy=1&fs=1&hl=en&autohide=2&wmode=transparent&w=545&h=307]

    and slides are available as well.


    This post is part of a series. Read more about movement data in GIS.

  • 25 février 2021Video recommendations from FOSDEM 2021
    The Geospatial Dev Room at FOSDEM 2021 was a great event that (virtually) brought together a very diverse group of geo people. All talk recordings are now available publicly at: fosdem.org/2021/schedule/track/geospatial In line with the main themes of this blog, I’d particularly like to highlight the following three talks: MoveTK: the movement toolkit A library for understanding movement by Aniket Mitra Telegram Bot For Navigation: A perfect map app for a neighbourhood doesn’t need a map by Ilya Zverev Spatial data exploration in Jupyter notebooks by yours truly …
  • 12 février 2021Movement data in GIS #32: “Exploring movement data” webinar

    Last October, I had the pleasure to speak at the Uni Liverpool’s Geographic Data Science Lab Brown Bag Seminar. The talk starts with examples from different movement datasets that illustrate why we need data exploration to better understand our datasets. Then we dive into different options for exploring movement data before ending on ongoing challenges for future development of the field.

    Here’s the full recording of my talk and follow-up discussion:

    [youtube https://www.youtube.com/watch?v=mIktJKbj8Ms?version=3&rel=1&showsearch=0&showinfo=1&iv_load_policy=1&fs=1&hl=en&autohide=2&wmode=transparent&listType=playlist&list=PLJUR-WK2_HcU_ycS_uXFTmnOFlyNvRngL&w=545&h=307]


    This post is part of a series. Read more about movement data in GIS.