Markus Neteler, PhD, is a cofounder of mundialis after having spent 15 years as a researcher in Italy. His focus is on Earth Observation, GIS and cloud computing. Markus managed for two decades the GRASS GIS project, and he is a founding member of OSGeo and other organizations.
The new GRASS GIS version 8.2 is a special edition including all new features developed during Google Summer of Code 2021. One of the enhancements is the parallelization of several raster modules by means of OpenMP, an implementation of multithreading to speed up massive data processing. Another exciting new feature is much improved, the Jupyter notebook support. Here, a new python package (grass.jupyter) is available which allows to interactively visualise maps and time series given the integration with folium.
The graphical user interface in version 8.0 introduced faster and more streamlined startup without a need for a welcome screen. For even more convenience, version 8.2 adds an experimental single window layout with familiar look-and-feel.
Related to raster data, a new metadata class called semantic labels can now be added to raster maps. Examples of semantic labels are aerial or satellite spectral bands, dataset names in remote sensing products (ndvi, evi, lst, etc), or any custom names.
At community level, we have developed a student grant program and, thanks to the move to GitHub, we have welcomed numerous new contributors.
„Hello again, my name is actinia. Still new to OSGeo and a Community Project since 2019, you might have heard about me already. In short I am a REST API on top of GRASS GIS to allow location, mapset and geodata management and visualization as well as execution of the many GRASS GIS modules and addons. Processing with other tools like GDAL and snappy is supported as well. I can be installed in a cloud environment, helping to prepare, analyse and provide a large amount of geoinformation. Besides these facts about me there is also a lot to tell about what happened last year! Besides vector upload, citable DOI, QGIS and python client implementations and more, I can be a Spatio Temporal Asset Catalog myself with the actinia-stac-plugin, am able to use data registered in a STAC for processing and after processing register the resulting data. With the ongoing development of the openeo-grassgis-driver, you can use this new functionality either in my native language or via openEO API. To learn about the details, come on over!“
All of us involved in the creation and publication of large amounts of geodata are familiar with the complexities of data management. In the case of geodata created with GRASS GIS, we asked ourselves how they could be made accessible to GeoServer without duplication. To overcome the previous limitation of GRASS GIS having its own data format, we connected the tribes and let Java and C/Python communicate with each other. So the challenge was to be able to efficiently read the GRASS GIS database directly with GeoServer. And why is that? Because this directly links the analytical capabilities of GRASS GIS with the exceptional geo service & publishing capabilities of GeoServer.
Our approach is to use the existing GDAL-GRASS bridge, and add this bridge as a new extension to GeoServer. To this we add two new GRASS GIS addons (r.geoserver.style + r.geoserver.publish) to easily publish the data from a GRASS GIS session as an OGC service. The new GeoServer GRASS raster datastore allows to use GRASS raster data directly in a GeoServer instance. In this way it is now very easy to publish GRASS data as a web service via GeoServer without having to export the data from GRASS GIS to GeoTIFF or COG files. This works for both classic raster data and also for timeseries which can e.g. be inspected as a WMS Time.
Globally, the population living in urban areas is increasing with a strong impact on land use patterns, particularly on the availability and use of green spaces. The impact of green spaces is beneficial to health, for example, by reducing mortality or improving mental health. These effects are also related to different ecosystem services provided by green spaces, such as regulating temperature, modifying air pollution and noise levels, and offering more opportunities for physical activity.
GreenUr is a plugin for QGIS that aims at putting together knowledge and information on the impacts of green space on health. It is developed as a prototype representing a work in progress coordinated by the World Health Organization (WHO) to provide an educational tool to introduce the relation between green spaces, health, and well-being and raise awareness of the importance of green spaces in cities globally. The tool can also be used as ‘quickscan’ for urban spatial planners that would like to orientate on possible effects of current and new green space design. The plugin has been tested with different experts and locations, and it will be downloadable via the QGIS Plugin manager from the project website.
The GreenUr tool allows the users to estimate the impacts of green spaces on health in a given population. The main questions addressed by the current version of the GreenUr prototype are the following:
- How much green space is available for the population of a specific city?
- Which are the pathways through which green spaces relate to health?
- Where within a city are health-related benefits of green spaces the largest?
- Which are hypothetically different land-use scenarios for green spaces?
- What would be the magnitude of the change in health impacts if future green space would be changed in cities?
All calculations performed by GreenUr are based on methodologies established by social, environmental, and epidemiological studies identified by WHO. The computational backend used is GRASS GIS and other processing methods available in QGIS. The plugin is running any common operating system and offers a demo database.