Own exercises in actinia

Meanwhile you have seen a lot of material. Time to try out some further exercises...

Find the relevant vector layers
Check the zipcode vector layer for the relevant column to get the zipcode
Create a process chain as a .json file to ask for the number of hospitals in the zipcodes: Use the GRASS GIS modules g.copy (because you are not allowed to change data from an other mapset), v.vect.stats and v.db.select
Post the created process chain to https://actinia.mundialis.de/api/v3/locations/nc_spm_08/processing_async for ephemeral processing

Create a process chain as a .json file
- Remember to set the computational region
- Compute the NDWI (Normalized difference water index); use r.mapcalc or i.vi
- Filter water bodies by a threshold of e.g. 0.35 using r.mapcalc
Either export the water bodies (use the exporter with the ephemeral processing) or render the maps of NDWI and water bodies with a nice color (use r.colors and persistent processing in your own mapset)

needed geodata:
- Worldwide SRTM 30m (already available in actinia as srtmgl1_v003_30m - find out the location yourself)
- South America Population 2015 (already available in actinia as worldpop_2015_1km_aggregated_UNadj- find out the location yourself)
- raster shorelines (already available in actinia as ne_1000m_coastlines- find out the location yourself)
fetch metadata with actinia interface and render input data
proposed workflow:
- set computational region to a small subregion (hint: align the region resolution to the population raster) and check the pixel number against user constraints
- buffer the coastlines by 5000 m and set a mask to the result
- Extract only the peopulation below 10 m
- Calculate the statistic to get the population at risk near coastal areas
Hints for example GRASS modules to use in process chain: g.region, r.buffer, r.mapcalc, r.mask, r.univar