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AGIT 2013: QGIS Cloud - Karten einfach ins Netz

Noch nie war es so einfach individuelle Web Map Services mit ansprechenden Karten, Geodatenbank und Web Client zu erstellen, wie mit QGIS Cloud. Der am 5. Juli an der AGIT 2013 in Salzburg präsentierte Vortrag kann hier herunter geladen werden.

Präsentation

AGIT 2013: QGIS Cloud - Karten einfach ins Netz

Noch nie war es so einfach individuelle Web Map Services mit ansprechenden Karten, Geodatenbank und Web Client zu erstellen, wie mit QGIS Cloud. Der am 5. Juli an der AGIT 2013 in Salzburg präsentierte Vortrag kann hier herunter geladen werden.

Präsentation

Mapping Hubway Station Stats

Today, I’ve been working on some station statistics. From the trip data, I calculated incoming and outgoing trips per station as well as the station’s first day of operations. Combining this information makes it possible to calculate the average day’s “bike balance”. A balanced station has the same number of incoming and outgoing trips while an unbalanced station will either run out of bikes or empty slots for returns.

I’ve published the resulting station map on QGIS Cloud (http://qgiscloud.com/anitagraser/hubway_cloud1) where you can have a look at the bike balance values.

Additionally, I’ve created a mashup in Leaflet pulling together background tiles from Stamen and the cloud-hosted WMS for better orientation:


Physical Maps on QGIS Cloud

QGIS Cloud is a great cloud hosting service for QGIS Server by Sourcepole. After online registration and installation of an (experimental) plugin, QGIS projects can be uploaded to the cloud quite comfortably.

For a quick test, I tried to recreate the map from “Open Data for Physical Maps”. Right now, one of the limitations is that raster layers cannot be uploaded. Instead of the SRTM data, I therefore chose OCM landscape from OpenLayers plugin to provide some hillshade. The process of uploading data and publishing the project went smoothly and I didn’t encounter any problems.

You can explore the map online at qgiscloud.com/anitagraser/corine_austria.

Considering the complexity of the Corine dataset, rendering is quite fast – certainly much better than on my notebook.


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