• davidneudecker

#VizRisk Challenge

How data visualization can help in protecting the life and livelihood of people?

I have found the #VizRisk Challenge very useful way to share my experience on how to approach this extremely important topic of the data visualization.

The very first step in this process is to find people, which we want to protect. The threat of natural disasters is a global challenge and as a matter of fact, we cannot help everywhere. Even if the site is suggested, like in this #VizRisk Nepal case study, we still don't know, where the help will be efficient and where not.



To fulfill this mission I first used an online version of the tool ur-scape, developed by Urban-Rural Systems Team in Future Cities Laboratory. This version available online offers the collection of many great open datasets available for the whole world in very high resolution. The tool allows in one click to intersect the various data topics and help us to identify for example where a lot of people live under the high threat of landslides.



As I learned from material collected by the Labs team at GFDRR for this challenge (e.g reliefweb.int), the most common threat for local residents is the landslide collapsing over the road network. There are many technical solutions, which might be used to protect the road from a landslide. But where we should build them? Should we build them where the risk of the landslide is the highest or where most people might travel to their jobs, or children travel to schools?

To answer these questions I imported 2 datasets for our site to the standalone version of the ur-scape which is available for free on urs.fcl.sg. One dataset is again from the material collected by The Labs team at GFDRR which show us the spatial distribution of the landslide risk. The other dataset was network downloaded from Open Street Maps. The network itself tell us where the road physically is located, but unfortunately, we don't know how much is used by locals. To get a better understanding I used Space Syntax analysis, which is available as an extension for QGIS software. I used the Choice Analysis to see which parts might be most likely used by local residents to travel. For more information please see spacesyntax.net. QGIS I can use as well to import my data to my ur-scape visualization, by using python code, which is available on GitHub

For more information how to import your data to ur-scape you can visit Online Teachning Platform of the Bauhaus - Weimar University.




Having data ready inside of our ur-scape visualization, we can use unique "on the fly" filtering technique called Contours. This tool highlights areas, where our datasets intersect and we can anytime change the minimum or maximum filtering parameters for any of them. As the potential decision maker, we can see immediately the visual and quantitative difference between our choices. Choices of areas with a smaller or higher risk of landslide, or the difference of frequently or less frequently used network. Thanks to this tool we don't need to know our entry parameters for analysis in advance, but rather find them in the process of decision making.

Result of our effort is the area which can be further used for for example calculation of the funding, which would local government representatives use to build the road's landslide protection and protect lives and livelihoods of people.


Please try this ur-scape visualization and share with me your feedback.


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