Figure 1 - 17/01/2013 08.00 AM: The Sunter Polder is taking in water from the Kali Sentiong (by operator)
Until recently, the downside has always been that getting these observations
Figure 1 - 17/01/2013 08.00 AM: The Sunter Polder is taking in water from the Kali Sentiong (by operator)
Until recently, the downside has always been that getting these observations has been a slow, laborious and biased process. With the arrival of the social media, getting local content has all of a sudden become easier. And that's where Floodtags come in.
What kind of insights can Floodtags provide to disaster response agencies and development organisations that they would not be able to get through more “traditional” means? Can you give us an example?
Traditional means such as monitoring and modelling are tremendously important. They are the basis of any flood information system. However these means have also their limitations, as they don’t provide a direct window to what is happening on-the-ground. With Twitter, people are actually providing that window and their observations are coming in 10 tweets per second!
Figure 2 - People are often very specific in their tweets, like where the floods are and how deep it is (on 29/01/2014)
By all means, Twtitter has proved to be a fantastic flood monitor tool and we encourage people to share even more of their flood experiences on Twitter. Now the difficult part is, to create the right flood filters and enrichments, so that disaster managers only need to look at a fraction of the hundreds of thousands of observations coming in.
So we enrich and analyse all flood data in real-time, and present them in an understandable format through our web service. A good example is the water depth of a flood. It turns out that a large number of people both mention the flood depth as well as the location where they monitored it. Take for instance January 29th, 2014: out of the 360.000 tweets we collected on floods, 15.000 included water depth observations (see picture). Together with the Dutch water management institute Deltares
(@arnejanvl) we are working to develop a sound interpretation framework for these observations to create real-time floodmaps. For reference, to make a reliable floodmap of the January 2013 flood took a total of nine days. This was thanks to the hard work of the disaster management office and the HOT team (Humanitarian OpenStreetMap Team).
Figure 3 - The approximate locations of 15.000 unique water level observations on 29/01/2014 (amounts of tweets in the circles)
Another example of getting the best from the data is our work together with SEPAM (a.o. @mwieriks), where we combine social network analysis and data analytics to identify “Flood Twitter Champions”. With this, flood and disaster managers can effectively engage with the public and target interested individuals. Better community connection for better community resilience.
You put quite a lot of thought into your business model. Perhaps it can be described as “freemium” - free basic tools for government but pay-on-demand for added value data analytics. Would you agree? If so, how do you think this guarantees sustainability?
Our starting point was that we weren't going deep into developing technical solution until we had a sound business model to sustain it. I've seen too many great studies ending up on the shelf, pilots that run out of money and just evaporated! Our firm belief at Floodtags is: whatever problem we are going to technically solve, it should stick and never let go!
Our model is that we give away real-time data for free, so that affected people and supporting organisations always have access to life-saving information on floods. This includes flood maps with water depths, flood alerts and overviews of flood impact and humanitarian response. Everything you need for an efficient handling of water and disaster management operations. And we don't limit ourselves to Indonesia, but we offer this as a worldwide web service, now in 21 languages. Where we earn our money is from advance analytics of the data for our costumers. This can be deeper data mining, enrichments with other data sources, research to historical flood events and so on. And when we work on a project with a customer, we ask whether the scripts developed for th