Our Trip to Redlands GIS Week

Last week Andrew and I went to Redlands GIS Week.  Hosted by Esri it was a conference of students, academics and professionals.  Each year there is a different topic and the one covered this week was Volunteered Geographic Information (VGI): Real-Time and Emergency Applications.  Essentially this is how can crowd-sourced information be utilized, created, enabled for crisis response, especially with a focus on real-time data.

A combination of talks and break-out sessions the event was interesting.  The mix of students, academics and professionals meant there were different views on the suitability of crowd-sourced information.  Discussion within my break-out group ranged from how can we verify the crowd to trust the information to how can we incentivize people to provide more structured information.  There was also the typical concern of how can responders know if they can trust information.  This had already been brought up in Michael Goodchild‘s talk “It’s About Time: The Temporal Dimension in VGI,” the idea being having some unverified data is better than having no data.  I think wider acceptance of crowd-sourced information is just a matter of better analysis tools to determine what data is better and encouraging the crowd to submit data so there is more information available.

I gave a talk about the new collaborative analytics tools we’ve been adding to GeoCommons.  Specifically my talk was “Enabling Collaborative Analytics for Faster Answers in a Crisis,” the idea is that the next step in a crisis is enabling the crowd to perform analysis.  Traditionally analysts create reports which then go to decision makers.  If changes need to be made to the end report tasks go back to the analyst.

The next step in analysis is to enable everyone to perform analysis. There are key things that need to happen in a system for this to be effective though. The first is to make analytics easy, this allows the user to make good decisions when they perform their analysis. Within GeoCommons we have aimed to do this and you see results of it through the application, such as when making a thematic map. When deciding on classifications of data we allow the user to match the type of data by matching their histogram versus the available classification schemes.

By making decisions as easy as matching pictures but allowing the user to go into the details it means that both experts and beginners can perform useful analysis. The next steps in encouraging collaborative analytics is making analysis traceable and making results extend-able. That changes the flow of analysis from an analysis making a report to allow analysis to be branched from and putting analysis tools in the hands of decision makers. This eliminates the bottle neck of only have a small group of individuals that can perform analysis and allows for faster response.

See my full presentation below: