The proliferation of mobile, social and sensor data has given us a new playground for analysis and visualization.    It is an area we’ve been particularly excited about from our earliest experiments with streaming data.  Since joining ESRI we’ve had the opportunity to focus on these emerging data sources, and integrate them into the larger concept of Real Time GIS.  Baking our ideas into this broader framework gave us the opportunity to fully move past Tweets on a map.

We believe stream analysis has the ability to be a paradigm shift in GIS and also broaden its applicability to new audiences.  The shift to streaming means that we don’t have to wait to collect our data before we do analysis.  This opens the door to generating analysis that detects patterns in data as events are occurring.  We can be proactive in our response vs. reactive after events occur.

A great example of this concept is disaster response.  Social media provides a direct communication channel to a subset of citizens.  We can tap into conversation about how citizens are reacting to disasters and the issues emerging in their streams of dialog.  The majority of social media usage in disasters is currently qualitative.  We visualize, map and ultimately read social media messages.  Exceptional project like TweakTheTweet and Ushahidi help us categorize and organize social data during crises.  These are immensely valuable, but we ultimately run into scaling issues with qualitative approaches.  This is where we believe the addition of stream analysis opens up the door to more quantitative approaches with social data.   

Not only can we analyze data as it emits, we can also look for changes in the stream that are indicative of significant events.  In which case we can alert users there is a change of interest to them.  Like streaming data, analysis can be perpetual as well, constantly keeping us updated and alerting us about significant activity.  This concept is best seen in practice.  Below is a video that walks us through tweets on a map, to simple streaming analysis and culminating in alert triggers based on event detection in the stream.

Social Media Streaming Analytics from GeoIQ on Vimeo.

We are excited to be moving these capabilities into the Esri platform and stay tuned for more on that front soon!

 

2 Responses to Helping to Pioneer Real Time GIS with Social Streams

  1. Beautiful interface and visualization. Do you envision this analytical and visualization infrastructure to work well with very large, complex time series data (e.g. simulation model outputs or distributed sensor data – not streaming) as well?

  2. Andrew Bailey says:

    I worry that the lone tweet of high importance gets lost in a swamp of noise. I’m yet to be convinced that quantitative or automatic analysis wins.

    /runs off to code up a tweetbot to save my ass when i’m drowing.