When I first started studying geography I was geekily enthralled with all the different ways you could slice and dice data based on location. As we’ve worked on adding analytical capabilities to GeoCommons and GeoIQ it has been fun re-imagining how they would apply to the new world of real time and mobile/social data. Since it is the Christmas season we thought it would be apropos to introduce a new analytics tool for each of the 12 days of Christmas.
So, the question is what analytic is going to be the Partridge in the Pear Tree? It was a tough call but we had to go with aggregation. What is so special about aggregation? It gives you the ability to see distinct patterns in massive data sets. What happens when you put tens of thousands of points on a map – in the words of Schuyler “red dot fever”.
Above we have all the tweets from mobile devices on black friday as red dots. The fever is rampant to the point of not being able to see the map. What we can discern is quite limited from this perspective, but if we were to count the number of tweets in each state that would tell us something meaningful.
This is what the “aggregation” tool in GeoCommons does. It allows you to count and run statistics on the points that intersect a set of polygons – like USA states. In computational geometry this is often referred to as the “point in polygon” problem. When we link up the ability to create thematic maps from the tabulated statistics we can quickly create pattern maps. Which state, county, or zip code had the most tweets on Black Friday.
Black Friday Tweets Aggregated to USA States
Black Friday Tweets Aggregated by USA Counties
Black Friday Tweets Aggregated by USA Zip Codes
So, how easy and fast is it to go from 15,000 tweets to a thematic map? Answer – a couple of clicks and about two minutes to go through the work flow:
The best part is my entire analysis work flow has been captured and metadata about it automatically generated:
No more finding files and trying to guess what the heck was done to create an analysis and what data sources they used. It is all automatically captured for you. Your entire data lineage generated with no extra work and you can control who can access it and tweak your analyses. The future of making analysis collaborative and social.
Welcome to the Esri DC Development Center blog. We write about features of our work on big data analytics, open platforms, and open data, what is new and exciting in the Esri and community, and general industry thought leadership and discussions of geospatial data visualization and analysis.
Please explore what we're working on and let us know if you have any questions or ideas!
- Tile Layer
- Aggregation of crime95 into zones
- Result of percent change in population against Dataset from 'Bus; underground and railway stops in London' with a 100m buffer
- Dataset from 'Bus; underground and railway stops in London' with a 100m buffer
- Merge of 'Military Installations, Ranges, and Training Areas Points' into 'Garcia Date Place of Enlistment'
- Aggregation of UFO Sightings from National UFO Reporting Center into USA Counties