Thanks for all the comments on the mashup posting. We were excited about the positive feedback and suggestions. The Digg.com posting was awesome and many thanks to Kevin Rose for posting it. The Digg traffic gave us a surprise stress test for the API. Someone clever grabbed the Mac.com homepage url Mookie was testing the mashup with and posted it on Digg. The resulting traffic swamped us pretty quickly, but gave us a chance to tweak the concurrent users parameters and it held up quite well to the onslaught once we sorted everything out. The exception being anyone using IE, who got a non transparent black heatmap. Still sorting out the alpha channel transparency issues on that one, but we’ll have it all sorted for the real mash up launch. Since the URL is by no means secret anymore feel free to check it out:

http://homepage.mac.com/prak/gmap.html

A few tips 1) best to use Firefox or Safari 2) we do not have panning hooked up yet so the heat map only refactors when you zoom in 3) you can pan over to any location in the US but will not get a heat map till you zoom in 4) to get the best performance wait for the heat map to pop up before you zoom again otherwise things get a bit backed up in the queue.

We’ll have panning and all the other bugs worked out for the real mashup release, but something to play with since it is not longer really secret. Please post any feedback or suggestions. We are finalizing the API for public consumption this week and should have documentation done next week and have it out for people to use shortly after.

As for the real mashup that will go with the API, we thought we’d have a little fun with it and show some of the more advanced features. So the guts of the mashup are going to be a comparison of San Francisco and New York City. We’ll have detailed census block demographic data and a yet to be determined point of interest data set – current contenders are coffee shops, bars, bookstores, or some variety of restaurants. Ideally we’ll do this with a Yahoo Local API feed so you can look at any point of interest, but there are some issues with that we are working on. The unsolvable one being you can’t pull more 20 locations at once, but that is just a general complaint about the API.

The point of all this – showing how you can make decisions with multiple data sets – like finding the highest concentration of coffee shops in high income neighborhoods with lots of single men/women between the ages of 30 and 40. The idea actually came from a trip to a local bar here in Georgetown for an “off-site” and thinking about how we could find the best bar or neighborhood to go out for the night. Back at the office it quickly grew to where could I find the right house, the right school, the right job, the right meeting location etc. etc. It also raised a lot of questions, like does location “A” have a higher concentration of bars and single women or does location “B”. Our board never liked the bars and single women analogy, but we always like the simplicity of it. So the idea with the mashup is to provide super detailed demographic data with a lot of destination locations so you can compare which location fits your needs to best. While the mashup will be NY vs SF you can also do neighborhood comparisons, NY (Soho) vs NY (Greenwich Village) or SF (SoMa) vs SF (The Haight) etc. etc. Hope it is something that will resonate with people and get folks thinking about how they could use the API to look at other interesting things.

Popularity: 12% [?]

So it has been a while since we posted, but the rationale was we’d wait till we had a working example of moving past push pins. This week we got our GeoIQ API working with the Google Maps API and have the first set of screen shots to show. One of the things we thought is really missing from web mapping applications, right now, is the ability to do geographic analysis. Even the ability to make basic decisions like – is location “A” better than location “B” is missing. With this first simple idea in mind we’ve built a quick mashup with Google Maps. We took our heat mapping API and integrated it with a split screen Google Maps viewer. That way you can look at two locations at the same time and compare them.

We wanted a fun data set to play around with and thought traffic congestion/delay would be interesting. The Bureau of Transportation Statistics (BTS) has a cool data set with average traffic delay for all the US highways available, so we threw that in. One of the problems with pushpins or polylines in Google Maps (and others) is there is no way to visualize what are the high value or low value pushpins. In this case, which road has high traffic delay and which roads have low traffic delay. We do this with a heat map (similar to Zillow, Google Adsense, etc.) that can be dynamically refactored as you zoom in/out (see previous post). We added to this heat map tool a concentration index – which gives you a score of the value (weight) of your pushpins and how closely they are located together. Once you have the score you can see if location “A” is better than location “B”. In this case is traffic delay more concentrated in location “A” or location “B”

A comparison of the concentration of traffic delay in San Francisco and Los Angeles

The GeoIQ API creates a heat map based on an index that measures the amount of traffic delay on the roads and how closely that road delay is located to other delayed roads. The higher the delay and the closer together the roads, the hotter the map and the higher the score. The score ranges between 0 and 1. If all the traffic delay and highways were concentrated at one single location the score would 1 and if there was no traffic delay the score would be 0. In the map above traffic delay for Los Angeles in .26 and for San Francisco it is .15, so if you believe the BTS data traffic, LA is about twice as bad as SF. Lets go east coast – NYC vs. DC.

A comparison of the concentration of traffic delay in New York and Washington DC

According to this score NYC is a little worse than DC. The cool thing about the technology is you can run these comparisons on the fly as you zoom in and out of the map. So – let’s compare two big traffic bottlenecks in DC to see which is worse the I-270 Spur or I-95 Mixing Bowl.

A comparison of the concentration of traffic delay at the I-270 Spur and the I-95 mixing bowl – both in Washington DC

The Spur looks to get the better of the Mixing Bowl. In the app you can do this with any data set or mash up multiple data sets to solve a variety of problems surround location decisions. We’ll have more to come so stay tuned if this looks interesting.

Popularity: 100% [?]