As usual, I’ve got a hard act to follow with Sean’s latest post about our wacky adventures in New York, but I might have an ace up my sleeve this time.
With all the GeoCommons discussion and interest of late, and with the first public release just around the corner, I thought I’d take the opportunity to share a little sneak peak with you.
I was wracking my brain yesterday trying to come up with some sort of real world, but not too serious and quick demonstration of GeoCommons.Â Luckily for me, I didn’t have to look past my trusty RSS reader before I found my inspiration: the â€œLive: Schiphol flight paths + noise sensor webâ€ post I saw yesterday on Ogle Earth.Â They’ve written up the details better than I’ll be able to, but the long and short of it is that the Dutch company Geluidsnet has launched a new service that mashes up noise sensor data with radar data representing the current positions of planes in the area.Â And luckily enough for me, they were kind enough to provide a KML version of this data.
In the interests of cutting straight to the chase, here’s a screenshot of a GeoCommons workspace with the noise data represented as a heatmap inside of Google Maps:
Read past the jump for the full details of what I did and how.
Disclaimer: The screenshots I’m showing here are of an in-progress development version of GeoCommons that does not yet have all of the fancy UI widgets/images/etc. hooked up, so rest assured, if you see something that looks ugly, it will look nicer when it’s public.
The first step in getting the Geluidsnet data imported into my GeoCommons profile was simply to obtain a copy of it that I could import.Â The direct KML link actually contains a NetworkLink or two that you need to traverse before you find the source of the noise data.Â GeoCommons will not traverse these itself for various reasons, security being one notable concern, so I had to do a little bit of legwork, but ended up with the kmz file that I wanted.
From there, it gets a little easier.Â First, I’ll need to upload it to GeoCommons.Â GeoCommons has KML/KMZ import support built right in, and it additionally imports the z or elevation coordinate of any imported KML geometries, if present, into a classifiable attribute.Â Since this is how the noise levels were classified in the original KMZ file, this works out pretty well for our purposes.
Since I’m such a nice guy, I went ahead and entered some useful tags, description, and source/attribution data.
Since I want other people to be able to see the fruits of my labor, I’m going to publish this dataset using the handy link there so that others can find and use it.
And since this would be a far more boring use case if I only included one person in the scenario, let’s have another user find this data and include it in their workspace.
If the tags listed in that tag cloud look a little scary to you, don’t be too put off, we’re currently in the process of pre-populating GeoCommons with a lot of the data that we’ve collected, and a lot of the stuff that has gone in recently has to do with crime and risk statistics.
Note that this also showcases one of the other key features of GeoCommons, and specifically the in-development version — the Fat Baby-based rating system.Â Once the dataset showed up in the search results, I clicky-fatbaby rated it rather highly since it’s obviously top-notch material.
Now that I’ve found what I’m looking for, it’s time to get down to business — so I clicked the appropriate link to add the dataset to my Workspace… this is where the real fun begins.
Your GeoCommons workspace is essentially an auto-generated map mashup (Google Maps right now — support for other platforms in the future, but I digress) that you can use to publish various analyses incorporating data that lives within GeoCommons, which you can then share with the world.
Once I’ve loaded that dataset into my workspace, it’s all downhill, I dragged around a bit and zoomed in a bit to generate some screenshot-worthy material, but after a few seconds, I came up with this:
That looks interesting, but I think I’d like a little more detail for one of those areas, so a couple clicks later, I had zoomed in, and was presented with a newly rendered heatmap to represent the noise sources within that extent:
And so there you have it, externally published sensor data, annotated, published, and run through the GeoIQ platform’s heatmap analysis, and finally presented as an instant google maps mashup with a few clicks of the mouse.
There’s more I could show, but I’d like to leave a few surprises in store for the coming weeks.
You can also re-export that data as KML
…and basically copy-and paste some code to include your GeoCommons workspace in your own mashup
…and of course you can load several datasets into your workspace to get more complex multivariate analysis, but sadly, nobody has yet shared much data that covers the greater Amsterdam area.Â If you have some you’d like to share, get it ready because we’d definitely like to see it contributed to the community when GeoCommons launches.
We’d welcome any questions or comments you have, each and every piece of feedback we get is taken into consideration when we plan out future development, so please don’t hesitate.
I’m also rather doubtful that we’ll be able to get away with using Fat Baby rating icons when we launch, but I’m of the opinion that stars are a little clichÃ©, so any suggestions you have about what sort of creative icon we could use to represent ratings would also be greatly appreciated
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