Geocoding 1000 postcodes in 7 seconds… for free

Our drag-&-drop geocoder and converter allows you to geocode data quickly and effortlessly.

Oh, and did we mention it’s free?

Hard to believe? Here’s a list of 1000 postcodes, download it, and drop the file in the geocoder!

The story behind our geocoder and converter is closely tied to the development of Mapsdata and other products by Inquiron, which we’re part of.

Here is why: the Mapsdata online app didn’t always have many native ways to geocode data. As we’ve mentioned before on this blog, we have progressively been working to add new ways. Most recently allowing the intuitive use of simple place names.

Our mapping system, though, still ultimately rests on latitude and longitude. The way we are able to plot data on a map thanks to city names, ZIP codes, UK postcodes, etc. is by converting them into latitude and longitude. This process: finding the geographic coordinates for other forms of geographic data, is known as geocoding. In order to do this, our software engineering team had to develop ways to geocode the information as quickly and efficiently as possible, and then implement them seamlessly into the app, and they did.

We didn’t stop there, though.

We know (from first hand experience!) that it can be time-consuming and impractical to geocode, say, 1000 postcodes using online tools. Although there are numerous websites that offer this kind of service, some of them are very slow, some have low limits — allowing you to geocode only 50 or 100 entries at a time, at most — and the ones that don’t are not free.

In response to this, and because we had to develop the capabilities for Mapsdata anyway, we decided to let others benefit from it, for free, with no limit (aside from how much our servers can take!). The result is this: a brilliant, free, drag-and-drop geocoder, which will geocode this list of 1000 UK postcodes… in 7 seconds!

Below is a video of the converter in action.

Mapping Healthcare Costs: Medicare

The debate surrounding healthcare in the US has always been a lively one. As more transparency and more open data emerge and fuel it, we think it’s also becoming more interesting: A few weeks ago, a new medical insurance market place in Oregon caused two companies to lower their charges to remain competitive. The US government, for its part, released some fascinating data showing large discrepancies in what healthcare providers charge for treatment within its Medicare program of health insurance for the elderly.

As often, this data makes much more sense when visualized on a map. The example below shows the price charged by different providers for one common procedure. Some of the differences are staggering, sometimes even between healthcare providers only a few miles apart.

The map is based on the recently-released data, which lists the average charges to the patient, as well as the payments made by the Medicare program for the most common procedures. We chose one (respiratory diagnosis with less than four days on a ventilator) and mapped it. The differences were very significant even within local environments. The map above shows the Los Angeles region where you can find the charges for this one procedure to be $70,000 or less at one hospital, but $130,000 at its nearest competitor, and even $250,000 or more in some parts of the city.

Using the visualization you can zoom out to explore the entire country, finding providers only a few miles apart whose charges vary by an order of magnitude, where there is little reason for such huge discrepancies in cost.

The Medicare payments made are much more consistent, as whatever the hospital charges the government had the data to work out the correct price, but individual patients had no way to know that hospitals in the same city might be drastically cheaper. With the newly-released governments data, anyone can use Mapsdata to visualize and analyse for themselves this database and others like it.

The data can be found on the US government data portal. What could you do with it? Perhaps see how it changes at a larger scale, state by state, or check the differences for other procedures? Whatever you do, click below to try your own visualization with Mapsdata.

Try now