Driven by this thought we’re produced a new tool for the ScraperWiki Platform: an importer for GeoJSON.

GeoJSON is a file format for encoding geographic information. It is based on JSON which is popular for web based APIs because it is light weight, flexible and easy to parse by JavaScript – the language that powers the interactive web.

We envisage the GeoJSON importer being handy to visualise geographic data, and export that data to software like Tableau using our OData connector.

Why should I import GeoJSON into the ScraperWiki Platform?

Importing any data to the ScraperWiki Platform allows you to visualise your data using tools like View in a Table, or Summarise this Data which is great for this GeoJSON of Parisian Street Art:

In addition you can use tools such as download to CSV or Excel, so it will act as a file converter.

An improved View on a Map tool

We’ve improved the View on a Map tool so you can visualise GeoJSON data right on the Platform, we found that if we tried to plot 10,000 points on a map it all got a bit slow and difficult to use, so we added point clustering. Now, if you have a map with lots of points on it then are clustered together under a symbol with the number of points on it. The colour of the symbol shows the density of points… a picture paints a thousand words, so see the results below for a map of Manchester’s grit bins:

Linking to Tableau using OData

Or you could use the OData connector to attach directly to Tableau, we did this with some data from GeoNet on earthquakes around New Zealand. We’ve provided instructions on how to do this in an early blog post. If you want to try an interactive version of the Tableau visualisation then it’s here.

What will you do with the GeoJSON tool?

Canadian weather stations

Clear Climate Code is a timely project to reimplement the software of climate science academics in nicely structured and commented Python. David Jones has been using ScraperWiki views to find out which areas of the world they don’t have much surface temperature data for, so they can look for more sources.

Take a look at his scraper Canada Climate Sources. If you scroll down, there’s a section “Views using this data from this scraper”. That’s where you can make new views – small pieces of code that output the data the way you want. Think of them as little CGI scripts you can edit in your browser. This is a screenshot of the Canada Weather Station Map view.

It’s a basic Google Map, made for you from a template when you choose “create new view”. But David then edited it, to add conditional code to change the colours and letters on the pins according to the status of the stations.

This is the key powerful thing about ScraperWiki views – even if you start with a standard chart or map, you have the full power of the visualisation APIs you are using, and of HTML, Javascript and CSS, to do more interesting things later.

There’s more about ScraperWiki and the Canada weather stations in the posts Canada and Analysis of Canada Data on the Clear Climate Code blog.

Next week – part 2 will be about how to use views to output your data in the machine readable format that you want.