Archive for the ‘Mapping’ Category

Embracing Advanced Visualization - apps4NSW Comp entries

Friday, March 26th, 2010 by Jo Deeker

Space-Time Research have developed two entries for the apps4NSW competition (for New South Wales, Australia) using SuperVIEW.  The apps4NSW competition, like the Mashup Australia and Apps For Democracy competitions, invited the public to submit ideas and applications that would benefit the citizens of New South Wales.

I’m excited about our two applications because they are genuinely useful online interactive publications of complex data that everyone will benefit from.  Our Why Australians Travel application presents a dataset from Tourism Research Australia that has not been made available to the public in an interactive way before.  It also includes advanced visualization in the form of a Motion Chart (Gapminder-style) which we’re very excited by! The motion chart can tell a story with data over time that you simply don’t see in static tables or reports.

The How Safe Is Your Suburb 2.0 application provides NSW Crime data in an interactive way, allowing users to analyse relative crime rates ot absolute crime rates by suburb.  This application is supported by one of our newest features - metadata -where explanations about the data are provided to the user to help them understand the meaning of the data.

Go check our applications out and vote for us if you like them!  And if you have any feedback on our entries please don’t hesitate to make a comment on our blog here.

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Posted in Gov2.0, Mapping, Mashup, Privacy protection, Quality, SuperVIEW | No Comments »

KML Cruncher - Mashup entry

Thursday, November 12th, 2009 by Andrew Naish

The KML Cruncher was an entry in the Mashup Australia contest.

Click here to try the KML Cruncher

A utility that converts and generalizes ESRI polygon shape files into KML ready for the web. The KML Cruncher might is useful for people who want to quickly move from the shape file format into KML for web mashups.

Using the utility is easy - here’s an example of how to convert an ESRI polygon shape file to a KML file ready for the web:

Step 1 Obtain the shape file you would like to convert and save it to a local drive.

There are many example shape files at http://data.australia.gov.au.

In this example I will use the ‘Drainage Basins Queensland’ dataset available at http://data.australia.gov.au/134. Note, this utility works with polygon shape files only, so ensure you obtain a shape file that contains polygons (also referred to as ‘boundaries’). The ‘Drainage Basins Queensland’ dataset is archived in a .zip file, so make sure you extract it to your local drive before continuing.

Step 2 Now you are ready to convert your shape file.

  1. Click on the Browse button next to the ‘Choose a shape file (*.shp):’ text box.
  2. Locate and select the *.shp file from your local hard drive.

In this example I used the ‘Drainage Basins Queensland’ dataset at http://data.australia.gov.au/134, therefore I will select ‘IQATLAS.QLD_DRNBASIN_100K.shp’ file.

Step 3 Specify the dbf file.

  1. Next to the ‘Choose a dbf file (*.dbf):’ field, click on the Browse button.
  2. Locate and select the associated *.dbf file.

In this example I specified the *.dbf file that is associated with the *.shp file select in step 2, therefore I will select the ‘IQATLAS.QLD_DRNBASIN_100K.dbf’ file.

Step 4 Specify a label field. Note this field is optional.

The label field is used as an identifier for each of your converted polygons – once in KML format this is what will be shown in the information window when you click on a polygon.

This field is optional, if you do not specify it, the utility will take the first field it finds. If you would like to know what fields are available in your .dbf file you can open it using Microsoft Excel, or if you would like to inspect the data further before converting, try ESRI’s ArcExplorer product.

In this example I will set the label field to: BASIN_NAME

Step 5 Specify a generalisation tolerance.

In a nutshell the generalisation tolerance is a measurement between polygon vertices, if this tolerance is exceeded, one of the vertices will be removed. Generally you will need to specify a larger tolerance for more detailed data sets. It is likely that you will have to convert the shape file a few times to get the right tolerance, luckily I have had a bit of time to play with it, so I will specify 0.005 as the tolerance.

Step 6 Convert

  1. Click the convert button.
  2. Wait patiently and you will have a nicely generalised KML file ready to serve on the web!

Also for the developers – this is a simple HTTP post action from a WEB form (nothing fancy) therefore it could easily be used as a web service.

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Posted in Mapping, Mashup | No Comments »

The Auto Correlation Engine

Sunday, July 12th, 2009 by Andrew Naish

An idea came to me after viewing the Campaingers embrace maps article from The Economist.

Say you had a bunch of data, and I’m not talking a couple of spreadsheets, I’m talking tens of millions of records, each holding attribute information… so much data you literally don’t know what to do with - like perhaps all the information collected by governments around the world in their yearly census. It’s too big to simply browse through to find out any useful information and there’s too many geographic layers to add into a G.I.S to do any manual spatial analytics on it. But you know there’s gold in the data somewhere. You know there must be some correlation between separate observations.

Enter the Auto Correlation Engine.

Imagine you had a system where by, for each geographic layer (State, Suburb, Region, Census District, etc, etc) you could attach a predefined observation (e.g, count, percentage, calculation) and derive all the possible spatial correlation indices amongst the observations, and report them to you.

E.G:

Let’s say your a government employee in charge of deciding what to do next about the high rates of child obesity in your district. Naturally as a G.I.S user you decide to add a new layer to your system displaying the count (and the lat/long points perhaps using proportional symbols) of obese children in your district. But what next? Do you add the fast food restaurants and perhaps do some concentric ring analysis? Do compare it with a layer displaying the number of game consoles bought in the area?

What if, you had a system that had already found out, for that layer, what other geographic layers and associated attributes have a high correlation index. As soon as you added the child obesity rates to your G.I.S platform, the Auto Correlation Engine would have predetermined that there is a correlation between high child obesity rates and the number of parks in the area, and informed you of the correlation. It would then ask you if you would like to add the correlated layer to your map. Of course it wouldn’t be one of those annoying Microsoft paperclips, but it might be useful.

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Posted in Mapping | No Comments »