Suburban Trends

Suburban Trends

View Suburban Trends

http://www.suburbantrends.com.au/

Contest Winner
Excellence in Mashing and Best Student Entry
Created by
Alejandro Metke and Michael Henderson
data.australia.gov.au datasets used

Crime incidents data - 2004 International Crime Victimisation Survey (ICVS)

NSW Bureau of Crime Statistics and Research, Recorded Crime Dataset

Other datasets used

Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA) - 2006

Census of Population and Housing: Census Geographic Areas Digital Boundaries

ABS Postal Area Concordances

Suburban Trends compares and contrasts Australian suburbs. Graphical indicators reveal different aspects of each suburb, including socio-economic standing, education levels and perceived safety levels. For suburbs within NSW, crime trends are also available. Additionally, a Web Services API allows this data to be easily reused.

Judges' Comments: A mashup of different types of crime and census data that allows you to compare and contrast suburbs by a range of economic, education, safety and socio-economic indicators. The judges thought the ability to compare suburbs visually combined with the selective choice of statistics was excellent especially in a field dominated by many entries using similar datasets.

5 Responses to “Suburban Trends”

  1. Tom Loverd says:

    Excellent !!!

  2. Ei Sabai says:

    Love it! One of my favourite entries so far. Would be nice to see demographics details of each suburb too.

  3. Jim says:

    Lots of fun – I spent hours just going around looking at different groups of suburbs and how they stacked up according to different metrics.

  4. Di E says:

    This mashup is already useful – it will help my local group as we promote walking as a safe and healthy way to get around our neighbourhood. Thanks!

  5. [...] Surburban Trends a mashup of different types of crime and census data that allows you compare and contrast suburbs by a range of economic, education, safety and socio-economic indicators. The judges thought the ability to compare suburbs visually combined with the selective choice of statistics was excellent especially in a field dominated by many entries using similar datasets. [...]