2006 Census Meshblock Dataset

Date listed : 26 August 2011 (3 years ago)
The 2006 Census Meshblock dataset contains counts starting at the meshblock level for selected variables from the 2006, 2001 and 1996 Census of Population and Dwellings, rebased to 2006 Census boundaries. These counts are at the highest level of each variable’s classification. The dataset also contains counts for area units, wards, territorial authorities, and regional council areas.

Dataset Information

Dataset URL ...
Re-use rights
Creative Commons Attribution 3.0 New Zealand licence

Source Agency Information

Statistics New Zealand ( 104 datasets )
0508 525 525


Date of creation
26 August 2011
Date last updated
Frequency of update


Population and society
population, statistics, area, population and dwellings

Re-uses of this dataset

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  • Hot Mash by Bernard O'Leary

    Hot Mash is a dynamic heatmap tool that allows the user to view changes in the Stats NZ census data geographically over time. I have selected five questions that seem interesting to me, from the fifty-odd available in the Stats NZ census dataset. So, the user can also potentially make a geographic comparison between questions - for example level of education attained vs. level of household income. I have searched far-and-wide for another heatmap tool that focuses on New Zealand census data. To my way of thinking, the census dataset is crying out to be heatmapped, so I took the opportunity to do it!

  • NZ People on Maps by Nora Wang

    This entry takes data from the NZ 2006 Census and presents it as a series of maps to help us understand the people of NZ a little better. While the census data is very useful for producing statistics and summaries, it can be hard to understand the patterns across the country until you see it on a map. This data mashup uses Google Fusion tables to hold the census data, and presents various columns of it in a Google map with an overlaid NZ Census Area Unit (CAU) layer accessed as a KML file. Pie charts or swing-o-meters are also available by clicking on a census area unit on the map, for categories where there are many variables available (eg ethnicity). Linking charts to maps like this is a useful way to allow the user to examine the data in greater detail, and in different ways yet keep the interface simple. The website is designed to be very easy to use, and hopefully to ignite some enthusiasm in the user to zoom in and pan around the map looking for interesting spatial patterns and trends... and learn a bit more about NZ and the distribution of its inhabitants.

  • Know where - the earthquakes are (in 3D) by Miles Denton (Critchlow)

    This study shows earthquake density, population density and dwelling insurance cost per meshblock for New Zealand in 3D. Earthquakes are a hot topic currently around NZ, especially due to the Christchurch Earthquakes which devastated the Canterbury region and the people of NZ. However did you know that in 2009 an earthquake of 7.8 hit Fiordland at a depth 30km. The February Christchurch earthquake was a magnitude 6.3, 10 km south-east of Christchurch at a depth of 5 km. Imagine if there was a large population density or city in Fiordland during the 2009 quake, the consequences could have been far worse than Christchurch. This is why the location of populations/ cities are important to avoid events like Christchurch. Obviously proximity to fault lines (especially unknown ones) are also important, along with soil type. What I hope this 3D visualisation does, is to get people to think about Location Intelligence and how this industry can help shape our future and that of New Zealand cities. I think the 3D element provides the audience with new insights on eathquakes and location, this is why it's original. This is why I use Critchlow's catch phrase of - know where. Location is very important and it can not only save lives but also your pocket. This is why the cost of dwelling insurance is "mashed up"/ included, which adds value and something different. The most useful part of the study I discovered was that Wellington pays a high premium on dwelling insurance even though the frequency of earthquakes are low. The most appealing aspect would be the seven "mash up" comparisons between the three data sets in 3D. The most fascinating and gosh darn awesome part would definitely be Christchurch due to its high levels across all visualisation models and Fiordlands frequency of earthquakes! Please refer to the following links to access and view all 7 pdfs (Google couldn't share them all on one page/link, as they are individual pdfs, the offical link pasted further into the entry form is all three datasets mixed and mashed together): EQ=Earthquake, IN=Insurance & POP=Population EQ EQ_IN EQ_POP EQ_POP_IN IN POP POP_IN

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