A Retrospective Look at NHS Income Data

How bad were the NHS income numbers?

Jens von Bergmann

13 minute read

NHS Income Data, a First Retrospective There was much hand wringing when NHS income data got released. The change in methods were big, most notably the replacement of the mandatory long form census, that was administered to a random 1 in 5 sub sample, by the voluntary NHS that went out to approximately 1 in 3 households. The (design-weighted) response rate for the NHS was 77%, compared to 94% for the long form in 2006.

Evolution of the Income Distribution

Digging deeper into the evolution of incomes

Jens von Bergmann

9 minute read

Vancouver’s median household income has grown. But there are many ways how this could have happened. We want to take a deeper look to understand how the income distribution changed. To that end, we will investigate the change in the number of people in each income bracket between the census years. And put that into context to what happened in the region and Canada wide. This is a mixture of what we have done when comparing the size of age groups between censuses.


Multi-category dot-density maps are hard.

Jens von Bergmann

10 minute read

I started writing this blog post in December 2015, when CensusMapper quite a bit younger and I hacked together some basic dot-density maps. I never much liked the results and have been slowly improving and thinking about them. I am still not entirely happy with the current implementation, but it is slowly getting there. The final impulse to finsish this post was the work on cancensus, and R wrapper for the CensusMapper API my explorations in multi-category dot density maps in R, now tied up into the new dotdensity package.

Surprise Maps

Showing only what matters.

Jens von Bergmann

9 minute read

At CensusMapper we like building models based on census data. We now have a common tiling for 2011 and 2016 geographies that allows us to easily model changes over time. After building a model we often want to see how well the model performs. An easy way to do this is to simply map the difference of observations and model predictions. Those maps are great and it is easy to understand what is mapped.

Comparing Censuses

Not so unique GeoUIDs and other pitfalls.

Jens von Bergmann

7 minute read

It’s great to have fresh census data to play with. Right now we only have three variables, population, dwellings and households. There is still lots of interesting information that can be extracted. So we started exploring in our last post, things get really interesting when looking at change between censuses. But as we noted, there are several technical difficulties that need to be overcome. So we at CensusMapper took that as and invitation to do what we love most: breaking down barriers.