I'm from UCL as well, in the Center for Digital Humanities there.
Okay, I want to talk about something I've been working on recently, which is trying
to explore some data from Victorian censuses in Britain.
If you're not familiar with the history of censuses taken in Britain, the first census
was taken in 1801.
The most recent was in 2011, the results of which, the first results of which were
published a couple of days ago.
These were the questions asked in 1801, and this was very different to most censuses.
And the collected aggregate statistics, how many households there were, how many people
there were in a particular area, and these were asked of overseers of the poor and parish
priests and other local administrative agents.
The next three censuses were quite similar, but then we moved towards a more traditional,
more contemporary structure of asking individual questions of householders.
The 1841 census suffered from quite a lot of administrative hiccups, and was quite
transitional, but from 1851 onwards a mature structure developed.
The 1851, 61, and 71 censuses in Britain were overseen by William Farr, a highly regarded
medical statistician in England.
And this affected the structure of what was asked in those censuses.
The reason for taking those censuses largely was to explore questions of ill health in
different parts of the country.
From 1891 onwards, a change of focus occurred, and more questions started to be asked about
employment and occupation, and this reflected a growing interest then in class structure.
So what happened in those censuses from 1841 onwards?
Questions were asked of individual householders by enumerators, and they recorded the answers
in enumerators' books.
Those books were then sent to central statisticians who aggregated the numbers by hand, and published
them as tables in reports to parliament.
And this was done with incredible efficiency.
A similar set of reports to senial supplements, which aggregated data manually.
In a retyping project a few years ago, it was found that in the whole of the reports
for one particular period, there were only three typos based on incorrect summations
of numbers in the whole thing.
So where can these data be had from?
The personal level data are available in the National Archives, but via a commercial organization
who charge for access.
The aggregated data, which have been digitized from the published reports, are available
via an academic website, but only a version of those tables have been digitized.
So what is the problem with trying to use existing resources?
Here's an example looking at the 1901 census.
And just by way of a test to see whether the system was working, I looked for Florence
Nightingale, because I knew I should find her in 1901.
And you do.
She was 80 years old in 1901.
If you want to get the specific page that lists Florence Nightingale, what you find
is this, a form asking you to put in your credit card details.
Not so great.
But you can do some interesting work just on the names, because looking at Florence
Nightingale, what you find is this, a frequency of Florence Nightingale's by age.
Over on the right-hand side, you've got Florence Nightingale, the famous one, who's 80.
And then you get a large number of younger people clearly named after Florence Nightingale.
The fact that there were very few people older than the 45 called Florence Nightingale suggests
it wasn't a particular combination of names.
So this isn't a random effect.
So what I've been looking at is data published by an organization called the Freesend Project.
We're attempting to retype data in an open way from copies of enumerators' books, which
are available on microfilm and so on.
And that's part of a wider group of resources, also looking at marriages and deaths registers.
They're looking at data from 1841 to 1991.
The coverage at the moment is variable over time and over space.
This is a graph of the size of their data for one particular census, 1861.
So these are the amount of data that have been typed by volunteers over time.
So about 6 million, 7 million records at the moment, out of a population of about 20 million
at the time.
For some counties, coverage is 100%.
I'm using a sample at the moment from Norfolk, which is highlighted on the right-hand side
of the map there.
I've got about 40,000 records in my current sample, but Norfolk's quite interesting because
100% of those records have been transcribed, and I'll be moving to a larger sample later
on and then onwards to the whole of Britain.
Looking at the 1861 census, it's quite interesting from a historical point of view.
Out of those 40,000 people, you get 4,500 different self-described occupations, including
some of these ones, which are less common these days.
You also get 199 different responses in my sample to the relation to the head of family.
Most of these are just different ways of writing in son or daughter or mother-in-law or someone
with different abbreviations, but also there are a lot of people that are domestic staff
in households who put their relationship as nurse or governess or something like that,
reflecting the position they held in the household.
I've done various stages in processing, attempting to automate the process.
Some of those changes are relatively simple.
This is a table of recorded responses or recorded transcribed responses to the sex
question, which should be fairly simple.
You get different problems of capitalization, also some problems of embedded spaces, but
these are all easy to modify automatically.
Some of the other questions aren't nearly so simple to modify, and this leads to a bit
of a problem in what I want to do.
Age is relatively simple, only Dussema was simple, but it has variable levels of suffixes.
In some cases, age was just written in as a number.
In other cases, it would have a suffix of Y for years or M for months or W for weeks
and so on.
This isn't necessarily straightforward.
In the case where people have used M for months, sometimes the age is more than 12 months,
so you have to work out age in years as well.
You can't just assume anyone that that's only given to infants.
County of birth, which is one of the things I'm interested in as a geographer, is coded
depending on the transcriber.
The quality of transcription varies, as you'd expect.
There are much bigger problems with parishes, and this reflects in part the data at the time.
In 1861, England was in quite a state of flux.
Parish boundaries were changing from the ancient parish boundaries to more modern ones that
afflicted ecclesiastical reality.
Modern migration is a measure of where people have moved from their county of birth to
where they were recorded at the time of census.
This is of considerable interest in geography.
The census data from 1881 were used by Ravenstein in 1885 to publish a classic paper in which
he enumerated the so-called laws of migration.
As a geographer, and as a geographer interested in migration, I'm interested in looking at
these flows, saying, can we redo the work that Ravenstein did?
The answer is, at the moment, you can't redo it, but with this data, maybe we will be able
to do so in the future.
These are the counties of birth of people living in my sample, as you'd expect.
Most of them lived in Norfolk.
As you get further away, you get fewer people.
Even at this level, there are problems.
Yorkshire, which is a big county in the middle, should be split into three, but it's only
transcribed as Yorkshire.
These are the parishes in my sample.
There's only a few of them, but they account for about 10% of the population of Norfolk
because it includes some in Norwich, the main city in Norfolk, which is at the top right.
These are the number of people recorded.
This is the proportion of people in those parishes who were migrants, who had been born
in a different parish.
This is something I want to explore at the final level.
Coding place is very hard, and we need to work harder with transcribers to set up a
formalized classification of place for them to code, rather than simply trying to transcribe
what's written on the page.
Finally, what I'm interested in is the question of, can we produce new tables, new outputs
that were never produced at the time?
Even if you digitized the original reports, you could get new information.
Here's my first example of that, which is a count of the number of multi-generational
households in Norfolk.
This is a table that was never published at the time.
What you find is the most common situation is to have two generations in your household,
a householder and their children, but you get many others, including a small number
of households with four generations of people living in the same household.
The data will be published via the ESRC Census Support Service.
Finally, I'm duty-bound from those maps to put up various citations that don't fit on
the same slide, so I'll leave it on that one.