Aberdeen Plaques – Part Two

In part one I described what we did at CTC18 to capture data and images of Commemorative Plaques in Aberdeen, and what I then did in the following three weeks.

A few people asked my why we would bother to put plaques into Wikidata and WikiCommons in this way. Why not have a council website – or why not use Open Plaques?

In this second instalment I am going to demonstrate how we can use the data which we have created to make some interesting visualisations and even do some calculations and analysis.

It can also power other new apps and services – allowing developers to create tailored routes around the city, on themes such as the arts or medicine – which is beyond the scope of this post.

Getting Started

At the time of writing we now have 132 Aberdeen Commemorative Plaques recorded  in Wiki Data.

I can check that with this simple query on the Wiki Data Query Service:

Plaques - Query One
Plaques – Query One

All that this does is ask for every instance (P31) of a commemorative plaque (Q721747) whcich is located in (P131) the Aberdeen City (Q62274582) area.

Try It for yourself.

Click on the white-on-blue arrow at the left. See what it produces. Note the bottom half of the screen turns into a table of results, and on the centre bar there is a message ‘xxx results in xxxx milliseconds‘.

How many pictures of plaques?

I can retrieve the photograph for plaque using the following query.

Plaques - Query Two
Plaques – Query Two

Here I am saying give us plaques which have image (P18). In effect this is saying ONLY those that have an image. If not all entries have an image, yet, then we will get a smaller number.

Try it.

As I run it I get 126 – which is six fewer than I got plaques.

Get all plaques with images or not

Let’s modify the query to this.

Plaques - Query Three
Plaques – Query Three

Here I am the OPTIONAL command which has the effect of saying IF there is an image give me it, but don’t restrict the results to only those with images. When we run that we can spot the missing ones by scrolling down through the list. I get six plaques with no images. This is a useful technique to spot missing things when totals (in this case plaques and images) don’t tally.

Try it.

Commemorating who or what?

As it stands the query is still not very user-friendly as all we have for the plaques is their Plaque ID. Of course we can click on those, but it would be more helpful to have the names of their subjects.

We’ll do that in two steps.

Firstly, let’s work out what the subjects are.

We can add the following line to the query and remember to add ?subject to the SELECT on the first line.

 ?plaque wdt:P547 ?subject

Note P547 is the statement “commemorates“.

Try it

If we run that we get a new column called subject and it is filled with links to subject IDs, which are the Wikidata entries for either people or things that the plaques commemorates. I note that when I run it my list has grown from 132 to 134.

Any guesses why that should be?

Some of the plaques commemorate more than one person.

Let’s make it a bit more friendly.

Add the following line just before the end of your query

 SERVICE wikibase:label {bd:serviceParam wikibase:language "en". }

And change ?subject to ?subjectLabel in the first line.

This instructs the WikiData Query service to use another service to retrieve labels from the items.

Plaques - Query Four
Plaques – Query Four

The label is in effect the title of the Wikidata item. Look at this one https://www.wikidata.org/wiki/Q80818579 Immediately below the title, and to the left, there is an edit link. Click that. See how the ‘label‘ and the ‘description immediately below it become editable. Cancel that for now.

Try running that query to get subject names (labels) back

Now we have a name (in a subjectLabel column) for who or what is being commemorated.

Which provosts have plaques?

We can ask which of our plaques commemorates a previous Lord Provost of Aberdeen.

We use the P547 (commemorates) statement to get our subject, then use the following

subject wdt:P39 wd:Q57906938.

where P39 is Position Held, and Q57906938 is the identifier for Lord Provost of Aberdeen.

Plaques - provosts?
Plaques – provosts?

Currently we appear to have four plaques to former Lord Provosts.

Try it

A different view

At this point you might want to change the view for your query just to have a look at the images we have.

Above the table of results, on the extreme left there is an eye symbol and a drop down. Choose “Image Grid” to see the images only.

Plaques - change view
Plaques – change view

You might also have noticed that there are other options, several of which are greyed out as we don’t yet have that data in our query. These views include ‘Map‘ and “Timeline‘. We’ll come back to those.

Our Image Grid looks something like this:

Plaques - Image Grid
Plaques – Image Grid

Remember to swap back to ‘Table’ view once you’ve finished.

Adding more data fields

We can now add more data fields to our query.

Firstly, let’s add the geographic coordinates of the plaques’ locations.

Add the following line to your code:

 OPTIONAL {?plaque wdt:P625 ?coordinates .}

and, again add the new value, ?coordinates to the first line of the query too.

You will now have an extra field in the returned data table.

Try it 

Mapping results

Now change the view from Table to Map. The Wikidata query service automatically uses the coordinates to plot the results on a map which is scaled to show the results. You may need to scroll down to see all of the map. Click on one of the plotted points. You should get a pop up with the name of the person or building commemorated, plus a photo of the plaque itself, as shown below.

Plaques - map view
Plaques – map view

Note – if you add the following as the first line of your query, it will default to a map view rather than table when first run.

#defaultView:Map

Now let’s see if we can get more data for the people for whom there are plaques.

Dates of birth and death

We can change our query to find out if there are dates of birth and death for our human subjects  (rather than buildings).

We can use P569 (date of birth) and P570 (date of death) and ascribe those to
?DOB and ?DOD respectively – again, adding those fields to our SELECT statement on line one. Your query should look like this?

Plaques - Query Five
Plaques – Query Five

Try it

Looking at our table of results we can see that we have a mix of types of results – people, bridges, buildings etc. but only the people have dates.

Table showing dates of birth
Table showing dates of birth

Interestingly the one subject with the DOB and DOD in the screenshot above is Elizabeth Crombie Duthie who gifted Duthie Park to the city of Aberdeen.

Remember, if you change the DOB and DOB from being OPTIONAL to just being regular requests, you can filter records to show ONLY those with dates associated with them which will screen out not only non-human subjects but will exclude any people with incomplete or missing dates.

Notable people

It could be argued that the fact there is a plaque to a person would indicate that they are notable, but not every person or object for which there is a plaque has a Wikipedia article. Let’s add some code to see which of our plaques has an associated article.

Plaques - Query Six
Plaques – Query Six

Try It

Changing the above so that we remove the OPTIONAL {} around the section beginning ?article  we get ONLY those with Wikipedia articles which is, as I run it, 79 plaque subjects.

You can if you want we add the following

 ?subject wdt:P31 wd:Q5 .

where P31 (instance of ) is Q5 (human) we can screen out all of the non-people plaques.

Try it

At this point, try flipping the view to TimeLine – you may have to scroll down quite a way to see all of the plaques. Many of them are concentrated at the right, spanning much of the 20th century. You should see John Barbour (1316-1395 at the extreme left).

Plaques - timeline
Plaques – timeline

Finally, before we start doing some statistical analysis let’s try something more sophisticated.

Can we create a map showing only female subjects whose work was in the medical sciences?

To do that we need to select only subjects who have a P21 (gender or sex) of Q6581072 (female). Then we need to select an occupation (P31) which is an instance or subclass of Q66811410 (the medical profession). This requires a structure that we haven’t see before:

?occupation wdt:P31/wdt:P279* wd:Q66811410

While we are at it, let’s get an image of the subject if there is one, and find out of there is a wikipedia article about the subject. And, since we want a map, we add that as our default view at the top.

Plaques - map of female medics
Plaques – map of female medics

This gives us the following output:

Map view of female medics
Map view of female medics

Try it

Changing this query to male (Q6581097) or choosing different types of professions is straightforward.

Statistical analysis

The Wikidata Query Service allows us to move beyond visualising the data in different ways. Let’s have a look at a couple of examples.

Analysing who or what is commemorated

The following query finds out what the subject of the plaque is an instance of (P31) – line 6:

Plaque - query seven
Plaque – query seven

but instead of creating a list, it use the COUNT () function to analyse the subject being an instance of (P31) Instance Of.

Try it

We can see that we have 105 humans, 5 lanes etc. Note that some double counting occurs. Some structures, for example, are instances of two things.

We can also analyse the gender of the human subjects just by changing P31 in the above to P21 (Sex or Gender).

At present I get

Plaques by gender
Plaques by gender

That’s far from gender equality, isn’t it!

What’s in a name?

Ascertaining the most common first names on plaques is also straightforward.

We use P735 (given name) statement, get the labels, count and group by those.

Try it.

We get the following results

Plaques - given names chart
Plaques – given names chart

With 81% of plaques to people being for males it is hardly surprising that our league table of names begins with James, William, George, John, Alexander ….

We can do more sophisticated analysis too.

Analysing Occupations

We can add the following line to our query to get back the occupation of the subject of the plaque:

 ?subject wdt:P106 ?occupation

Bear in mind that many of our plaque subjects are true polymaths. Have a look at Robert Brown. He has 10 listed occupations!

So what are the most common occupations of those people for whom there are plaques? Any guesses?

Let’s use the following query:

Plaques - Using Count()
Plaques – Using Count()

This uses the COUNT () function as well as a GROUP BY clause. The query looks at all of the different occupation labels, counts how many of each there are.

Try it

This returns, by default, a table of values. We can flip to a Bar Chart to make better sense of the data:

Plaques - Bar Chart of occupations
Plaques – Bar Chart of occupations

So, we can see that for those commemorated by a plaque the most common occupations are Physician, Painter, University Lecturer, Writer and so on.

We can add a couple of refinements if we wish. If we want our query to default to a BarChart when we run it we can add the following line at the start of the query:

#defaultView:BarChart

and if we want the table to be sorted by value we can add a line such as

ORDER BY DESC (?count)

Try it

What next?

Over the last month I’ve been busy gathering data, taking photographs and publishing all of those on WikiData and wiki Commons. That phase is not quite complete, if it ever could be considered complete. You can monitor live progress here.

There are a couple of photographs which I can’t easily take which I know Aberdeen City Council’s Museum and Galleries team have. It would be great to see those made available by them on Wiki Commons, as I have shared the 148 plaque photos I have taken.

I know of at least 24 more plaques which I have photographed which are not listed yet in Wikidata.

When I published part one of this series I got some great feedback on Twitter. One suggestion is that we add structured data to the Wiki Commons pages for each photograph. Another was to add further data to the record for each plaque using statement P276 (location) where the plaque is on a known listed building. So far I have done that for 5 plaques – check it for yourself. There are loads more to do.

Many of the people records that I have created in Wikidata are skeletal. They need more detail, photographs, biographical links etc. Similarly, given that people or places are noteworthy enough to merit a plaque, they should pass the notability test for Wikipedia, yet at least 68 plaque subjects have no Wikipedia entry.

And plaques are just a start – an easy introduction to what is possible given, in this case, about 100 hours of work. While that was almost all done by one person, if we ran a Code The City weekend on a similar theme and similar sized challenge, six people could achieve the same over a weekend with a little coordination.

At Code The City, we’re about to start discussions with the local cultural institutions about setting up a more formal alliance for the city (shire?) to help shape how they use digital and data more effectively and grow volunteers with skills and tools to make that happen, which is an exciting note on which to finish this post! Watch this space, as they say.

Ian

Aberdeen Plaques – Part One

On Saturday 14th December 2019 we ran a one-day mini hack event. The idea behind it was for people to come along for a day to work on their side projects and, if they needed support, attempt to persuade others to assist them.

That’s what I did with my Aberdeen Plaques project: something I’d had on the back burner for more than a year.

Why do it?

The commemorative plaques which are dotted around the city are a perfect candidate for open data. They have a subject, usually some dates, are located somewhere, and are of different types etc. Making that all available as open data would open up a whole range of possibilities.

Some Aberdeen plaques
Some Aberdeen plaques

If we captured all of that well then we could do analysis on the data (ratio of women to men, most represented professions), create walking routes (maybe one for the arts, one for the sciences and so on), create timelines to see what periods are more represented.

Having recently trained as a WikiMedia UK trainer – and having experimented with some of the tools (Wiki Commons, Wiki Data, Wikipedia, Histropedia) I was convinced that these were the right way to go.

Pre-event prep

So, in advance of the hack day I’d done a bit of prep in the two weeks running up to the day iteself.

I’d created a spreadheet which recorded the
* subject (person or ‘thing’)
* Gender if known
* the link to the now-retired city council plaques system (hidden from public view)
* The location if known
* The geo coordinates (to be determined)
* Whether the subject had a Wikipedia page (tbd)
* Whether there was an image of the plaque on Wiki Commons (tbd)
* Whether the subject of the plaque was represented on Wiki Data (tbd)
* Any identifiers on Open Plaques (tbd)
* Any external links (eg to Flickr for photos)

I’d then populated some of the data (eg whether there were images of the plaque on Wiki Commons) as well as some other bits. But most cells were blank.

Pre-event spreadsheet
Pre-event spreadsheet

As a keen walker and photographer I had also photographed and uploaded seventeen plaque images to Wiki Commons in the lead up, so that we would have some images to work with.

How to use our time most effectively on the day?

Our aim for the day was then to find out what data / info / images existed, fill in the gaps, and explore how to use WikiData to store and retrieve data, and how we could potentially create maps, timelines and similiar new products.

What we did on the day

At the start of the event we pitched our project ideas, and I managed to persude five others (Angela, Mike, Stephen, James and Steve) to join me in working on the plaques project.

Angela and Mike, and later Angela and Stephen would go out and take photographs. Steve, James and I would work on the data capture, completing research on what existed, creating new entries for the data on Wiki Data, and testing queries on the Wiki Data query service.

How we did it

We used the spreadsheet that I had set up to capture all of the data we’d gathered – and as it eveolved it would show progress as well as what was still lacking. We had no expectations that we would do it all on the day, but we could pick away at it in future weeks and months.

In the run-up to the event I’d discovered The Pingus’ album of plaques photographs on Flickr. Sadly these had not been published with a licence that would allow us to use them. I’d sent a request, a few days before CTC18, for them to change the licence for the Aberdeen plaques pictures to a CC-SA one. This would have allowed our republishing on Wiki Commons. Sadly it didn’t elicit a response. But the album did show that there were many more plaques than the old ACC system listed. And it was possible to get co-ordinates from them. So the number of plaques to deal with kept growing.

During the day James filled in loads of gaps in which subjects were on Wikipedia and which on Wikidata.

Steve and I experimented with capturing and querying the data. Structuring that in a way that aids recall through Wiki Data Query Service was an interative process. Firstly I tried adding a statement ‘commomorative plaque image’ (P1801) into the wikidata record for the subject as you can see in this first example https://www.wikidata.org/wiki/Q2095630. But that limited what we could do.

So, we discovered that we could create a new object which was an instance of commemorative plaque. Our first attempt was https://www.wikidata.org/wiki/Q78438703 and we evolved what we captured there – adding statement, and Steve discovered the ‘openPlaques plaque ID'(P1893). Incidentally we also tried ‘openplaques Subject ID’ (P1430) but adding that to the plaque object throws an error. The latter should be added to the person record not the plaque.

At the end of CTC18

We ended the day with

  • 138 plaques listed.
  • 57 sets of co-ordinates identified
  • 68 Wikipedia articles identified as matching plaque subjects (and eleven plaques subjects who had NO wikipedia page)
  • 36 Images in WikiCommons
  • 77 WikiData entries for the subject of the plaques (existing or created)
  • 11 new wikidata entries for the plaques themselves

This was a great leap forward in one day and would pave the way for future work.

What next?

Since CTC18 ended, I’ve got firmly stuck into this project over the xmas break. Over the last three weeks I have now photographed over a hundred plaques (plenty of walking) and have created wikidata entries for most plaques and also their subjects in wikidata.

I’ll cover all of that, and how we can now use the data in part two, coming soon.

2019 – the year in review

Intro

The year just past has been a pivotal one for Code The City, we’ve moved into a new home, expanded our operations, engaged with new communities of people, and started to put in place solid planning which will be underpinned by expansion and better governance. 

Here are some of the highlights from 2019.

Sponsors, volunteers and attendees

We couldn’t do what we do without the help of some amazing people. With just three trustees (Bruce, Steve and Andrew) and Ian our CEO, we couldn’t cover such a range of activities without serious help. Whether you come to our events, volunteer, or your company sponsors our work, you are making a difference in Aberdeen. 

Listing things is always dangerous as the potential to miss people out is huge. But here we go! 

The Data Lab, MBN Solutions, Scotland IS, InoApps, Forty-Two Studio, who all provided very generous financial support; H2O AI  donated to our charity in lieu of sponsorship of a meet-up;  and the James Hutton Institute and InoApps who also donated laptops for us to re-use at our code clubs. Codify, IFB, Converged Comms who provided specific funding for projects including buying kit for code club, and paying for new air quality devices – some of which we have still to build.

Our regular volunteers – Vanessa, Zoe, Attakrit, Charlotte, and Shibo –  plus the several parents who stay to help too, all help mentor the kids at Young City Coders club. 

Lee, Carlos, Scott, Rob who are on the steering group of the Python User Group meetup. 

Naomi, Ian N, David, and Gavin who are on the steering group for Air Aberdeen along with Kevin from 57 North who supervises the building of new sensor devices. 

The ONE Tech Hub, and ONE Codebase have created a great space not only for us to work in, but also in which to run our public-facing events. 

Everyone who stays behind to help us clear away plates, cups and uneaten food – or nips out to the shops when we run out of milk.

Apologies to anyone we have missed!


And finally YOU – everyone who has attended one of or sessions – you’ve helped make Aberdeen a little bit better place to live in. Thank you!

Hack weekends

We ran four hack events this year. Here is a quick run-down. 

Air Quality 1

We kicked off 2019 with the CTC15 AIr Quality hack in February. This saw us create fourteen new devices which people took home to install and start gathering data. We also had a number of teams looking at the data coming from the sensors, and some looking at how we could use LoraWAN as a data transport network. We set some targets for sensor numbers which were, in retrospect, perhaps a little ambitious. We set up a website (https://airaberdeen.org

Air Quality 2

Unusually for us we had a second event on the same theme in quick succession: CTC16 in June. Attendees created another fourteen devices. We developed a better model for the data, improved on the website and governance of the project. We got great coverage on TV, on radio and in local newspapers. 

Make Aberdeen Better

CTC17 came along in November. The theme was a broad one – what would you do to make Aberdeen a better place to live, work or play? Attendees chose four projects to work on: public transport, improved methods of monitoring air quality, how we might match IT volunteers to charities needing IT help, and the open data around recycling.

Xmas mini-hack

CTC18, our final hack of the year was another themeless one, timed to fit into a single day. We asked participants to come and work on a pet side-project, or to help someone else with theirs. Despite a lower turnout in the run-up to Christmas, we still had eight projects being worked on during the day.

New home, service

In the late summer the ONE Tech Hub opened and we moved in as one of the first tenants. So far we rent a single desk in the co-working space but we aim to expand that next year. The building is great, which is why we run all of our events there now, and as numbers grow it promises to fulfil its promise as the bustling centre of Aberdeen’s tech community. 

Having started a new Data Meet-up in 2018 we moved that to ONE Tech Hub along with our hack events. We also kicked off a new Python User group in September this year, the same year as we started to deliver Young City Coders sessions to encourage youngsters to get into coding, using primarily Scratch and Python. 

We also ran our first WikiMedia Editathon in August – using WIkipedia, WIki Commons and Wikidata to capture and share some of the history of Aberdeen’s cinemas using these platforms. We are really supportive of better using all of the wikimedia tools. Ian recently attended a three-day course to become a wikimedia trainer. And at CTC18 there were two projects using wikidata and wiki commons too. Expect much more of this next year! 

Some recognition and some numbers

We’ve been monitoring our reach and impact this year.  

In March we were delighted to see that Code The City made it onto the Digital Social Innovation For Europe platform.  This project was to identify organisations and projects across the EU who are making an impact using tech and data for civic good. 

In July we appeared for the first time in an Academic journal – in an article about using a hackathon to bring together health professionals, data scientists and others to address health challenges. 

We will be launching our  dashboard in the New Year. Meantime, here are some numbers to chew on. 

Hack events

We ran four sessions, detailed above. We had 102 attendees and 15 facilitators who put in a total of 1,872 hours of effort on a total of 20 projects. All of this was for civic benefit. 

Young City Coders

We ran six sessions of our Young City Coders which started in September. The sessions had a total of 114 kids attending and 28 mentors giving up two hours or more. 

Data Meet-ups

In 2019 we had 12 data meet-ups with 28 speakers and 575 attendees! This is becoming a really strong local community of practitioners and researchers from academia and local industry. 

Python Meet-ups

Each of our four sessions from September to December had a speaker, and attracted a total of 112 attendees who were set small project tasks. 

The year ahead

2020 is going to see CTC accelerate its expansion. We’re recruiting two new board members, and we have drawn up a business plan which we will share soon. That should see us expand the team and strengthen our ability to drive positive societal change through tech, data and volunteering. We have two large companies considering providing sponsorship for new activities next year.  We’ll also be looking at improving our fundraising – widening the range of sources that we approach for funding, and allowing us to hire staff for the first time. 

Open Data

We’re long-term champions of open data as many of you will have read in previous posts. We’ve identified the need to strengthen the Open Data community in Scotland and to contribute beyond our own activities. Not only has Ian joined the Civic side of Open Government Partnership, and is leading on Commitment three of that to improve open data provision, but he has also joined the board of the Data Commons Scotland programme at Stirling University. 

Scottish Open Data Unconference

Beyond that we have created, and we are going to run, the Scottish Open Data Unconference in March. This promises to be a great coming together of the data community including academia, government, developers, and publishers. If you haven’t yet signed up please do so now – there are only 11 tickets of 90 still available. We’ll also need volunteers to help run it: scribes for sessions, helping to orientate new visitors, covering reception, photography, blogging etc. Let us know how you could help. 

We look forward to working with you all in the New Year and wish you all a peaceful and relaxing time over the festive period. 

 

Ian, Steve, Bruce and Andrew

[Photo by Eric Rothermel on Unsplash\

A timeline of Female Aberdeen Uni Graduates

Background

Earlier this year Code The City held an Editathon with Wikimedia UK. The subject was the history of Aberdeen Cinemas. We ended up with 16 people all working together to create new articles, update existing ones, capture new images for Wiki Commons, and generate or enhance WikiData items. This was a follow up to previous sessions that Dr Sara Thomas of WikiMedia UK led for us in the city, mainly for information professionals.

This has led to significant interest from cultural bodies in the city in using the suite of WikiMedia platforms and tools to improve access to their collections in Aberdeen. We expect to do quite a bit more of this with them in 2020.

Two weeks ago I attended a Train the Trainer 3-day workshop in Glasgow for Wikimedia UK to become a trainer for them in Scotland.  That will see me training professionals and volunteers in how to use Wikipedia, Wiki Commons and Wikidata in particular.

In this blog post I explain why you might want to use some of the fancy features of WikiData query service, show you how to do that, using on my adaptation of others’ shared examples, and encourage you to experiment for yourself.

Wikidata

Wikidata uses a Linked Open Data format to store data. While I have added quite a number of items to Wikidata I’ve not had a chance to really study how to use SPARQL (the query language behind the scenes) to to execute queries against the data. This is done in the Wikidata Query service. This is a key skill to using some of the more advanced features. Without the means to extract data there is little point in stuffing data into it. In fact WikiData allows us to do some very fancy things with the data which we retrieve.

So, I decided this week to start working on that. This describes the first steps  I have been doing. It should also provide a simple introduction to any else wanting to dip their toe in the SPARQL waters.

Where to start?

This 16-minute tutorial on Youtube is a great place to begin; it is where I started. It describes how to create a simple query and build it up to something more powerful.  I copied what it did then adapted that to build a query that I wanted. I suggest that you watch it first to understand what each line of SPARQL is doing.

Here are the steps, mainly frown from and adapted from that tutorial.

Find all female graduates of Aberdeen University
Find all female graduates of Aberdeen University

In the query above we use the Educated at statement (P69) and the identifier for Aberdeen University (Q270532 ) in combination with the Sex or gender statement (P21) with the Female identifier (Q6581072).

You can run this for yourself here using the white-on-blue arrow. I’ve used one of the great things you can do with Wikidata which is to share this query  using the link symbol on the left of the page just above the arrow:

Save a Wikidata query
Save a Wikidata query

Changing the parameters of the query means that we can check males (Q6581097) against females (Q6581072). Or you can compare different universities. To do this go to the Wikidata homepage and search for the name of the institution. The query will return a page with the Q code in the title. Thus we can compare various universities by amending the Q code in the query above: University of Aberdeen (Q270532) with University of Glasgow (Q192775) or Edinburgh University (Q160302).

Running these queries we can see that the number of both male and female graduates with entries on WikiData of Aberdeen University  is significantly smaller than from either Glasgow or Edinburgh, and we can see that the proportion of females of all graduates for each university is smallest for Aberdeen.

 

University Male Grads Female Grads % Female
Aberdeen 944 125 11.7
Edinburgh 3804 571 13.1
Glasgow 1562 291 15.7

The results of these queries should themselves cause us to reflect on the relatively smaller number of results of either gender from Aberdeen compared to the other universities;  and also the smaller proportion of women. It suggests that there is some work to do to ensure that we get better representation of both genders in Wikidata.

Enhancing our query

Now that we have a basic query we can retrieve additional bits of data for the subjects of the query including place of birth, date of birth and images.

These are represented by P19 (birth place), P560 (date of birth) and P18 (image). As we see in the example below, when we query these we follow them with a name we assign to the item returned (e.g. ?person wdt:P19 ?birthPlace ) and we add the name we give it, in this case ?birthPlace to the Select statement on the first line of the query, ensuring that it will feature in the data returned in the table or other format output.

enhanced wikidata query

You will note that the above example now uses the ?birthPlace  to create a new query to get the co-ordinates (P625) of that place which we assign to coordinates:

> ?birthPlace wdt:P625 ?coordinates

and we include coordinates in the first line of things we will display.

Advantages of extra data elements

By having birthplace coordinates we can plot the results in a map which is easily done using the tools built into the wikidata query service.

Run the query (white arrow on blue on the left menu) and observe the table that was returned. You can see that the first line of the Select statement formed the columns of the table.

Table of wikidata query results
Table of wikidata query results

Note that instead of 125 results as we had in the simple query, we only get 20 results. My understanding of this is that we are specifying records which must have a place of birth, an image etc. Where these do not exist then they records for that person are not returned. This in itself shows that there is a piece of work to do to identify where records in the batch of 125 lack these elements and fix them.

In fact you could say that there is a whole cycle of adding data, querying it, spotting anomalies, fixing those and re-querying which leads to substantial enrichment of the data.

Map results

Now click on the dropdown by the eye symbol, on the left immediately above the results, and choose the map option. The tool will generate a map with a pin in the location of each place of birth. You can pan and zoom to the UK and click on each pin. Try it. To get back to the query, click on the arrow, top-right.

wikidata map view with clicked point
Wikidata map view with clicked point

A timeline

Now click on the eye symbol to show other options, and choose Timeline.

As we can see below, the Wikidata query service will construct a rudimentary timeline with relatively little effort.  This is one of its great features. So far we have the same 20 complete records – and the cards or tiles are titled by the place of birth but we can change that.

Wikidata timeline
Wikidata timeline

Enhancing the timeline on Histropedia

To improve on our timeline we can construct a better query using the Wikidata Query Service then paste it into the Histropedia service to run it.  Our first version which makes small improvements on our previous timeline produces the results below. This labels by the person’s name, and colour codes the individual records by place of birth label. To see the code, click the gear wheel at the top right of the screen. Note we still only retrieve 20 results.

A first query on Histropedia
A first query on Histropedia

We can substantially enhance this query as we have done on the following version. This makes certain items optional, gets the country of birth and colour-codes by that, and ranks the records by prominence (with the most prominent at the front). If I understand it correctly by using optional elements it also retrieves 76 records, much more than previously.

enhanced Histropedia timeline
enhanced Histropedia timeline

I would encourage you to watch the tutorial video at the start of this post, then try to hack some of the queries to which I provided links. For example how many female graduates of the Robert Gordon University would each query generate? How would you find the Q code of that institution?  Have fun with it!

 

We help kids in regeneration areas. What’s one of them?

At CTC we work with ONE Codebase to deliver Young City Coders classes. These are after school activities to encourage young people to get into coding by trying Scratch, Python and other languages in a Coder Dojo like environment.

Inoapps generously gave us some funding to cover costs and donated old laptops (as did the James Hutton Institute) which we cleaned up and recycled into machines they could use.

All of which is great – and we have 20-25 kids each session starting to get into these coding languages.

The Challenge

But there is an issue – the bulk of our kids are overwhelmingly from west-end schools. And we have an aim to help kids in regeneration areas where opportunities are generally fewer.

So, that means identifying Aberdeen schools that fall in the regeneration areas and contacting the head teacher and having a discussion about what help they would like to see us provide. Simple?

No.

Search for regeneration areas

Starting with the basics – what are the regeneration areas of Aberdeen? According to Google, the Aberdeen City Council website doesn’t tell us. Certainly not in the top five pages of results (and yes, I did go down that far).

Google’s top answer is from the Evening Express article which says that there are five regeneration areas: Middlefield, Woodside, Tillydrone, Torry and Seaton. From what I have heard that sounds like it might be about right – but surely there is an official source of this.

Further searching turns up a page from Pinacl Solutions who won a contract from ACC to provide wifi in the Northern regeneration areas of “Northfield, Middlefield, Woodside and Tillydrone.” Which raises the question of whether Northfield is or isn’t a sixth regeneration area.

The Citizens Advice Bureau Aberdeen has an article on support services for regeneration areas of “Cummings Park, Middlefield, Northfield, Seaton, Tillydrone, Torry, Woodside and Powis.” That adds two more to our list.

Other sites report there being an “Aberdeen City Centre regeneration area.” Is that a ninth?

Having a definitive and authoritative page from ACC would help. Going straight to their site and using the site’s own search function should help. I search for “regeneration areas” and then just “regeneration.”

ACC results for regeneration areas
ACC results for regeneration areas

I get two results: “Union Street Conservation Area Regeneration Scheme” and “Buy Back Scheme”. The latter page has not a single mention of regeneration despite the site throwing up a positive result. The former appears to be all about the built environment. So it is probably not a ninth one in the sense that the others are. Who knows?

So what are the regeneration areas – and how can I find which schools fall within them?

Community Planning Aberdeen

Someone suggested that I try the Community Planning Aberdeen site’. Its not having a site search wasn’t very helpful but using Google to restrict only results from that domain threw up a mass of PDFs.

After wading through half a dozen of these I could find no list or definition of the regeneration areas of the city are. Amending the query to a specific “five regeneration areas” or “eight….” didn’t work.

Trying “seven regeneration areas” did return this document with a line: “SHMU supports residents in the seven regeneration areas of the city.” So, if that is correct then it appears there are seven. What they are – and which of the eight (or nine) we’ve found so far is not included – is still unknown.

Wards, neighbourhoods, districts, areas, school catchment areas

And – do they map onto council wards or are they exact matches for other defined areas – such as neighbourhoods?

It turns out that there are 13 council wards in the city. I had to manually count them from this page. I got there via Google as search the ACC site for Council Wards doesn’t get you there.

I seem to remember there were 37(?) city neighbourhoods identified at one time. To find them I had to know that there were 37 as searching for “aberdeen neighbourhoods’ wasn’t specific enough to return any meaningful list or useful page.

And until we find our what the regeneration areas are, and we can work out which primary and secondary schools fall in those areas, we can’t do very much. Which means that the kids who would benefit from code clubs most don’t get our help.

I though this would be easy!

At the very minimum I could have used a web page with a list of regeneration areas and some jpg maps to show where they are. That’s not exactly hard to provide. And I’d make sure that the SEO was done in a way that it performed well on Google (oh and I’d sort the site’s own search). But that would do at a pinch. Sticking at that would miss so many opportunities, though.

Better would be a set of Shape Files or geojson (ideally presented on the almost empty open data platform) with polygons that I could download and overlay on a background map.

That done I could download a set of school boundaries (they do exist here – yay) and overlay those and workout the intersections between the two. Does the school boundary overlap a regeneration area? Yes? If so, it is on our target list to help.

Incidentally what has happened to the ACC online mapping portal?  Not only does it not appear in any search results either, but all of the maps except the council boundary appear to have vanished, and there used to be dozens of them!

Lack of clarity helps no-one

A failure to publish information and data helps no-one. How can anyone know if their child’s school is in a regeneration area. How can a community group know if they are entitled to additional funding.

Without accurate boundary maps – and better still data – how can we match activities to physical areas (be they regeneration areas, wards, neighbourhoods, or catchment areas)?

How can we analyse investment, spending, attainment, street cleanliness, crime, poverty, number of planning applications, house values, RTAs per area if we can’t get the data?

For us this is a problem, but for the kids in the schools this is another opportunity denied.

Just as we highlighted in our previous post on recycling, the lack of open data is not an abstract problem. It deprives people of data and information and stifles opportunities for innovation. Our charity, and our many volunteers at events can do clever stuff with the data – build new services, apps, websites, and act as data intermediaries to help with data literacy.

Until there is a commitment nationally (and at a city level) to open data by default we will continue to highlight this as a failing by government.

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The header image for this page is for a map of secondary school boundaries from ACC Open Data, on an Open Street Map background.

 

Scotland’s Open Data, February 2019. An update.

Note: this blog post first appeared on codethecity.co.uk in February 2019 and has been archived here with a redirect from the original URL.

Scotland’s provision of open data may be slowly improving, but it is a long way behind the rest of the UK. In my most recent trawl through websites and portals I found a few minor improvements, which are positive, but progress is too slow; some data providers are slipping backwards; and most others are still ignoring the issue altogether. Now is the time for the Scottish Government to act to fix this drag on the Scottish economy and society, and stop inhibiting innovation.

Latest review

Over the last week, I have conducted yet another trawl of Scottish Open Data websites and portals. I keep this updated on this Github Repo.  I’ve carried out this research without assistance, in my own time. The review could be more comprehensive, frequent and robust if I was supported to do it.

This work builds on previous pieces of research I’ve carried out and articles that I have written. Recently, I’ve created an index of those blog posts here as much for my own convenience of finding and linking to them as anything.

During this latest trawl, I’ve tried to better capture the wide spread of Scottish Government departments, agencies, non-departmental public bodies, health boards, local authorities, health and social care partnerships and academic institutions;  and assess each sector using quite conservative measures.

The output of that, as we will see below, does not paint a good picture of Scotland’s performance, despite a few very good examples of people doing good work despite a clear policy gap.

Let us look at this sector by sector, following the list of findings here.

Local Authorities

Of Scotland’s 32 local authorities, only 19 produce open data of any kind.  This group uses a mixture of open data portals (10), web landing pages (7) and GIS systems (2). This leaves 13 who produce no open data whatsoever.

Those 19 councils (ignoring the other 13) produce a total of 731 datasets, giving a mean for the group of 38 and a median of 17 datasets. This total is only six more than I found three months ago, despite Dumfries and Galloway launching a new portal with 33 datasets !

Also, stagnation is a real issue. For example, it is worth noting once again that while Edinburgh produces an impressive 234 open data sets, only five of those have been updated in the last six months, and 228 of them date from 2014-2017.  While there is a value in retaining historic data ( allowing comparisons, trends etc to be analysed), the value of data which is not being updated diminishes rapidly.

When I ran the OD programme for Aberdeen City Council (which, like all Scottish councils, is a unitary authority), based on some back-of-the-envelope calculations I reckoned that we could reasonably expect to have about 250 data sets. So, if each of the 32 did the same, as we would expect, then we’d have 8,000 datasets from local authorities alone. This puts the 731 current figure into perspective.

Scottish Government

So far, I have found the following open data being produced:

  • 248 datasets on the excellent, and expanding, Statistics.Gov.Scot portal  covering a number of departments, agencies and NDPBs,
  • 54 datasets on the Scottish Natural Heritage portal, 53 of which are explicitly covered by OGL and one marked “free to use data.”
  • At least 43 OGL-licensed mapping layers on the Marine Scotland portal
  • Just four geospatial datasets for download on the Spatial Hub
  • Six Linked open data sets, licensed under OGL, on the SEPA site.
  • Great interactive mapping of the Scottish Indices of Multiple Deprivation, for which the source Data is included above on the Statistics Portal mentioned above.

That makes a total of 353 datasets. I’ve not tracked these number previously, so can’t say if they are rising, but there certainly appears to be good progress and some good quality work going on to make Scottish Government data available openly. This includes the four newly-opened sets of boundary data by the Spatial Hub, out of 33 data sets.

However, if we look at the breadth of agencies etc that comprises the Scottish Government, it is clear that there are many gaps. In addition to the parent body of the Scottish Government there are a further 33 Directorates, 9 Agencies, and 92 Non-Departmental Public Bodies. That’s a total of 135 business units.

Let’s assume that they could each produce a conservative 80 data sets, and it is arguable that that should be considerably higher, then we’d expect 10,800 datasets to be released. Suddenly, 353 doesn’t seem that great.

Health

Scotland’s Health service is composed, in addition to the parent NHS Scotland body, of 14 Health Boards and 30 joint Health and Social Care Partnerships. That gives a total of 45 bodies.

Again, taking the same modest yardstick, of 80 open data sets for each, we would expect to see 3,600 data sets released.

What I found was 26 data sets on the new NHS Scotland open data portal. This is a great, high-quality resource, which I know from conversations with those behind it has great commitment to adding to its range of data provided.

However, given our yardstick above, we are still 3,574 data sets short on Scottish Health data.

Higher and Further education

Scotland’s HE / FE landscape comprises of 35 Universities and colleges.

Glasgow and Edinburgh Universities each have an open data publication mechanism for data arising out of a business operation, which contain interesting and useful data.

Despite that, there is no operational, statistical or other open data being created by any universities or colleges that I could identify. Again, using the same measure as above, that produces a deficit of (80 x 35) or 2,800 datasets.

Supply versus expectation

If we accept for the moment that the approximate number of data sets that we might expect in the Scottish public sector is as set out above, and that the current provision is, or is close to, what I have found in this trawl, then what is the over all picture?

Sector Published Expected Defecit
Local Government 731 8000 7,269
Scottish Government 353 10,800 10,447
Health 26 3,600 3,574
FE / HE 0 2,800 2,800
Totals 1,110 25,200 24,090

Table 1: Supply versus expectation of Scottish public sector Open Data

As we can see from the table above, it appears that the Scottish public sector is currently publishing 1,110 of 24,090 expected open data sets. This is just 4.6%. So, by those calculations, more than 95% of data that we might reasonably expect to see published as Open Data is not being released.

Scotland is behind the UK generally

Whether you agree with the exact figures or not, and I am open to challenge and discussion, it is clear that we are failing to produce the data that is badly needed to stimulate innovation and deliver the economic and social benefits that we expected when set out to deliver open data for Scotland.

I’ve long argued that in terms of the UK’s performance in Open Data league tables, such as the Open Data Barometer, Scotland is a drag on the UK’s performance, with Scotland’s meagre output falling well short of the rest of the UK’s Open Data.  In addition to existing approaches, we should see Scotland’s OD assessed separately, using the same methodology, in order to be able compare Scotland with the UK as a whole. That would allow us to measure Scotland’s performance on a like-for-like basis, identify shortfalls and target remedial action where needed.

Policy underpinning

I have argued previously that a significant issue which stops the Scottish public sector getting behind open data is the lack of public policy to make it happen, as well as an ignorance, or denial, of the potential economic and social benefits that it would bring. While I was part of the group who wrote the Scottish Government’s 2015 Open Data Strategy, it was, in its final form, toothless and not underpinned by policy.

We now have an Open Government Action Plan for Scotland 2018-2020 (PDF). This is  great step forward but unfortunately it is almost entirely silent on Open Data, as pointed out in my response to the draft in November 2018.

Even when Open Data does make an appearance, on page 19, it is relation to broader topic rather than forming actions on its own merits.  The position is similar in the plan’s detailed commitments.  This is not to denigrate the work that has gone into these, and the early positive engagement between Scottish Government and civic groups, but this is a huge missed opportunity – and we should not have to wait until 2020 to rectify it.

At this point, it is worth contrasting this with the Welsh Government’s Open Government plan 2016-2018 which was reviewed recently (PDF). In that plan, Open Data was the entire focus of the first two sections, and covered pages 4 to 6 of the plan. This was no afterthought: it was a significant driver and a central plank of their open government plan.

The broader community

Scotland still lacks a developed Open Data community. This will come in time as data is made more widely available, is more usable and useful – and also through the engagement with the Open Government process  – but we all need to work to develop that and accelerate the process. I set out suggestions for this in a previous post.

There are significant opportunities to grow the use of open data through the opening of private sector and community-generated and -curated data.

The universities and colleges in Scotland should be adopting open data in their curriculum, raising awareness among students, creating entrepreneurs who can establish businesses on the back of open data.

Schools should be using open data to get their classes involved: using it to explain their environment, climate, and transport system; to understand local demographics, the distribution of local government spending, or comparative attainment of schools.

Government should be  developing the curriculum to use open data to foster a better understanding of data and how it underpins modern society.

There are some positive things going on: the roadshows that the Scottish Government are doing, as well as other Data Fest Fringe events; the regular data hack weekends we’ve been doing in Aberdeen under the Code The City banner; and the major long-term project to build and deploy community-hosted air quality monitoring sensors which provide open data for the local community. These need to become the norm – and to be happening across the country.

Organisations such as The Data Lab, Censis and other innovation centres have a great opportunity here to advance their work, whether in education, community building or fostering innovation, and to support this to achieve their organisational missions.

Bringing people together

Having earlier created a Twitter account for a nascent Scottish Open Data Action Group (@Soda_group), I have reconsidered that. Instead of an action group to pressure, shame or coerce the Scottish Government into action, what we need is a common group that has the Scottish Government onside – and everyone works together. So I have renamed it @opendata_sco. It already has 179 followers and I hope that we can grow that quickly, and use that to generate more interest and engagement.

I have also launched a new open Slack channel for Open Data Scotland, so that a community can better communicate with one another.

Please join, using this form.

As I have said previously this isn’t a them-and-us, supply-and-demand relationship. We’re all in it together, and the better we collaborate as a community the better, and quicker, society as a whole benefits from it.

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Header photo by Andrew Amistad on Unsplash

Boundaries, not barriers

Note: This blogpost first appeared on codethecity.co.uk in January 2019 and has been archived here with a redirect from the original URL. 

I wrote some recent articles about the state of open data in Scotland. Those highlighted the poor current provision and set out some thoughts on how to improve the situation. This post is about a concrete example of the impact of government doing things poorly.

Ennui: a great spur to experimentation

As the Christmas ticked by I started to get restless. Rather than watch a third rerun of Elf, I decided I wanted to practice some new skills in mapping data: specifically how to make Choropleth Maps. Rather than slavishly follow some online tutorials and show unemployment per US state, I thought it would be more interesting to plot some data for Scotland’s 32 local authorities.

Where to get the council boundaries?

If you search Google for “boundary data Scottish Local Authorities”  you will be taken to this page on the data.gov.uk website. It is titled “Scottish Local Authority Areas”  and the description explains the background to local government boundaries in Scotland. The publisher of the data is the Scottish Government Spatial Data Infrastructure (SDI). Had I started on their home page, which is far from user-friendly, and filtered and searched, I would have eventually been taken back to the page on the data.gov.uk data portal.

The latter page offers a link to “Download via OS OpenData” which sounds encouraging.

Download via OS Open Data
Download via OS Open Data

This takes you to a page headed, alarmingly, “Order OS Open Data.” After some lengthy text (which warns that DVDs will take about 28 days to arrive but that downloads will normally arrive within an hour), there then follows a list of fifteen data sets to choose. The Boundary Line option looked most appropriate after reading descriptions.

This was described as being in a proprietary ERSI shapefile format, and being 754Mb of files, with another version in the also proprietary Mapinfo format. Importantly, there was no option for downloading data for Scotland only, which I wanted. In order to download it, I had to give some minimal details, and complete a captcha. On completion, I got the message, “Your email containing download links may take up to 2 hours to arrive.”

There was a very welcome message at the foot of the page: “OS OpenData products are free under the Open Government Licence.” This linked not to the usual National Archives definition, but to a page on the OS site itself with some extra, but non-onerous reminders.

Once the link arrived (actually within a few minutes) I then clicked to download the data as a Zip file. Thankfully, I have a reasonably fast connection, and within a few minutes I received and unzipped twelve sets of 4 files each, which now took up 1.13GB on my hard drive.

Partial directory listing of downloaded files
Partial directory listing of downloaded files

Two sets of files looked relevant: scotland_and_wales_region.shp and scotland_and_wales_const_region.shp. I couldn’t work out what the differences were in these, and it wasn’t clear why Wales data is also bundled with Scotland – but these looked useful.

Wrong data in the wrong format

My first challenge was that I didn’t want Shapefiles, but these were the only thing on offer, it appeared. The tutorials I was going to follow and adapt used a library called Folium, which called for data as GeoJson, which is a neutral, lightweight and human readable file format.

I needed to find a way to check the contents of the Shapefiles: were they even the ones I wanted? If so, then perhaps I could convert them in some way.

To check the shapefile contents, I settled on a library called GeoPandas. One after the other I loaded scotland_and_wales_region.shp and scotland_and_wales_const_region.shp. After viewing the data in tabular form, I could see that these are not what I was looking for.

So, I searched again on the Scottish Spatial Infrastructure and found this page. It has a Download link at the top right. I must have missed that.

SSI Download Link
SSI Download Link

But when you click on Download it  turns out to be a download of the metadata associated with the data, not the data files. Clicking Download link via OS Open Data, further down page, takes you back to the very same link, above.

I did further searching. It appeared that the Scottish Local Government Boundary Commission offered data for wards within councils but not the councils’ own boundaries themselves. For admin boundaries, there were links to OS’ Boundary Line site where I was confronted by same choices as earlier.

Eventually, through frustration I started to check the others of the twelve previously-downloaded Boundary Line data sets and found there was a shape file called “district_borough_unitary_region.shp” On inspection in GeoPandas it appeared that this was what I needed – despite Scottish Local Authorities being neither districts nor boroughs – except that it contained all local authority boundaries for the UK – some 380 (not just the 32 that I needed).

Converting the data

Having downloaded the data I then had to find a way to convert it from Shapefile to Geojson (adapting some code I had discovered on StackOverflow) then subset the data to throw away almost 350 of the 380 boundaries. This was a two stage process: use a conversion script to read in Shapefiles, process and spit out Geojson; write some code to read in the Geojson, covert it to a python dictionary, match elements against a list of Scottish LAs, then write the subset of boundaries back out as a geojson text file.

Code to convert shapefiles to geojson
Code to convert shapefiles to geojson

Using the Geojson to create a choropleth map

I’ll spare the details here, but I then spent many, many hours trying to get the Geojson which I had generated to work with the Folium library. Eventually it dawned on me that while the converted Geojson looked ok, in fact it was not correct. The conversion routine was not producing the correct Geojson.

Another source

Having returned to this about 10 days after my first attempts, and done more hunting around (surely someone else had tried to use Scottish LAs as geojson!) I discovered that Martin Crowley had republished on Github boundaries for UK Administrations as Geojson. This was something that had intended to do for myself later, once I had working conversions, since the OGL licence permits republishing with accreditation.

Had I had access to these two weeks ago, I could have used them. With the Scottish data downloaded as Geojson, producing a simple choropleth map as a test took less than ten minutes!

Choropleth map of Scottish Local Authorities
Choropleth map of Scottish Local Authorities

While there is some tidying to do on the scale of the key, and the shading, the general principle works very well. I will share the code for this in a future post.

Some questions

There is something decidedly user-unfriendly about the SDI approach which is reflective of the Scottish public sector at large when it comes to open data. This raises some specific, and some general questions.

  1. Why can’t the Scottish Government’s SDI team publish data themselves, as the OGL facilitates, rather than have a reliance on OS publishing?
  2. Why are boundary data, and by the looks of it other geographic data, published as ESRI GIS shapefiles or Mapinfo formats rather than the generally more-useable, and much-smaller, GeoJson format?
  3. Why can’t we have Scottish (and English, and Welsh) authority boundaries as individual downloads, rather than bundled as UK-level data, forcing the developer to download unnecessary files? I ended up with 1.13GB (and 48 files) of data instead of a single 8.1MB Scottish geojson file.
  4. What engagement with the wider data science / open community have SDI team done to establish how their data could be useful, useable and used?
  5. How do we, as the broader Open Data community share or signpost resources? Is it all down to government? Should we actively and routinely push things to Google Dataset Search? Had there been a place for me to look, then I would have found the GitHub repo of council boundaries in minutes, and been done in time to see the second half of Elf!

And finally

I am always up for a conversation about how we make open data work as it should in Scotland. If you want to make the right things happen, and need advice, or guidance, for your organisation, business or community, then we can help you. Please get in touch. You can find me here or here or fill in this contact form and we will respond promptly.