Urban Henges

Yesterday, thanks to Giuseppe Sollazzo’s fantastic newsletter, I discovered a great project on Github: Urban Henges. This is the work of Victoria Crawford. The purpose of the project is to take a map of any town or city and work out which streets align with sunrise each day of the year. It then creates images for each day and compiles them into an animated GIF.

I cloned her repo and after a little tinkering I was able to run it for myself. At present it is a single Jupyter Notebook containing some Python scripts.

If you are looking to run it for yourself I recommend creating a new Anaconda environment, running Python 3.7, and then installing the OSMNX library using

> conda install -c conda-forge osmnx

I chose to make an animation for Aberdeen. I spotted too late that it truncates the city title after 7 characters, something I later changed.

The process took one hour and 20 minutes to complete, even on a fast MacBook Pro with 32Gb RAM as there is a lot of computation.

Here is the Aberdeen animation.

Aberdeen Urban Henge Animation
Aberdeen Urban Henge Animation

Fun, don’t you think!?

Kudos to Victoria for sharing her code on Github, and to Guiseppe for highlighting this, and so many more projects in his regular newsletter. Hopefully Victoria will add an open licence to the Github repo to make it clear that we can repurpose the code.

And don’t forget this is only possible because the main data for the streets network is Open Data from Open Street Map which is entirely contributed and published by a large community of users. Why don’t you help maintain the maps for your area?

Ian

Header image  by Simon Hattinga Verschure on Unsplash

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.

Note: the “Try it” link below has been updated to take  account of subsequent work done to separate Provosts and Lord Provosts into separate categories.

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

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.