Tag Archives: Finland

A Finnish alien

On 17th April 1929, Aimo August Sonkkila, brother of my late grandpa, left Finland to London. He was 30 years old, son of a farmer in the then rural Laitila municipality in SW Finland.

In London, Aimo embarked S/S Orvieto. His target destination was Brisbane, Australia.

The same year, 589 males in his age emigrated. 11 of them were from the countryside of the same province, Turun ja Porin lääni.

© Gordy

The first stop was Gibraltar. Then, via Toulon and Neapel, over the Mediterranean Sea to Port Said, Egypt. From there via Suez Canal to Colombo, Sri Lanka. Finally, on the horizon, the West coast of Australia, Fremantle! But the trip was not over yet. Following the Australian coastline, Orvieto visited Adelaide and Melbourne before reaching Brisbane.

I haven’t found the date of the departure, so Orvieto’s exact travel time is unknown. Unlike the newspaper archive provided by National Library of Australia from where I found the route, the British Newspaper Archive is subscription-based. An unfortunate show-stopper for a random visitor like me, although I can understand the monetizing idea.

Anyway, there are hints that the voyage lasted several weeks, which is what you would expect, really. If we trust the computations of Wolfram Alpha, the travel time would’ve been around two weeks, had Orvieto managed 25 knots. Orvieto’s speed, however, was only 18 knots. Yet the globe-shaped map that Wolfram Alpha serves, wakes suspicions. Maybe they use a straight line of distance? In any case, given the fair number of waypoints on route, let us imagine a rough travelling time of one month.

As it happens, Orvieto’s voyage became one of its last ones. The ship was taken out of business in 1930.

Aimo travelled in the 3rd class with roughly 550 other passengers, whereas the 1st class only occupied 75. Among these lucky ones were few celebrities and other prominent figures, featured by The West Australian the day after Orvieto’s arrival to the Australian continent. Onboard was also mail and cargo.

On 28th May, Orvieto docked Fremantle. From the Incoming passenger list, on row 692, we find Aimo. A search by Sonkkila hits 0 because the name is mistyped as M. Sonkkilla. A non-English person, misspellings were to follow Aimo the rest of his life. In the scanned bundle of official records of him, Amio comes up just as often as Aimo. Maybe not a big deal. In Australia, with a hint of Italian, that version was perhaps more practical anyway.

Why did Aimo emigrate? We can only guess. Was he adventurous? Driven to believe in juicy stories of easy money, or official promises on steady income? Had someone he knew and emigrated before him, sent assuring letters to homeland, making him decide to follow suite? As a son of a farmer, he had prospects of taking care of the farm after his father. But, he was not the only son – always a problematic situation. Besides, what if farming was not something he looked forward to? Both push and pull may have played a role here.

We know now that 1929 was the year when Great Depression started. Still, it is difficult to judge in what way and how soon, individual lives are affected by economic fluctuations of such a global scale.

Emigration from Finland was by no means a sudden fad. Previously, the obvious target for the majority of people had been the North American continent. The Immigration Act of 1924 drastically changed this. People were still let in, but in much less quantities than earlier. Very much like in Europe at the moment, both the US and Canada had switched to a selective immigration policy.

This sankey diagram tries to visualize where Finns left between 1900 and 1945, aggregated over decades. Data come from Institute of Migration (Emigration 1870-1945). Note that all targets are not mutually exclusive. Between 1900 and 1923, Americas was recorded as one entity, but from then on, as separate countries. In addition, during that same period, statistics from other countries are scarce. [A technical side note: with Firefox, the diagram may appear very small. Chrome and Internet Explorer don’t have this issue.]

Life in Australia proved a challenging endeavour for Aimo, to say the least. The records are fragmented and don’t reveal much, but it is fairly easy to imagine what is in the gaps.

Work as a miner was incredibly tough. Some of it is captured in The Diminishing Sugar-Miners of Mount Isa, Australia by Greg Watson, linked to by Institute of Migration. I wonder if Aimo had any realistic idea beforehand what it was to be like. Yet, with his modest background, he had not much choice once he had arrived.

Then, after 12 laborious years, Second World War.

On 12th April 1942, Aimo is arrested in Townsville. He is still a Finnish citizen, and because Finland is Axis-aligned, he is a member of the enemy. The rest of the year Aimo would stay in an internment camp at Gaythorne (Enoggera), Brisbane. However, on the application by the Mt Isa Mining Company, his employer, he is allowed to work. Between a rock and a hard place is an idiom that must have been coined by Aimo himself.

At some stage, Aimo had married Impi Rapp. That’s basically all I know about her, the name. Few years after WWII, a son is born. His life would become totally different from that of his parents.

Finger print

National Archives of Australia, NAA: BP25/1, SONKKILA A A FINNISH. Digital copy, page 31

R code of the diagram is available here.

Some Europeana AV resources related to Finnish municipalities as RDF triples

In the previous post, I told how I learned to stop being afraid of Europeana and love SPARQL. As a proof, I gathered statistics on how many video resources there are from different Finnish municipalities. Proportionally, taking into account of the number of inhabitants, the #1 video corner in Finland is Saarijärvi. My Finnish readers, please note the EDIT section towards the end of that posting. For some strange reason I first claimed it to be Helsinki. Sorry about that, Saarijärvi.

BTW, did you know that there is a connection between Saarijärvi and Pamela Anderson? I certainly did not.

What is it that is there?

My so-called research problem with Europeana, nicely summarized by Mikko Rinne, was that in most of the cases, the semantic information about the shooting location of the videos was missing. Therefore, I had to query the name of the municipality around several elements like description, title and subject.

The main contributor of Finnish videos in Europeana is KAVA, National Audiovisual Archive of Finland, in cooperation with European Film Gateway. The videos are digitized newsreels from 1943 to 1964, shown at the Elonet site of KAVA. While perusing the site, I noticed that KAVA is currently growdsourcing metadata about Finnish fiction films. This is a wise move. There is only so much resources to put into this kind of work by KAVA itself. Who knows, maybe my exercise is of some help at some stage, although there are strong caveats e.g. due to the clumsy search logic that returns false positives here and there.

There do exists some spatial data too, enriched by Europeana itself I understand. The most interesting metadata element for me was edm:hasMet with the value of GeoNameID of the municipality. The same element is also used for geolocation coordinates, and Europeana offers a neat interactive map interface built upon them.

How can I find out which GeoNameID belongs to which municipality? Luckily, DBpedia has done the job, see e.g. the resource of Saarijärvi and the property list of owl:sameAs.

Some 8% of municipalities lack the ID, but that’s good enough for my purposes. With the list of municipality names, I gathered the IDs by querying the SPARQL endpoint of DBpedia. The names themselves I had downloaded previously from the National Land Survey of Finland via the indispensable R package soRvi. With the IDs at hand, I turned to Europeana again. This time, I was interested in how much geonamed items there were in different categories.


Europeana resources are divided in four media types: image, sound, text and video. Here I visualize the raw numbers in few separate graphs, roughly based on the number of items. Otherwise it would be difficult to see any nuances between municipalities. The R code of stacked bars is adapted from the Louhos Datavaalit examples. Note that what I did not succeed in doing yet was to sort the bars based on the size of item counts; maybe some misunderstanding from my part on how the factor levels are working.

The first thing you notice is that text items outnumber all others. As far as I know, they consist mainly of newspaper articles digitized by the National Library of Finland. This is no news (pun not intended). Of all newspapers published in Finland 1771-1900, the Library has already digitized the most.

In the third graph, one municipality stands out: Rauma. Quite a lot of images, even more texts. Interesting. I was born in rural Laitila, located some 30 km SE from Rauma, so of course I was keen on knowing what kind of material Europeana has got in such quantities from such a familiar spot. FYI, Rauma was given town rights in 1442. This small coastal municipality is known of its wooden Old Town, a Unesco heritage site.

Rauma turned out to be two-headed. It was not just my childhood neighbour, Finnish Rauma, but also Norwegian Rauma, established in 1964 and named after Rauma River. The reason for false hits was that the GeoNameID of both places has been saved in all Rauma instances. By mistake, I guess. Anyway, Finland brings texts and Norway images – which is probably just right, Norway is so much more gorgeous.

Under CONSTRUCTion (couldn’t resist)

After all Europeana SPARQLing, I decided to try the idea that Mikko had thrown in his blog comment: why not offer links to these resources? Yes, there are false hits – be aware of e.g. Ii, Salo, Rautavaara, Vaala and Kolari for reasons that relate to Finnish language and my REGEX FILTER statements – but the majority should be decent.

Although I’ve been practising SPARQL queries for some time now, I am a complete newbie when it comes to linked data modeling, RDF and all that jazz. BTW the SPARQL package, contributed by a friend of mine, Tomi Kauppinen, et al. has worked like a charm. So, I ventured along with the help (again) of Bob DuCharme’s book and blog. It was actually quite exciting to be able to create new RDF triples with the SPARQL CONSTRUCT statement! Then, when I found rrdf which, out of the box, offers functions to store, combine and save triples, I was ready to try. While at it, I decided to gather data about all AV resources, not just video.

Here they are now, my first RDF triples from my very first in-memory triple store, containing data about Europeana Finnish resources featuring image, sound and video media types. The triples are serialized as RDF/XML and Turtle/N3. RDF/XML was done with the rrdf save.rdf function, and conversion to Turtle/N3 was also easy with the Apache Jena command-line tool rdfcat.

Rauma I left un-tripled – although I could have added an IF function to trap it, and then FILTER out all images and texts.

I would be more than happy if you’d like to comment on anything related to this exercise, especially on the CONSTRUCT part!

The R codes of querying DBpedia and drawing bar charts, and CONSTRUCTing RDF triples.