Walks (and biking) in Helsinki

Walking is fun. When there is something to look at (or listen to), it is even funnier. Urban tree walks are popular – see e.g. TreeTalk – and in Helsinki there are over 50K planted trees to stand by and admire. Besides, there are roughly 12K park roads to stroll around. In most cases, these two come together. More inclined to man-made stuff like buildings? The register of buildings protected in the plan of the city of Helsinki has nearly 5K rows.

Where to go? One option is first to choose a district.

No matter what you are up to these days, you better have a mobile app for planning, navigation, and information. Thanks to the fairly new shinyMobile library, former desktop-only R Shiny web applications can now easily be modified to mobile-friendly ones. In this app, by district, I added several layers of Helsinki-related data points to a standard OpenStreetMap base map: park roads (in brown), planted trees (opaque green), protected buildings (orange), and City Bike stations (biggish yellow circles). Hint: click a bike station. After a second or two, it shows the number of available bikes.

The default district of the app is Kluuvi, the commercial centre of Helsinki. Fun fact: geographically, this area is a gloe lake (kluuvi), the final stage in the process where a bay in the sea turns into a freshwater lake due to post-glacial rebound. Select another district from the hamburger icon top left.

For some reason, the granularity of Helsinki park roads is amazingly high. To demonstrate this, there is a separate tab for just park roads, with a histogram 🙂

Two years back, my nearest planted tree was an Allegheny service berry. Since last fall, when the first bicycle boulevard of Helsinki took shape, there are new trees that grow ever nearer (I guess). But, they are not in the urban tree database yet, so I don’t know their species, nor their coordinates.

Planted trees by the Kulosaaren puistotie biking boulevard

Almost all of the protected buildings in Helsinki are just that, separate buildings (rakennus). However, there are some intriguing exceptions. One of them is a protected facade. There is only one of those in the database – and it is part of our house!

A protected facade

The R code of the mobile app is here. There is also a desktop version with the respective code.

Classifying images and other housekeeping with Instagram data

The other day I requested my data from Instagram where I have had a public account since late 2012. Within one hour the data was ready for download. In my case, it was roughly 500 MB worth of JPG photos, MP4 videos, and metadata as JSON files.

What was perhaps a small surprise was that the likes data was not reciprocal. I have now detailed information about whose photos I have liked and when but who has liked mine? No record. The little I have looked at the Instagram API, you cannot fetch this information via it either.

Below, as two separate images, is my liking behaviour over the years rendered as a calendar heatmap. The R code is on Github. Just about the only bigger thing I needed to do to the Instagram data was to fiddle with the data types so that I could input dates to the calendar function by Paul Bleicher.

What about my own postings?

My first Instagram years I spent mostly watching, then gradually liking a bit more. It took some time before I started to contribute myself but after that, it’s been a rather steady stream of photos at a rate of 2 per day in average. Actually, somewhat under 2 because the original data doesn’t include zero-image dates.

Anyway, what are the photos about? Time to some elementary ML.

Something I was not aware of is that, following the concept of transfer learning, you can directly use pre-trained models to classify images. The most popular models are available via the Keras Applications API.

I found Transfer Learning in Keras with Computer Vision Models by Jason Brownlee to be a good hands-on introduction to the topic. The model used here for classification, VGG16, is popular for learning purposes I’m told. I also learned that VGG16 isn’t state-of-the-art any longer so I shouldn’t expect it to give anywhere near accurate results except perhaps in some limited classes. However, as it turned out, using the model against my images was an eye-opener, and a funny one at that!

I have a Windows machine and have been using Anaconda a little already so to get all that was still missing from the setup, Installing a Python Based Machine Learning Environment in Windows 10 by Frank Ceballos worked very well. Unlike Frank I prefer Jupyter Notebooks over the Spyder IDE but following his advice I first checked in its console that all packages were installed correctly. However, for coding I open Anaconda Navigator, select the PythonCPU environment I made, and launch Notebook.

In this ~13 MB Notebook I first print out a manually curated selection of some 90 images, their most likely class label according to the model, and its probability. Then, for the sake of statistics I gather all labels, and count the most frequent ones. I didn’t clock but the latter one took at least what felt like half an hour on my Lenovo ThinkPad X1 Carbon (Intel Core i7 CPU 18GB RAM)

As you can see from the individual images, if the photo features an animal and not much else, the model performs surprisingly well. Especially dogs seem to be well-trained (pun not intended) but other common mammals are not far behind. Other canines pose a challenge, see e.g. hare (37.17%) which is a jackal.

Among dog breeds, dalmatians are obviously easy, ditto chows. However, if the whole body is not visible, things get trickier. One notable exception in this respect is the pointer; almost exactly the right breed although you only see the head. At times, the dog hammer makes everything look like a nail, see wire-haired_fox_terrier (26.29%) !

From other animals, some species are universally easy. Chameleons, snails, lions, ibexes, bears, zebras… no other fauna quite resembles them – unless it’s a dog in a zebra suit! The weevil was almost too perfect for this exercise thanks to the solid white background.

Given the accuracy of labeling dogs, it baffled me why the model did not give notice at all to the gray poodle. Why instead an ashcan with 30% probability? Well, there is an ashcan in the lower left corner. A similar case is the moving_van (27.47%) where the by far most prominent object is a big mural of a bear. But, there is a van. What’s happening here? Is the lower left corner a hot spot of some kind?

In the code, by image, I retrieve the highest probability class with

label = label[0][0]

In these two photos, ashcan and van simply get a higher score than the other objects in the image. When I define

label = label[0][1]

the labels are returned as soft-coated_wheaten_terrier (28.72%) and bison (12.02%), respectively. Next highest probabilities that is, and now also with a dog breed. To continue, with

label = label[0][2]

the result is streetcar (7.90%) and Newfoundland (8.34%). In other words, we are not just ascending the probability tree but also moving towards slightly more abstract, or at least not so obvious, classes. Derived, if you wish.

The model cannot be fooled that easily. Parachute (69.60%) shows a piece of textile with two tropical birds, artistically drawn but still with distinct features of real-life bird species. No matter how much you lower the probability of the class, the model is adamant that the image is about texture, not animals: sleeping_bag (11.72%), pillow (8.84%), quilt (4.95%), shower_curtain (0.63%)

It is interesting how VGG16 reveals bits and pieces about its training set.

First of all, avian fauna is mostly American. The bird pic I lol’d the most at was boldly labelled bald_eagle (14.20%) The image shows a European starling up on a pine tree. Goldcrest is taken either a jacamar or hummingbird, depending on the angle, although with a lowish probability in both cases. Likewise, architectural classes tell about a society where the state-of-the-art construction for people to live in is a house, as it is in the US. This explains why local blocks of flats are grimly classed as prisons! What was also intriguing to notice was that images on commercial activities like various merchandise must have been well represented in training data. Otherwise I cannot understand why shoe_shop (84.31%) and butcher_shop (78.01%) were so spot on.

At first sight, on the level of all my 2000+ photographs, the VCC16 model does not seem to perform that good, which was expected. There are only so many clear images of animals. Yes, it is true that water is a frequent element in my shots, but lakeside and breakwater could have been elaborated on a bit more maybe, park_bench and stone_wall too. Yet, at the end of the day, I think that these top four labels do are quite accurate classes (although it’s mostly seaside rather than lakeside). And the rest? Some of them sound a bit odd.

Lines of text in the image is a hard nut to crack. Is it a book, book_jacket, menu, doormat, envelope, packet, or a web page? In my case it’s oftentimes a screenshot from a web page I guess. Besides, how would you classify an abstract and artsy image without any discernible shapes? There is no “right” class for them. That’s why the class can be anything really; maze, window_shade, fountain etc.

Let’s face it, I like to take (and post) obscure pictures. Especially if there is a shortage of dogs, benches, walls and water.

Trees and areas

The city of Helsinki is home to quite a big number of trees. Trees are interesting living organisms, and their sheer existence makes your life better in so many ways. This is how I personally feel anyway.

Thanks to the newly opened Urban tree database of the City of Helsinki we can now look at trees’ whereabouts also digitally. Note that the database is not exhaustive, error-free, nor regularly updated. The coverage is better on trees growing along streets, less so on trees within parks, which I find understandable.

To start with, let’s take a sample of 5000 (10%) and plot them as points on top of the Helsinki district map.

Here we can start to get a general understanding of where Helsinki is as its greenest at street level. The southernmost green points fall on the island of Suomenlinna so imagine that you see the shoreline somewhere above those.

Where are the tree hotspots? A density map reveals that they are not far from the city centre; around Töölö bay and Hietaniemi cemetery, and in Kaivopuisto by the sea.

I was surprised by the number of different tree families, 115! Yet, the top 8 families are far more common than the rest: linden (Tilia), maple (Acer), birch (Betula), elm (Ulmus), rowan (Sorbus), oak (Quercus), pine (Pinia), and alder (Alnus).

Rowan trees are the most widespread ones whereas pines are very concentrated to SW.

How about the age of the trees? Data does not tell about the age very much at all, but a good proxy is the size.

Smallish trees seem to the most widespread. Their density is relatively high especially in the city centre which sort of sounds right; in recent years, Helsinki has been quite busy in rejuvenating its tree population. Note that only about 3% of the trees are missing the size info, i.e. the size is given as a NA.

While at it, I also checked which tree grows closest to where I live, and which one the most far.

Turns out that the nearest one is 150 m from my home door, on the bank of the Itäväylä highway. An Amelanchier laevis, planted last year.

The most remote one on the other hand was planted earlier this year on the southern shore of the Kerava River, 11 km to the North from here. The family? Thuja, my namesake 🙂 More exactly, a Thuja plicata, a Western red cedar.

These Thujas can become tall if all goes well. The Finnish name Jättituija (“giant Tuija”) reflects this fact. In North America where the species is native, its wood has been frequently used in e.g. Haida totem poles, few of which I only this week had the chance to see in British Museum, London.

With almost 50K items in the dataset, there is really no easy and practical way to show information from every tree at the same time. Instead, I decided to combine data with another open dataset from Helsinki, Valuable environments in the public areas of the city of Helsinki. This interactive web app shows, which trees are located inside these areas. The bigger the tree (diameter on the chest level), the bigger the circle that points to its location. Be aware that all text in tooltips and pop-up boxes is in Finnish.

R code is available here.

M/S Haaga

M/S Haaga

On a sunny +1 C mid-winter Saturday, when the Helsinki metro was having a two-day maintenance break, I walked via Mustikkamaa to Helsinki city center. While passing the Sompasaari area with its 45 year old Hanasaari power plant, I noticed that there was a cargo ship moored at its general berth (I learned new English words while writing this piece), its cranes slowly transporting coal from the ship on land.

It was M/S Haaga, owned by ELS Shipping, a subsidiary of ASPO plc.

ASPO is a four-legged conglomerate with an interesting selection of branches: chemicals, shipping, electronic solutions especially for mobile work, and bakery technologies. The acronym ASPO comes from the Finnish Asunto-osakeyhtiöitten Polttoaine Osuuskunta (Energy coop of housing cooperatives). The company started in the late 1920’s by importing petroleum coke.

The past and present fleet of ELS Shipping carries names that sound familiar to Helsinki city dwellers; they are named after the districts of the city.

M/S Haaga is a 25,600 dwt self-discharging, ice classed, LNG-powered bulk carrier, launched in Nanjing, China on 20th October 2018. The port of registry is Madeira, Portugal, mainly due to its tax benefits I understand.

From China, M/S Haaga arrived to the Baltic Sea through the Northeast Passage (article in Finnish), after having first sailed to Japan for a cargo of raw materials.

Based on information at Marine Traffic and Vessel Tracker, M/S Haaga has been criss-crossing the Baltic region since mid-November 2018. To fill its tanks with liquefied natural gas (LNG), the ship sails to Oxelösund, Sweden. For coal, M/S Haaga heads South-East.

Ust-Luga is an important coal terminal in Russia. According to Wikipedia, the target is high:

As of 2005, the population of Ust-Luga does not exceed 2,000, but the port administration expects it to grow to 34,000 by 2025. This would make Ust-Luga the first new town built in Russia after the fall of the Soviet Union.

Whether M/S Haaga is transporting coal mined in Russia, Poland or some other European country, I don’t know, but Russia is a good candidate. In his book Coal Energy Systems, Bruce G. Miller writes:

Russia’s main coal basins contain coals ranging from Carboniferous to Jurassic in age. Most hard coal reserves are in numerous coalfields in European and central Asian Russia, particularly in the Kuznetsk and Pechora basins and the Russian sector of the Dontesk basin.

The Helsinki area still depends a lot on coal, especially in district heating. However, the aim is to become carbon neutral by 2035.

Energy sources

The dashed line denotes the fact that there are no data points between 1990 and 2000. R code.

Our 4-family housing company is one of the customers of district heating of HELEN, the energy company owned by the city of Helsinki.

On average, we consume 180 MWh for heating per year. This means a roughly 13,000 Euro bill. As heat of combustion, hard coal is about 30 MJ/kg. In MWh, 1 tonne of coal is ~8 MWh. Our yearly 180 MWh is thus 180 / 8 = ~22 tonnes. From the 25,600 deadweight tonnage (dwt) of M/S Haaga, that’s less than a 1/1000.

Where is Haaga now? After a one week stay in Helsinki, it is now back to Russia, this time in St. Petersburg.

Death in Finland

In early December, Kieran Healy posted tweets where he presented quite spectacular heatmaps of mortality rates, based on data from The Human Mortality database.

How would the respective Finnish heatmaps look like, I wondered.

Luckily, in PX-WEB API Interface we have a helpful R library to fetch data from Statistics Finland open data API. With the interactive_pxweb() function you navigate in the data hierarchy, select what you want, and then download the data. As as sidekick, for later use, you get a ready-made query. A nice and easy solution.

However, year-by-year data via the API starts only from 1980, so for previous statistics I needed to go elsewhere.

Statistics Finland have digitized all their legacy reports on population shift that include births, marriages, deaths etc. These are available as PDF files at the Doria repository of the National Library of Finland. Initially, to extract numbers from the PDFs, I had high expectations on the pdftools R library but rapidly fell down into the valley of despair. Either the table structure was just too complicated and I got only headers, or the PDF was a scanned picture. Among the former, you could oftentimes manually copy-paste table columns though. So, a few days in a row I was mostly just tap-tapping numbers to an Excel worksheet. Then it suddenly dawned on me that I never actually checked the Mortality database. Would they perhaps have data from Finland? Of course they do! So I ended up combining data from three sources: Mortality.org, my manually entered file, and API.

When data were ready, the graphs were easy to do thanks to Healy’s example code in R. I even copied his color palette and all.

Below are the final heatmaps, covering mortality statistics since 1878. I deliberately left out older years because before 1878, statistics are not available year-by-year but in five-year age groups.

Female mortality in Finland

Male mortality in Finland

For comparison, here is neighbor Sweden.

Female mortality in Sweden

Male mortality in Sweden

What do the graphs show?

Healy explains it best in the legend of his French mortality poster

Mortality rates are calculated for each age in each year and binned by percentile. The darker the color at any particular point, the more people of that age die in that year. The lighter the color, the more people of that age survive in that year.

Historical trends are visible, such as the rapid decrease in infant mortality rates after World War II, as well as increased life expectancy overall. Specific events show up as vertical streaks in the graph. The death toll due to wars in evident for Males. Pandemics are also visible, most notably the 1918 influenza pandemic and the death toll to Smallpox outbreaks after the Franco-Prussian war of 1870.

Diagonal streaks in the data are visible in some parts of the data. These are artifacts due to the estimation of the mortality rate in some years. Single-year-of-age figures prior to 1900 are calculated from five-year age groups, as no single-year data is available in the original mortality tabulations from which the rates were derived.

World War I lasted from 1914 to 1918. However, the peak in 1918 among young Finnish males in particular was mostly caused by the Finnish Civil War. During the same year–perhaps partly assisted by the WWI–the Spanish Flu influenza pandemic caused havoc in all age groups, as Healy points out too.

Winter War 1939-40 and Continuation War 1941-1944 paint dark columns as well.

Tuberculosis was a deadly national disease in Finland until WWII. Infant mortality began to wither around the same time, and better maternity care started to save lives of young women in labor.

Note the coloring in the age groups above 80. It does not claim that, particularly in the past, the older you got the less likely you were to die. On the contrary. There simply did not exist that many people alive in those age groups (who would then die). While preparing the data, I replaced NA values with zero. Perhaps it would’ve been wiser not to do that and let the graph color these missing values differently from the rest of the graph. The mostly zero valued groups now fall into the first bins, i.e. the lowest percentiles which are given the lightest colors from the viridis palette. Anyway, why was this phenomenon missing from the French graph? I suppose because compared to Finland, France is a big country with more than a tenfold population. A lot of people in all age groups.

The lighter diagonal strands are interesting. They look like sunbeams that drill through the dark clouds of death. Unlike in Healy’s case, these cannot be artifacts or at least not similar ones to his, because numbers are not calculated from age groups but are year-by-year. My initial thought was that they reflect the increased birth rate that tends to occur especially after wars; when population grows, the proportion of deaths diminishes. However, we have no population data here so this cannot be true. Yet, they do seem to have their origin around the war years. Did these baby-boomer cohorts get such a favorable start that it shows throughout the rest of their lives?

Although I did some amount of redundant work while I extracted data manually from digitized reports, I don’t regret it. Following the death statistics year by year I had the opportunity to notice gradual changes in terminology, presentation, and typeface. The arrival of the line printer in 1971! Also, there were other intriguing tabulated data in the reports. For example Causes of accidental and violent deaths that was published between 1921 and 1935 at least. Death in this category of the prosperous Finland of today often occur in extreme leisure activities up in the highest mountains and in the deepest caves of the oceans. In the 1920’s the end could arrive in the shape of the hoof of your domestic horse. Yet some things remain. Finns still drink, drive or dive, and die.

Streets of Helsinki

Helsinki streets on a map

Walking is fun, and there are always new ways to move forward, literally. Some people (not unlike me) have had this silly idea to walk all the streets of Helsinki, in alphabetical order. Johannes Laitila is one of them, and his blog is a good read (in Finnish). Recently Sanna Hellström, the head of Korkeasaari Zoo and former member of Helsinki City Council, mentioned in Twitter that since last fall, she had started to follow in Johannes’ footsteps.

Sanna’s tweet made me think about the size of her plan.

Spatial data of addresses of Helsinki are available from the city’s WFS API, via e.g. the key data site of Helsinki Region Infoshare. Of course addresses are not quite the same thing as streets but will do. Because addresses can refer to almost anything urban, I filtered them with a list of Official street names, the domain name of which tells something about the pragmatism of my home city; the name translates to Plans for cleaning.

The number of unique address names in my filtered data is 3788. The total sum of the geographical distances of individual addresses is 783 km (486 miles). There are some caveats though. Firstly, not all streets are populated by addresses from start to finish. In my home suburb Kulosaari for example, the two longest streets are Kulosaarentie and Kulosaaren puistotie. However, the former starts as a motorway exit road and meets its first address only after a few hundred meters. The latter is equally without addresses a long stretch from both ends. Secondly, distances are not calculated by the street level, so the meters you walk are bound to be more than what the figure says, except in those rare cases where the street forms a straight line.

Anyway, let’s assume a very rough error rate of 20 km to end up to a convenient total length of 800 km. To put that in perspective, Sodankylä, the venue for the legendary annual Midnight Sun Film Festival in Finnish Lapland, is about 800 km North from Helsinki. Given a modest rate of 5 km walking per day, starting about now, I’d reach Sodankylä in time for the next festival. In Helsinki, were I to walk one or two streets every weekend, the project of Walking Them All would be finished in 15 years.

What does Helsinki look like, street-wise?

The opening state of the interactive web app hkistreets that marks the first and last address on every street, shows how the bulk of them is spread along the North-South axis. Helsinki sits on a tip of a peninsula with a slight bending towards right. This North-Eastern area is fairly new. In 2009, a slice of Sipoo was annexed in Helsinki.

Notice the few markers above the sea. Helsinki occupies 315 islands, and from these, a couple have got an address which reveals that there’s something else on the island than just summer cottages, if anything. Rysäkari for example, the most Southern island, is a former military base, and a future tourist attraction (news in Finnish).

Most of the streets of Helsinki can be walked in 5 minutes if you are in a hurry; over 90% are under 500 m (1640 feet). 6% have only one address which means that they are an obscure lot. Either the street do is short or in fact it is some other place of interest (to be cleaned) like the centrally located square Paasikivenaukio 2 with the 40 ton granite statue of the former President of Finland Juho Kusti Paasikivi. 2% are under 1 km, and 0.9% between 1 and 3 km.

Only two streets in Helsinki are longer than 3 km. Mannerheimintie is a giant, almost 14 km, and known to all, whereas Jollaksentie (5 km) in South-East is a less visited suburban stretch. At the end of it, you are close to the last big unbuilt island of Helsinki, Villinki.

Google Street View does not cover all coordinates in Helsinki, as neat as it would be – sometimes because my coordinates are too far from streets – so popup links will often hit a black screen. Technically, I guess I could scrape all targets beforehand and only serve those that have something to look at, but Google TOS might not like it so I’ll let that idea be.

The R source code is available at Github.

Consuming IFTTTed Twitter favs

‪I consider my Twitter favourites to be mainly bookmarks although endorsements happen too. Liking comes from Instagram and Facebook but because my social media communities do not really overlap, I sometimes want to send digital respect in Twitter too. I’ve tried to unlearn the classic liking in Twitter and instead reply or retweet, but old habits – you know.‬

‪There’s little point in bookmarking if you cannot find your bookmarks afterwards. The official Twitter archive that I unzip to my private website few times a year, does not include favourites, which is a pity. The archive looks nice out of the box, and there is a search facility. ‬

‪Since late 2013, I have an active IFTTT rule that writes metadata of the favourited tweet to a Google spreadsheet. IFTTT is a nifty concept but oftentimes there are delays and other hickups. ‬

‪My husband and I lunch together several times a week. Instead of emailing, phoning or IM’ing the usual 5 minutes and I’m there bye message, I had this idea of enhanced automation with IFTTT. Whenever I entered the inner circle around our regular meeting point, IFTTT sent me an email announcing Entered the area. This caused a predefined email rule to trigger which forwarded the message to the receiver. Basta! At first this simple digital helper was fun, but as soon as it didn’t deliver, trust started to erode rapidly.

– Where are you? Didn’t you get the message?
– What message?

Sometimes the email came doubled, sometimes it arrived the next day (or never). After few months I had to deactivate the rule.‬

‪With tweets it’s not so critical. In the beginning I checked few times that the spreadsheet do was populated, but then I let it be. From time to time Google (or IFTTT) opens up a new document but fortunately keeps the original file name, just adds a version number.‬

IFTTT rule

IFTTT rule

‪I appreciate Google’s back office services but don’t often use their user interfaces. Besides, my IFTTT’ed archive does not include the tweet status text, so without some preprocessing the archive is useless anyhow. In theory I could get the text by building calls myself to the Twitter API with the Google Query Language, or become a member of the seemingly happy TAGS community. TAGS is a long-lasting and sensible tool by Martin Hawksey. But what would blogging be without the NIH spirit, so here we go.‬

‪Because I have access to a shinyapps.io instance, I made a searchable tweet interface as an R Shiny web app. For that I needed to‬

  • Install googlesheets and twitterR‬
  • Collect information on my tweet sheets‬, and download all data‬ in them
  • Expand short URLs‬
  • Fetch the Twitter status from the API‬
  • Write a row to the latest sheet to denote where to start next time‬
  • Build the Shiny app‬

The app acts now as a searchable table to my favourite tweets. While at it, I also plotted statistics on timeline.

Here is the app. The R code for gathering all data is in GitHub, likewise the code how I built the app.

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.

Seven Brothers

A movie that I’ve never seen myself but what apparently forms a Christmas Eve tradition in the US, inspired David Robinson to write a thorough and illustrative blog post about its network of characters. Like some commentators have already mentioned, his analysis is particularly interesting e.g. because of the way he parses and processes raw text with R.

I wanted to replicate David’s take. For it, I needed material that’s more familiar.

One of the well known group of characters in Finnish pre-1900 fiction is Seven Brothers by Aleksis Kivi. Published in 1870, the book is already copyright free, and available in plain text from Project Gutenberg.

Brothers form a tight group but how tight, actually? With whom did they speak? What else is there to find with some quick R plotting?

First I needed a list of all characters that say something.

In the text, dialog is marked by uppercase letters with a trailing full stop. Thanks to a compact synopsis I found (in Finnish) I noticed that the A-Z character class wouldn’t suffice in filtering all names.

names <- data_frame(raw = raw) %>%
  filter(grepl('^[A-ZĂ„Ă–-]+\\.', raw)) %>%
  separate(raw, c("speaker","text"), sep = "\\.", extra = "drop") %>%
  group_by(speaker) %>%
  summarize(name = speaker[1]) %>%

Some of the names referred to a group of people (e.g. VELJEKSET) or other non-person (the false positive DAMAGE), so I excluded them. I also found one typo; KERO is not some person but in fact EERO.

This data frame I then wrote to file, added manually some descriptive text in English with help of the above mentioned synopsis, and read in again. Later on in the process, this information would be joined with the rest of the processed data.

Next, with some minor modifications to David’s script, the main parsing process.

What the script does is that it filters all-blank rows; detects rows that mark the beginning of a new chapter (luku in Finnish); keeps a cumulative count of chapters; separates the name of the speaker from the first line of his/her dialog; groups by chapter, and finally summarizes – which I found interesting, because I’d thought that summarize() would be of use only with numerical values.

lines <- data_frame(raw = raw) %>%
  filter(raw != "") %>%
  mutate(is_chap = str_detect(raw, " LUKU"),
         chapter = cumsum(is_chap)) %>%
  filter(!is_chap) %>%
  mutate(raw_speaker = gsub("^([A-ZĂ„Ă–-]+)(\\.)(.*)", "\\1%\\3", raw, perl=TRUE)) %>%
  separate(raw_speaker, c("speaker", "dialogue"), sep = "%", extra = "drop", 
           fill = "left") %>%
  group_by(chapter, line = cumsum(!is.na(speaker))) %>%
  summarize(name = speaker[1], dialogue = str_c(dialogue, collapse = " "))

Inner_join()‘ing lines with names.df by their common variable name, only the relevant rows are kept.

lines <- lines %>%
  inner_join(names.df) %>%
  mutate(character = paste0(name, " (", type, ")"))

How much do the brothers speak across the chapters?

by_name_chap <- lines %>%
  count(chapter, character)

ggplot(by_name_chap, aes(x=character, y=dialogs, fill=character))+
  geom_bar(stat = "identity") +
  facet_grid(. ~ chapter) +
  coord_flip() +

From the facetted bar chart we’ll notice that Juhani, the oldest brother, is also the most talkative one. He remains silent only in the very last chapter, the epilogue.

Facetted bar chart

Whenever we have a matrix, it’s worth trying to cluster it.

– says David, so let’s follow his advice.

Cluster dendrogram

Brothers are mostly together, which is not a surprise. Lauri does not talk much, and Timo has got his own chapter. These facts might have influenced to their having a separate branch each. The few other people that have a say in the book, form their own hierarchies.

Next David shows how this ordered tree can be transformed to a scatterplot. What a neat way to make a timeline! Because of the great number of different permutations of pairs, his example movie is visually more interesting in this respect than Seven Brothers. Still, even here the plot acts nicely as a snapshot of the storyline.

Scatterplot as a timeline

The network graph of brothers and their allies does not reveal anything overly exciting. This part of the analysis I took merely as an exercise in plotting the network with the new geomnet R package.

# Adjacency matrix
cooccur <- name_chap_matrix %*% t(name_chap_matrix)


# Define network from the matrix, plus few attributes
g <- graph.adjacency(cooccur, weighted = TRUE, mode = "undirected", diag = FALSE)
V(g)$lec_community <- as.character(leading.eigenvector.community(g)$membership)
V(g)$centrality <- igraph::betweenness(g, directed = F)
E(g)$weight <- runif(ecount(g))
V(g)$Label <- V(g)$name

# Plot network 

# From the igraph object, two dataframes: vertices and edges, respectively
gV <- get.data.frame(g, what=c("vertices"))
gE <- get.data.frame(g, what=c("edges"))

# Merge edges and vertices
gnet <- merge(
  gE, gV,
  by.x = "from", by.y = "Label", all = TRUE

# Add a new variable, a pretty-print variant of names
gnet$shortname <- sapply(gnet$name, function(x) {
  n <- strsplit(x, " \\(")[[1]][1]
  nwords <- strsplit(n, "\\-")[[1]]
  paste0(substring(nwords, 1, 1),
         tolower(substring(nwords, 2)),
         collapse = "-")

# Colour palette from Wes Anderson's movie Castell Cavalcanti
# https://github.com/karthik/wesanderson/blob/master/R/colors.R
wesanderson.cavalcanti <- c("#D8B70A", "#02401B", "#A2A475", "#81A88D", "#972D15")

p <- ggplot(data = gnet,
            aes(from_id = from, to_id = to)) +
    ecolour = "lightyellow", # edge colour
      colour = lec_community, 
      group = lec_community,
      fontsize = 6,
      linewidth = weight * 10 / 5 + 0.2,
      size = centrality,
      label = shortname
    show.legend = F,
    vjust = -0.75, alpha = 0.4,
    layout = 'fruchtermanreingold'

p + theme_net() +
  theme(panel.background = element_rect(fill = "gray90"),
        plot.margin = unit(c(1, 1, 1, 1), "lines")) +
  scale_color_manual(values = wesanderson.cavalcanti[1:length(unique(gnet$lec_community))]) +
  guides(linetype = FALSE)

The community detection algorithm of igraph found four communities. In the network graph, these are shown with different colours. Most of the characters in Seven Brothers belong to the same community, but there are few loners.

The size of the node tells about the centrality of the person. Timo seems to be influential, probably because he is the only one from the brothers that shares a chapter with his wife and maid.

The thicker the edge, i.e. the line connecting two nodes, the more weight there is. I assume that here weight is simply a measure of co-appearance.

Network graph

Discussion on diabetes

A tweet by Peter Grabitz got my attention the other day.


This is worth of a brief investigation. It’s seldom you start an altmetrics project from a topic.

One obvious choice for getting article ID’s is either Web of Science or Scopus, but to go down that path you obviously need to have access to them to start with. Another solution is to query the PubMed API for a list of PMID’s.

Thanks to the helpful posting Hacking on the Pubmed API by Fred Trotter, you are led to the PubMed Advanced Search page. There, you can define your search with a MeSH topic, and filter articles by the publication year.

PubMed advanced search

PubMed knows of 4890 articles on diabetes mellitus, published this year.

As Fred explains, by URLencoding this Search details string and joining it to the base URL, you are ready to approach the API.

If you are familiar with R, here is one solution. From 4890 articles, Altmetric had metrics on 505, based on PMID. Note that there are probably also mentions that use the DOI.

The Altmetric result dataset is on Figshare.