QGIS Exercise 9: Into the nature, but where?

Analysis based on the maps

What are important areas for certain species? Viikki arboretum (shown on map on the left) is highlighted as an area of special plant diversity. The arboretum features lots of trees that aren’t native to Finland. The Botanical gardens of Kaisaniemi and Kumpula are examples of areas with an exceptional variety of plants. Otherwise, the areas with largest biodiversity are concentrated on eastern and southeastern districts of Helsinki, where the largest unbuilt areas are situated.

Coastal areas of Helsinki and towards the southern borders of Vantaa are highlighted in the summary layers? Also on the islands surrounding the city, which are in many cases preserved land. This could be because it has less development. Areas scoring zero for both biodiversity and plants in general are along the highways.

The plant and biodiversity summary layers are highly similar and overlap in all of the areas. The nature preserve of Vanhankaupunginlahti stands out as an area of particularly high plant diversity. Total biodiversity in that area is high, too, but similar to the wooded areas in the east.

Many islands off the coast of Helsinki seem to have similar environments rich in biodiversity. This would make sense, since the coastal type of nature of those islands isn’t affected by built-up areas nearby. The fact that Suomenlinna islands with landscape heavily influenced by man have poorer biodiversity would support this argument.  

In general, built-up areas with high population density and lots of pavement have less biodiversity, and large wooded areas relatively untouched by humans have richer biodiversity. In between, the low-density suburbs are shown with a pale green color, because of gardens and small forests dotting the area. The maps suggest that the amount of paved roads and rooftops would correlate with the biodiversity score of the area.

Biodiversity matrix layer also features the same highlighted layers as the other two maps. The highest areas located mostly in unbuilt for both plants and biodiversity maps are located near Sipoonkorpi National Park, the north-eastern side of Helsinki on the borders of Sipoo. In addition to the islands in Helsinki, which have almost have high diversity scores along all their land’s surface.

The city of Helsinki might face problems regarding nature conservation in that there are not many areas to preserve that are close to the center. That could be an issue when preserving those areas, especially if there is more urban infill that is going to happen in the center.  

The biodiversity matrix layer is a general overview of an complicated subject. It is therefore dangerous to draw too many conclusions based solely on this presentation. The map handily shows the areas with the highest conservation ranks, but tells nothing about the specific nature types of those regions.

QGIS Exercise 8: Preparing our own data into webmaps

Map 3. A 3D-webmap of the highest-scoring areas in the city centre. Screenshot.

Introduction:

Evaluating the quality of public space is a highly subjective matter. It is still something essential to consider in urban planning. Helsinki city centre has wildly different locations regarding urban quality. But how to measure these? For today’s exercise, we, the self-named experts of urban space, collected our own data!

Method:

We used the free EpiCollect5 app developed by Imperial College London for spatial data collection. The teachers had designed a 5-part questionnaire based on Jan Gehl’s framework of assessing urban quality. We answered the questions on scale from 1 to 5, which resulted in over 100 entries from students.

Maps:

Map 1. Urban quality and safety scores in the city centre.
Map 2. Heatmap of the highest scoring areas in the city centre.

Analysis:

First of all, it is to be noted that the data was collected around 10 a.m. in the morning, when people are usually in their offices and classes. This understandably results in less activity in the public spaces. We could’ve received dramatically different results around lunch hour in the university campus area.

For most part, squares, parks and pedestrianized streets received the best scores. There are some notable exceptions, such as a car traffic intersection in the calm Kruununhaka neighborhood. We shouldn’t jump into conclusions using only this small sample of data. Still, it can be said that low numbers of car traffic is a common denominator between areas with high scores.

Traffic itself doesn’t seem to be a crucial factor: the intersection of Keskuskatu and Ateneuminkuja (both pedestrian streets) received very high scores, despite being more crowded than average. Instead, it looks like the number of car traffic might have a role on how welcoming a place is felt.

Low-scoring areas seem to be centered near large roads, where car traffic is heavy. Interestingly, some of the very lowest scores (marked with the color white on the map) are in pedestrianized streets of Kluuvikatu and Keskuskatu. The renovation work going on with nearby buildings there might have something to do with the result. Also, the majority of maintenance traffic on pedestrianized streets takes place during the morning, which might also affect the results.

High values are clustered in quieter areas, where car traffic is limited. It can be assumed that heavy traffic makes a place feel more unwelcoming for pedestrians. The high safety score also seems to correlate with a high total score, because the highest scores are clustered close to each other. This might be, because safety is considered a large part of the quality of a place. Lowest scores don’t seem to cluster in our map (except maybe Rautatientori Square, where bus traffic is constant).

One result that can be read on the map is that the results aren’t to be overly generalized. While it’s clear that parks enjoy higher urban quality scores, the results are mixed with pedestrian streets. For example, there is one bad score in Keskuskatu and three in Kluuvikatu, both of which see otherwise excellent results. The subjective nature of the questions expands the answers’ variety.

For this exercise, we decided to eliminate answers with less than 100-meter location accuracy from the final map. The answer’s locations still ranged largely between 10 and 65 meters, which is unfortunately inaccurate for this kind of research. It is a problem that is difficult to fix, because different mobile phones use different GPS location techniques. Still, if an average location accuracy of under 50 metres could be maintained, the results might be trustworthy enough to compare different city blocks or intersections. The GPS question is always important for a researcher to keep in mind.

QGIS Exercise 7: Analysis on wind power locations survey

In general, residents support the city’s investment in renewable energy and particularly wind power. On the other hand, proposals for wind power farms might face resistance from the local residents. This contradiction is called NIMBY; not in my back yard.  

Purpose and method:

The NIMBY phenomenon has been a hot topic in Helsinki and it’s one of the major challenges in public participation. In this study, Abdulrahman and I used data from a 2015 online map questionnaire about possible wind power locations in Helsinki. For map analysis, we created standard deviational ellipses to visualize agglomerative clusters from point data features. The maps composed by Abdulrahman could reveal possible NIMBY attitudes of Töölö and Vuosaari residents regarding windmills.

For Töölö residents, the preferred locations of windmills seem to be furthest from their own neighbourhood, since the ellipse for the preferred locations goes well into the sea. A notable exception is some residents’ suggestion for wind mills at the very centre of the city. Could these windmills be located on rooftops or parks? With current technology, windmills are still quite large and noisy, which means that these “hyper-urban” windmills probably won’t become reality anytime soon. Preferred locations for Vuosaari residents windmill locations are located outside there living area (Vuosaari). The survey results are widely spread over the three islands west of Vuosaari in addition to Suomenlinna.

Among both Töölö and Vuosaari residents, the unpreferred locations seem to be within or next to their own neighbourhood. People seem to value their home neighbourhood in its present state. Töölö residents have placed clusters of unwanted locations also in nearby Kaivopuisto and Lauttasaari areas. One explanation could be that their number of answers is larger, which results in greater variety as well. Another possibility is that Töölö residents simply feel more strongly for these areas, maybe because they are well connected to their home neighbourhood. Coastal areas and islands in general have the most clusters of unpreferred locations, Suomenlinna first among them. The historical and cultural scenery of that island would be vulnerable to development and seems to be worth preserving for Töölö residents.

The locations of the important places are best examined with the ellipses. In this comparison, the ellipses of unpreferred locations and places of importance clearly overlap, which would mean that there is a connection between these criteria. According to this survey, respondents don’t like to see major changes such as large windmills in their home neighbourhood, but rather place them somewhere else. Important locations for Töölö residents are centered in downtown Helsinki area as a whole, which hints that they might see that area as a continuation of their home neighbourhood.

The ellipses roughly show the vague areas where preferred, unpreferred and personally important locations are. Perhaps more interestingly, they show a similar pattern in the behaviour of both areas’ respondents. The places of importance are closest to their home neighbourhood, since their ellipses are the smallest. The unpreferred locations’ ellipses are only slightly bigger, while the respondents would place the windmills furthest from their home neighbourhoods of Töölö and Vuosaari. Another note is that both of these preferred locations’ ellipses reach in direction of the open sea, where windmills would have the smallest effect on their daily lives in Töölö and Vuosaari.

QGIS Exercise 6: Comparing accessibility between Tikkurila and Jumbo

This week, we studied certain places’ accessibility via different modes of transport. I compared the accessibility of two Vantaa regional centres, Jumbo shopping centre and Tikkurila railway station, by public transport and by car.

Overall, both Jumbo and Tikkurila are relatively well within reach of most parts of Helsinki region. Only the westernmost and southernmost parts of Espoo can be described as far away regardless of the mode of transport.

Observations and analysis – public transport

The public transport map is revealing. Tikkurila is within a 50-minute reach from large swathes of Helsinki and Vantaa. Even parts of Espoo that are connected by rail fall to this category. Indeed, rail connections seem to be the key here. Travelling by rail is fast in Helsinki region and commuter train and metro stations are well served by connection buses, all of which results in large, continuous areas falling inside the 50-minute reach from Tikkurila.

It is a very different story with the Jumbo shopping centre. Unlike Tikkurila, Jumbo is not directly served by rail. The nearest station (Aviapolis) is an unattractive 15-minute walk away. Instead, a shopper relying on public transport should arrive by bus. Buses, however, offer poor transfer connections, making the area from where Jumbo is reachable in under 50 minutes much more shallow and suspicious-looking. 

There is far less difference between the two maps when comparing the 100-minute radius. Both Tikkurila and Jumbo are reachable from almost everywhere in Helsinki region in roughly one and a half hours. 

Observations and analysis – private motoring

The first reaction is that radiuses in the two private motoring maps are more symmetrical than those of public transport. Motorists aren’t required to make compromises as often as public transport users, who are dependent on few rail links. Also, it’s worth noting that travel times when using car are much smaller than they are when using public transport. From practically everywhere, Jumbo and Tikkurila are at most an hour-long drive away. Car is king in Helsinki region.

It is worth noting that for a motorist, Jumbo is especially conveniently located. The shopping centre lies in an intersection of Kehä III ringroad and Tuusulanväylä motorway and both extend the area where Jumbo is under 30-minute drive away even further. Much like rail connections did in case of Tikkurila, the motorway intersection (and further intersections connecting Kehä III and Tuusulanväylä to other motorways) make Jumbo reachable from all directions.

Comparing Tikkurila and Jumbo

Both Tikkurila and Jumbo are regional centres in Vantaa. They rely on very different modes of transport. Tikkurila has better public transport connections than Jumbo, making it perhaps the more central location for a larger number of people. However, Jumbo’s slightly better accessibility by car more than makes up for its lack of adequate rail connections, since car is a much faster  mode of transport, especially over long distances.

As urban centres, both Tikkurila and Jumbo serve Vantaa residents well. No doubt, it can be said that of the two, Tikkurila is the more urban: but let’s not forget that Jumbo is the most popular shopping centre in the country and its accessibility is unbeatable, if you are a motorist. Vantaa can do well with both kind of urban centres.

QGIS Exercise 5: Finding pockets of low income and education in Helsinki area

My map of 250x250m grid cells with concentrations of both low income and low education in Helsinki region.

This week, I had fantastic group partners in Dan and Jaana to study segregation in Helsinki region. We used data from Statistics Finland’s Grid Database 2016 and learned new conditional statement tools available in QGIS.

The areas with the highest share of lowly educated and salaried people seem to spread all over different parts of the Helsinki region. Individual 250 * 250 meter squares can be found in small areas such as Eira, Westend or Kaskisaari which are usually concerned to be some of the wealthiest places in the metropolitan area. The only inhabited areas where there are no or only a few yellow squares are located in the central Helsinki and the eastern coast of Espoo. These spots that seem to include a lot of poverty in the middle of luxurious areas were probably the most surprising results we could find. Taking a closer look at these parts of the grid reveals some weaknesses of our approach. Different squares in the grid do not contain the same amount of people and that is why even a single low-educated person who has hardly any taxable income can make the results seem crooked.  

However we are more interested in bigger clusters of badly-off people. If many of the yellow squares are located next to each other, it tells a different story of actual poverty rather than statistical mistakes. The biggest clusters seem to locate in eastern side of the region stretching from the suburbs of Helsinki such as Vuosaari and Kontula all the way to Hakunila in the Vantaa side. Other concentrations of poverty and low education can be found along the railway tracks in north-eastern Vantaa, north-western Helsinki and even in otherwise well-being Espoo. Many of these areas consist of high-rise buildings and municipal rental flats often built from the 60s onwards. Take the small yellow spot in the southern parts of Laajasalo for example. The small island does not seem to have any poverty outside a tiny area of six 250*250 meter squares. These squares happen to be located right where the biggest city-owned high-rises also are.  

Income and education level data are sufficient variables when studying segregation. In order to achieve more precise results, they should be combined with other datasets. As such they can provide false conclusions as the example of poverty occuring in Eira illustrates. Factors that could be combined with income and education data would be the ones looking at the level of rental housing, health problems, employment, crime, alcohol consumption etc. A study on segregation in the Helsinki area has been made by Mari Vaattovaara and Matti Kortteinen on concentrations of poverty, low education and unemployment. Their results stated that even though there are no large areas of poverty, it can cluster in individual blocks inside neighborhoods. (Beyond Polarisation versus Professionalisation? A Case Study of the Development of the Helsinki Region, Finland. Vaattovaara & Kortteinen 2003.) In that sense refining the analysis would be useful.

Mid-term task: A Tale of Two Haagas

Sometimes it’s no use teaching an old dog new tricks. An apartment building being built in Northern Haaga around 1950.

At first glance, Haaga may seem like a relatively uninteresting lump of suburbia located roughly in the middle of Helsinki metropolitan area. An average place to live for people not I find that impression to be false and that there is much more to Haaga’s identity than it first may seem. There is a dramatic division cutting through today’s Haaga and there have been many more of those in the community’s past.

In my research into Haaga, I will argue that Haaga had its own identity, but lost it since. This is because of the drastic changes the neighborhood has undergone over the last 60 years.

Not our finest hour

The first historical division was among the Swedish and Finnish-speakers in Haaga. This division is intertwined with the division of the bourgeoisie and working-class that marked the first decades of the borough’s history.

As Eetu mentions in his post, the lowest point was reached during the Finnish Civil War, when 46 Reds (consisting of working-class) were executed by Germans in Huopalahti village. However, it seems that the only act of violence during the Civil War in Haaga was committed by Germans. While the Red Guard (the militant wing of the Reds) refrained from violence there. This might have helped the integration of working-class people in Haaga after the war.

Another thing is that the Red Guard consisted of out-of-town guests, who had arrived previously to build fortifications for the Russian Empire. The locals didn’t participate in the atrocities of the Civil War in the same extent. The fact that the Haaga Workers’ Association was able to continue its operation immediately after the war would also defend this idea.

Even though only 43,6 percent of the people were Swedish-speaking (according to census in 1930), they firmly hold the power over the borough council. Most of the Swedish-speakers were bourgeois and the Swedish Workers’ association had only a dozen members.

As was the case in rest of Finland, the working-class in Haaga were quickly integrated into the decision-making after the Civil War. The bourgeoisie did hold some grudge, though, as there was an attempt to blow up the people’s house in 1920. Finally, the Winter War against the Soviets in 1939 united the people across social classes and language borders, both nationally and in Haaga.

Unsuitable housing

During the decade after the Civil War, Haaga became more and more working-class. The newcomers that moved to the borough were young, of working age, lived in cramped conditions and commuted to Helsinki for work.

Haaga borough was originally planned for villas in separate plots. The 1920s saw a construction boom, but the old town plan allowed only building of wooden villas. The Stenius company that owned the land was just happy to do business and didn’t mind the proletarian community growing. In 1919, right after the Civil War, the company donated a plot to the Workers’ Association for the purpose of building a people’s house.

Even though the housing stock consisted of villas, three quarters of the population were renters. More than half of those renters inhabited only one or two rooms. The villas were unsuitable for the residents, relics of times gone by.  I would imagine that the villas weren’t sorely missed when the majority of them were torn down during the 1950s.

The independent Haaga borough’s fate was sealed in 1938, when the city of Helsinki bought the landed property from the Stenius company. The breaking out of the war delayed the process until 1946, when Haaga was incorporated into Helsinki.

Here, our paths diverge

Before the annexation of 1946, Haaga was a wooden house community. From 1950s onwards, Helsinki planned it to be a district of apartment buildings. The old and new residents never quite mixed, as is proven by the fact that to this day residents of Northern Haaga have some separate local organizations from those of Southern Haaga.

Despite being built roughly during the same period, the northern and southern parts of Haaga came to look very different. It makes one wonder, do the two neighborhoods share a name only for land ownership reasons?

Of course, circumstances were very different when the decisions to plan Haaga were made. Helsinki needed new housing badly and planned Northern Haaga according to modern suburban planning ideals. As Anna, Babak and Paula showed us in their post, very few buildings in Haaga predate the 1950s. The wooden villas were demolished to give way to new, tall apartment buildings.

A different solution was settled on Southern Haaga. The plots were left as they were, and villas that stood on them were simply replaced by low-rise apartment buildings. This preserved some of the village-like atmosphere and gave the area a very different character to the northern part of Haaga. No wonder then, if the new residents of Northern Haaga didn’t share a community spirit with the southerners.

A suburb worthy of a postcard? These newly-built apartment buildings in Northern Haaga ended up in one. Aerial photograph around 1955

Out of Many, One?

So Northern and Southern Haaga have separate identities. Perhaps the complementary development related to the new light rail project will bring the two districts closer together. Helsinki developers now have a chance to correct a mistake they made earlier.

To continue studying the research question, I would have to delve deeper into history of Northern Haaga. It says a lot that the history of that neighborhood is to be found in separate books! There are several books on the subject, for example Pohjois-Haaga – Pohjantähti Helsingissä (1987), Pohjois-Haaga: luonnon keskellä kasvava moderni kaupunginosa (2015) or PHYK : Pohjois-Haagasta menestyksen tielle (2006), the last of which tells the history of the Northern Haaga school. The school history might be interesting when it comes to local identity, if the school catchment area extends into both northern and southern parts of Haaga, for example.

References:

Leskelä, Ilkka: Oman tiensä tekijät – Haagan Työväenyhdistyksen 100-vuotinen historia. Haagan työväenyhdistys, Helsinki, 2008.

Both images from www.helsinkikuvia.fi

QGIS Exercise 4 – Time to put jobs on the map!

In this week’s QGIS, we examined the locations of workplaces in Helsinki area. Together with Mika, we created five heatmaps of different industry sectors’ workplaces in Helsinki area. The following is an excerpt of what we did.

Both industrial (figure 1) and commercial (figure 2) job distribution maps show plenty of activity around Helsinki capital region. With industrial jobs, this probably means factories, logistics centres and office complexes, located in established industrial zones and in proximity of motorways. Industry tends to avoid the pricey land of the city centre (with the exception of Hietalahti dockyard, which I will further examine later).

Figure 1: Industrial job distribution

Commercial jobs are centered in large shopping malls and they too have good connections to motorways. Helsinki city centre (which has its own share of shopping centres, too!) is the largest single cluster of commercial activity, but the shopping centre of Jumbo in Vantaa doesn’t seem to be far behind. Of the five sectors we compared in this exercise, commercial jobs are  the most evenly spread. This likely is the result of people’s desire to do their daily shopping close to where they live.

Figure 2: Commercial job distribution

An interesting possibility to continue from this study would be to compare the spatial distribution of commercial and industrial jobs historically, say between 1980 and 2010. In recent decades, many industrial workplaces have disappeared from the Helsinki city centre and relocated to the outskirts of the city (especially by the outer ringroad), that provide good connections and cheaper land. The major exception to me is the Hietalahti dockyard, where they continue to build ice-breakers to this day. Ship-building is one of the few labor-intensive industries remaining within Helsinki borders. Helsinki, hoping to preserve part of its industrial past, supports the dockyard’s operation in its master plan for decades to come.

Figure 3: Diagram of spatial industry structure

The diagram depicting industry structure by workplaces (figure 3) reveals that Helsinki is a true multi-industry city with no dominant sector of industry. Governance services and hotels and restaurants seem to be the largest sectors, but neither exceeds 1/6 of the total share. Historically, administrative sector has been important for Helsinki as it has never been known as an industrial town. The industry that it had has since relocated to surrounding cities, confirmed by the low share of the sector in the diagram. Those workplaces have been replaced by large scientific and information sectors, for which the university educates a skilled workforce. The relatively large share of international organizations is also worth noting, while it supports Helsinki’s claims of being a multicultural city.

The heatmaps reveal that most industries have clustered or at least have some activity in city centre. According to the maps, Helsinki city centre embodies that multi-industry city depicted in the diagram. The other clusters in the area can be very strong in one industry (such as scientific sector of Otaniemi) but severely lacking in another (for example real-estate sector in Otaniemi). The challenge for the future is to duplicate the diverse job structure of the city centre to other districts in the region.

Magical mix – The diverse historical layers of Haaga

This week, we were supposed to figure out what is the feature of diversity relevant to good/bad urban environment? I decided to take a wider approach and compare the historical layers of urban diversity of both buildings and people.

Haaga’s buildings aren’t that diverse these days. Instead, I took a long-term perspective and tried to find diversity by focusing on something that isn’t there anymore.

1969. Demolition work of Villa Johannislund in Sankaritie 9, Southern Haaga.

A history student always keeps their eyes open for historical layers in the area. However, nearly always all the historical layers aren’t there to be seen, because the old buildings have been demolished.

Walking around Southern Haaga, it is striking to notice how homogenous it is, despite its colourful history. In becoming just another generic Helsinki suburb, it is easy to claim that Haaga has lost a bit of its identity from the first half of the 20th century.

Especially the working class’ houses have almost completely disappeared. Those humble villas have been replaced by generic 1950s and 1960s apartment buildings, found everywhere in Helsinki. Of the more imposing, bourgeois villas, there are only very few left, forgotten in the woods and not visible from the street.

Of course, it was completely reasonable at the time to demolish the old wooden dwellings. They lacked the comforts of modern living and were often badly maintained. The working class buildings often stood on a plot owned by the municipality, and preserving them was always going to be an uphill battle.

Comparing Haaga to other villa communities near Helsinki, Kulosaari and Munkkiniemi could be fruitful, as they shared much in one point of history. However, Haaga, connected to Helsinki via railway, grew more quickly and gained a larger working-class population. Both Munkkiniemi and Kulosaari have managed to retain more of their pompous historical villas.

Could the absence of historical buildings then tell us about the past? In Haaga’s case, I’d argue that it does. Just by walking around in the three villa communities, it can be seen that Haaga was (and is) different. That it’s people were different.

1943. A picture of kids and their nurses in front of Kyläneva kindergarten. 75 years later, many of the kids are still with us, but the building certainly isn’t.

Does diversity (or the lack of it) affect the quality of urban environment? Urban areas are more diverse than rural areas, both through their population and temporal layers of built-up environment. It is widely known that they are more dynamic than scarcely-inhabited areas. An economist would confirm that cities are more productive.

Historical maps and books are essential for observing something that isn’t there anymore. Historical population data can be found open-source databases like Helsinki Region Infoshare (hri.fi). I would encourage everyone to dive deeper into the neighborhood’s history. They reveal much more about a neighborhood than can be seen at first glance.

Both images from helsinkikuvia.fi

QGIS Exercise 3 – Comparing regional efficiency in Helsinki

This week the exercise compared regional efficiency ratios in Helsinki districts. Together with Lauri and Mika, we created two maps on the subject and briefly discussed what they tell us about urbanity.

The map comparing regional efficiency ratios in Helsinki areas is a nice explanatory tool. Comparing regional differences gives one a good overview in general, but lacks more detailed information. The obvious weakness of the map is that the ratio tells nothing about the building types (aside from a short description in the legend). In case of Helsinki this is problematic, since the map shows semi-detached houses and rowhouses being the most common type of housing in Helsinki, while in reality that is not the case. A neighbourhood consisting mainly of apartment buildings among large parks might have the average ratio of a “semi-detached house neighbourhood”.

250 x 250 m grids or finer area units than districts give more detailed and comparable information of the efficiency because grids are equal sized and built environment that is not buildings, e.g. parks and football fields, are affecting less on the calculated values. The greater area unit we are focusing, the flatter values we get since it is always an average of several features within the area unit. Despite this, both maps tell the same story, highest building efficiencies are found in the downtown, but using the grid level it is possible to examine the variation also within the districts.

When the efficiencies were calculated with the districts, the highest ratio value any district received was 2.10 in Kamppi and the second highest was clearly lower in Punavuori 1.68. When calculating the same ratios with the 250 x 250 meter grids, variation is notably wider and because of that we created an extra class (2.10-3.64 Most dense apartment building blocks): highest grid value 3.64 was found in the Kluuvi district and totally 10 districts included one or more cells where the ratio is above 2.10. This is why many spatial phenomena should be examined in different area units. One example of the importance of using suitable area units can be raised from Vaattovaara & Kortteinen’s segregation studies: the concentrations of underprivileged people in Helsinki region are found in single housing companies or even in stairways, which couldn’t be observed if the area unit used is too large (e.g. blocks or districts).

What about building efficiency ratio as a measure of urbanity?

Building efficiency ratios are very useful in comparing different districts densities. The ratio gives an idea how densely built area is. Maps showing more detailed efficiency ratios can point where the most urbanized areas of the city are.

However, urban is not all about density and a high building efficiency ratio tells little about the neighbourhood’s character. Sure, a dense neighbourhood offers more encounters between residents, which is essential for an urban environment. But an urban area should offer a wide variety of services. A business district that is dead silent after 5pm in a Friday or a residential area with only high-rise apartment buildings and green areas between them are hardly urban.

An unlikely workers’ paradise? The short history of independent Haaga borough

The old grocery store, favored by the working class, now houses apartments.

The development of Haaga was kickstarted by completion of the Turku railway in early 20th century.  Rather than Turku, it was the fast service to Helsinki that tempted people to settle the quiet forests of Haaga.

First movers were Swedish-speaking well-to-do elites of Helsinki. They, following the garden city ideal, established a villa community there in 1906. Similar settlements developed elsewhere in Helsinki area, most notably in Kulosaari and Munkkiniemi. Like Haaga, those are nowadays all districts of Helsinki.

While the wealthiest settled the area closest to the Huopalahti station,  Finnish-speaking folk, brought by train from the countryside, built their houses in the forests and hills. Then came the workers and settled along the highway to Turku. (Roos 1950, 35) This meant that Haaga gained a very diverse population compared to other villa communities at an early stage.

Even though the administrative language of Haaga was at first Swedish, it quickly became a truly bilingual community. (Roos 1950, 50) Despite Finnish-speakers being sixty percent of the total population, Swedish-speakers gained the most seats in the municipal election in 1920. (Roos 1950, 94)

The election-winning Swedish-speaking population wanted more say in the matters of their community. Those plans finally bore fruit in 1922, when Haaga became an independent borough (kauppala in Finnish) from Helsinki rural commune (Helsingin pitäjä in Finnish). Boroughs were town-like settlements, but not large enough to be called such administratively. The population of Haaga at the time was 2,700. 

Becoming an independent borough was expensive: Haaga needed to improve its roads, water system and sewer networks. A school and a municipal building were built. All this was financed by loans, and they eventually became too heavy a burden for the small borough. Interestingly, the loans were partly backed by Helsinki through the Union of Finnish Towns. (Roos 1950, 193)

While Haaga had some small industry, larger factories were never established in Haaga, because those would’ve sat poorly with the image of the garden city.  There was also no liquor store due to the resistance by the commune council, only a few licensed restaurants (Roos 1950, 140, 191)

Regardless, in the following decades, Haaga became all the more Finnish-speaking and working class. In addition to villas, detached houses and small apartment buildings were built. Haaga started to resemble more and more like a small town. 

Statistics from 1930 show that Haaga residents had less space in their living quarters than the Helsinki average (and much less than their neighbours in the wealthy Munkkiniemi!) Most of the population was working class, which shaped the identity of the borough. The impressive building of Elanto  grocery store chain, favoured by the working class, (pictured at the top) was built in 1927 and is still standing today. (Roos 1950, 139)

The discussions to incorporate Haaga to Helsinki started already in the 1920s, but took more than two decades to complete. Haaga didn’t oppose the annexation as strongly as the wealthier Munkkiniemi or Kulosaari borouhgs, perhaps because of its financial struggles. In the end, the state government  authorized the annexation in 1944 and in 1.1.1946, the independent Haaga borough ceased to exist. The population of 3,300 at the time became Helsinkians more or less willingly. (Roos 1950, 205-212)

References:

Roos, John. E., Haagan kauppalan historia, SKS, Helsinki 1950

Grocery store image source: www.helsinkikuvia.fi