Routes of inhabitants when exercise


Here are the exercise routes of people who live in Haaga. I have collected these data from exercise application’s website. Some of them are open to the public. There are 343 routes in all and the width of the lines indicates how often people will choose this route to have an exercise. From this map we can roughly see the north of railway is much more active than the south. More precisely, there are three places most people like to go: along the railway, the Anio Achtes park and the City park which located in the right of Haaga.


Basing on their starting point, I can divide them into four parts – Laasila, North Haaga, Railway station and South Haaga. I have compose their frequent routes into one map so we can see if there is any difference or relationship.

Here you can see most routes in Laasila are going to Kaarelanpuisto and City park.

People start from North Haaga like to exercise in Anio Achtes park and the City park.

Here is a interesting point is that there are quite many routes start from the railway station. I’m not sure they come from Haaga or not so I conclude it into a single part. The most routes are coming along with the railroad. It seems like people like to run along there and come back at the station.

Routes start from South Haaga are confusing. They have many choices. Some of them go down to Pikku Huopalahden puisto and some are choosing Riistavuoren puisto. There are still a lot of people choose to the City park.


About the exercise time. There are 210 of 343 routes that indicate their training time. 14.8% (31) routes happened in the morning. 41.4% (87) routes happened in the afternoon. 43.8% (92) routes happened in the evening. It seems that most inhabitants will choose to go out in the afternoon or evening.

Mid-term: About What I May Do Next



Through these several weeks’ study. We have collected many information about Haaga before 17th century to the 20th century. Before 17th century there was a forest without anyone living here. In 1915 Eliel Saarinen was hired by Ab MGStenius to make the Munksnäs-Haga plan but unfortunately not implemented. And according to Babak, before 1960`s there were only a few villa type developments in Haaga area. Most constructions we see now were built between 1961 and 1997.


Haaga has changed a lot than yesterday. Rivers become the roads and green space. The original texture planned by Saarinen gets replaced by a new one. Now it looks like a normal area in suburb of Helsinki. Inhabitants are most in a middle or lower class. Two railways lay across the Haaga and there are only a few tunnels connected except the station. From the texture you can see the buildings are separated into several parts.


On the Monday’s introduction course Mr. Manninen showed us the city plan of Helsinki. We can see in the future there will be a lot of constructions built along the E12 highway. Besides, Eliel Saarinen tie will become an important part of the ring road of Helsinki.

Problems I see


Segregation is common phenomenon that usually happened in suburbs. It can get caused by physical and mental factors. Physical factors often conclude railways, highways and walls… So you can easily see these in suburbs especially the industrial areas. Mental segregation is often caused by different cultures, religions or even different classes people are in.

I think segregation do exist in Haaga. Inhabitants living in south part of Haaga have a better education and average income than north part of Haaga.  If we let it go then for a long time it will form a stereotype to this area’s people. New residents moving to here will follow and strength this stereotype. Unfortunately, we can’t simply mix these inhabitants together to solve this problem.

Public space and recreational services

Except the Alprosparken, Haaga has much green space but most of green space is on the periphery of Haaga and doesn’t connect like a system. In other words, the green areas actually don’t serves the inhabitants. In a smaller scale, inhabitants lack the some places to do some activities or enjoying their neighborhood.

Haaga also lacks many kinds of recreational services. According to Karolina, there are only three bars, no shops and cafes.

However, I don’t have an interview of the local people of their needs and complaints. Lack of public space and services is my own opinion.

What I want to do

The inhabitants here have a convenient transportation to the center of Helsinki. So I want to create a green system benefits Haaga inside. This system will link the different residential areas and important constructions in Haaga and more accessible for inhabitants. In the nodes of this system there will be some space to carry the recreational activities. I believe through creating a environment for inhabitants to communicate and other social activities we can improve the harmony of this area to decrease the segregation phenomenon.

Also this system will be more flexibility. In the future government may build many constructions here. I will create some transition zones. For now they are green infrastructure. But if there will be a new building units they can build in transition zones and get linked into the system.

Measuring Unmeasurable

This article is trying to analyse the development, people and services of Haaga in a quantitative way compared by other regions of Helsinki. In this analysis I will just state the result the map showing and not giving many my opinions or assumptions.

Construction Development:

I choose the density of building efficiency ratio (FAR) and job volumes to assess the construction development of Haaga. FAR indicates the building floor areas in each unit and job volumes shows the jobs offered in each area.

It’s easy to find the whole Haaga keeps a low development and an even distribution in the FAR map. The Stromberg and Pikku Huopalahti locate beside the Haaga and have a higher FAR compared with Haaga.  The jobs map illustrate the Haaga keeps a low level of job volumes and beside it Stromberg owns high amount jobs.

There is a interesting point that you can see a clear line starts from the central Helsinki passing Haaga to the north-west.


According to the data from PX-Web ( databases I try to analyse the Haaga’s population composition. First is the class. We can directly see that Haaga is not a wealthy district in a large scale and the high income  areas spread along the sea coastline. What’s more, the inhabitants in south of Haaga have a better salary than northern inhabitants. We can have a rough presuming that most inhabitants in Hagga are in a middle or lower class.


Then is the age structure.  The youth (<18) and the old (>60) account 12.6% and 24.8% of total inhabitants. It’s not a healthy rate compared with other areas of Helsinki.

About the job and education, Haaga keeps a medium unemployment rate of 5.6% and a relatively high education rate of 68.6%.

Compared with other areas,  Haaga’s inhabitants are not in a good situation both in age structure and income. There is also a economic gap between north and south Haaga.


Services analysis basing on the daycare institutions, schools and elderly homes (data comes from GIS courses). Despite the size of them, I set a 500m serving buffer of each POI. In general Helsinki have a good coverage of daycare but not well in  elderly homes and schools covering in suburbs. Haaga seems to have enough service institutions.

The result of analysis is similar to my impression of Haaga during the excursion – a normal residential district. From the income we can see a slight difference between the south and north Haaga. I’m curious that does this have any possibility to influence the cultural level and make a separation? From the data I collect can’t get any result. So I’ll see other’s posts about diversity to get any clue.


Practical 3 – Some Skills and Tips

1. How to replace the null as 0.

After you join the attributes you can find some fields have null values. Always these null values won’t show up when you illustrate them. To solve this phenomena you need to replace the null values to 0. First you need to choose the null values in the attribute table. Then open the field calculator, choosing the field you want to update and putting 0 in expression.

2. Data type chosen when analysing by fishnet.
The building data we analyse is a point type. I think there will be many offsets with point type. If there is one building located between the two units of fishnet. This building should be separated into two parts and be calculated. But if the polygon type is transferred into the point type that means the point will only locate in single unit and this unit’s ratio will be much higher.

3. Some offset may happen if switch the steps.

I want to list this example to show the importance of correct steps. Before you start to join the attributes with fishnet and building data you need to clip it first rather than clip it after you have done the analysis. There will be some offsets exist at the edge of your area. You can see there are some points located beyond the edge of area but still located in the net unit of the edge. If you clip it first the building data will not be attributed but if you analyze first this data will get attributed and the values will be higher.

4. Expression should be correct when calculating.

Also, when you calculate the FAR you need to use the expression by floor area / $area not floor area / size of net. Because at the edge of the area the nets get clipped so the unit is not a full square.


Group member: Yao Chaowen, Dong Jiayi, Yen-chi Liang, Li Yingxin

Beyond Wikipedia – An Unimplemented Planning

When introducing the construction history of Haaga, Eliel Saarinen must be mentioned. As the founder of Organic Decentralization theory and also the most famous urban planner in Finland, Saarinen was hired by Ab MGStenius to make the Munksnäs-Haga plan in 1915. Although the maps in 1952 showes that this planning didn’t get implemented, I still think this is important for Haaga’s development.


In this book Saarinen make a specific description of his plan from the whole area’s arrangement into each house’s planning. In his planning, a railway and boulevard connect the Haaga and Helsinki. Haaga will become a half-independent satellite town to solve the future population pressure of Helsinki. The transportation of network looks like the multi-core radial pattern and the traffic avenues and living roads get well organized. What’s more, the whole area is divided by main traffic avenues into many small parts. Each part has several living communities. Green spaces are placed in the nodes and broad avenues and hard to be regarded as a system. The order of buildings follows the orientation of the terrain. Different types of houses located in different areas, It seems Saarinen wants various kinds of classes and various functions of buildings to get grouped. The education institutions and warehouses are not noted on the map so it’s hard to tell the arrangement of public buildings. There is also an aesthetic principle about houses. In this principle, different kinds of buildings like villa or public ones get recommended the building style and elevation.

It’s a practical and detailed planning. But due to the economic impact the First World War brings, Finnish government couldn’t afford the huge cost of this planning. The abandoned of Munksnäs-Haga plan was a matter of course. Even so, there were some houses get built besides the railway and the main roads. After the Second World War, Haaga got a rapid development and the older wooden houses were replaced by modern multi-storey houses and townhouses. Then from 1970s till now Haaga nearly keeps the same.

By comparing urbanization between Finland to other countries, he makes a prediction that Haaga would have a population between 549,880 to 373,480 in 1945. If his planning gets full implemented, maybe we will see a bustling Haaga or another fail case like Howard’s Garden City. However, we all have seen its situation now.

  1. Munkkiniemi-Haaga ja Suur-Helsinki : tutkimuksia ja ehdotuksia kaupunkijärjestelyn alalta / tehnyt Eliel Saarinen.

QGIS Practical 2

We choose one segment of our excursion as this practical assignment because we think it contain almost all kinds of suburb views in Helsinki.

The basic workflow is background map – stop points – route line – interest points – exploration areas – layout. The whole process is not difficult. The basic tools are learnt and practiced in the crash courses. There is a new tool named Georeferencer may be needed if you want to use a download map as the background.

First you need to assure some location points between the picture and basic map (usually locating 5 or 6). The points should be spread in everywhere or there will be a big offset. After done it click green play button.

Group member: Yao Chaowen, Dong Jiayi, Yen-chi Liang, Li Yingxin

Reconstruct Haaga Before 17th Century

Unfortunately, it’s quite difficult to find any relevant information of Haaga before 1900. Therefore, I’ll use my available materials to speculate how Haaga looked like before 17th century.

From the satellite we can see Haaga get developed most in the period from 1943 to 1969. Before 1943 there were only few houses built besides the railway and farmland. We all know that things that can bring convenience or economic benefit have an attraction to settlement. Without the railway what can Haaga look like? May be only farmlands and few houses.

By googling the history of Finland I found an interesting point that at Finland was ruled by Sweden before 18th century. Helsinki was founded by Gustav Vasa in 1550, and it was regarded as a fishing village for more than two centuries (Wikipedia). No one wants to eat finish all the year, as a fishing village, there should be some land used as farms[2]. Another important information is that in 16th century Finland was growth of the area settled by the farming population(Wikipedia). So there must be many peasants living around the ancient Helsinki. But whether there would be farmland in Haaga?

Through the elevation map of Helsinki we can see Haaga is not a flat area compared with the east and south areas in helsinki. The north side and south side have a 30 meters height change. And the only river is far from Haaga. In my opinion, in ancient ages farmland would exist mostly in the flat and closing to fresh water area because it’s easier to break the ground and irrigate. What’s more, before 17th century there were only 300,000 people living in Finland[3]. Without the population pressure Finnish peasants didn’t need much land to farm. If I was a farmer, I would like to choose to break a farmland in areas like Pukinma and Tapaninkyla. Therefore, I think there would not be any people living in Haaga before 17th century.

What kind of nature did Haaga used to have? I think I can reply this answer through the Haaga Rhododendron Park. Through the satellite map we can see the park was the only area that didn’t get many changed since 1932. Although some spices in the park like rhododendron were introduced and didn’t exist at 17th century, the forest can’t be introduced and may exist for a long time. If we assure it, we can get a conclusion that Haaga is a forest before 17th century.

To sum up, Haaga before 17th century is totally a forest without any people living. In the north-east site there might be some bare huge stones like Viikin Kallilot. It’s absolutely the wild animals’ heaven.

Irrelevant to the conclusion, I still have a confusion about Haaga. In my recognition, the division of areas always based on artificial boundary or natural elements. For example, river, farm’s border, wall, railway… However, I don’t see any special elements on the boundaries of four Haaga areas. I’m curious in what reason government plan these boundaries.


  2. The Middle Ages: A Comprehensive Overview of Europe, 500-1500
  3. Westerholm, Populating Finland, Fennia vol. 180: 1–2 (2002), p. 145

QGIS Practical 1

Green Area Proportion

Description&Tips: 1. Open the attribute table of Helsinkin_small_area. Edit it and open the field calculator to create a new field with the expression of $area. Then getting the area(m2) of each small area of Helsinki.

2. Use the Zonal Statistic to calculate the account of pixels of green area in small area. (Notice that don’t type too much on Output column prefix or the name won’t show completely in the attribute table.) The _account means how many pixels located in this area.

3. Use the field calculator to get the green area(m2) with the expression of _account * 400. (Usually we can know the pixel size when we download or transfer it. But if we forgot, we can still get the approximately size by the tool Measure Line.) Finally, we can calculator the proportion by the expression of Green area(m2) / Area(m2) * 100.

Analysis&Discussion: 1. Basically, we can see that the central areas in Helsinki lacks green land. The Ulkosaaret area gets the lowest proportion because of mostly in the sea. The Ultuna is the highest area because of far from Helsinki center.


Population Pressure

Description&Tips: 1. Join the population layer into the small area layer by the tool Join attributes by layer. Choose intersects as the Geometric predicate, take summary of intersecting features in the Attribute summary and sum in the Statistics for summary. (Notice: the type of population must be point. There would be some repeated calculation when using polygon format and I’ll discuss it later in the Analysis.)

2. Next we can calculate the population pressure with the expression of sum_asukkaita / green area(m2) * 1000000 (Notice:1. You need to converse the meters to kilometers by multiplying 1000000. 2. When using Field calculator, remember to choose Decimal number as the output field type or some results would be single 0.)

Analysis&Discussion:  1. Generally, the result of the population pressure is quite similar to the proportion of green area. The central Helsinki is quite high and farer place is low. In detailed, Punavuori is the highest area and Salmenkallio is the lowest area. It’s different from the previous result because the population attribution varies.

2. Shortly, Punavuori, Kamppi and Kaupunginosa are the areas that pressure is much high. They may need to improve the quality of green space.

3. If you use the polygon type of population, the final result will become bigger because if there are some polygons located between two areas then these polygons will be counted in twice.


For instance, the four pictures below indicate the sum of population with point and polygon types. You can see the population with point type is smaller than with polygon type.







So what shall we do when our population data is polygon format? We can use the Vector -Geometry tools – Polygon centroids to convert the polygon into the point. You can see each polygon create a point in its centroid.

  1. Above the discussion we get acknowledge that elements between two areas may cause bias. How about if one point locate between two areas? We all know point is infinitesimal in mathematical definition. However, in QGIS we can’t assure if it has some size. So when a point locate in the line between two areas, what would happen? We’re still trying to figure it out.


Group member: Yao Chaowen, Dong Jiayi, Yen-chi Liang, Li Yingxin

Some Thoughts About QGIS

I have learned some about ArcGIS before, and this is my first time to use QGIS. After the crash courses I have some ideas about the difference between them. I’ll update if I find more.

1. QGIS has a clean interface, most difficult functions need to be achieved by plugins, so it’s nice for new beginners to start their journey.

2. QGIS are really good at graphic expression. To some degree you even don’t need to decorate the output in Photoshop.

3. Usually you can open the all kinds data in a button in ArcGIS. However, in QGIS you need to use different buttons with different type of data. May be it’s good for student to distinguish the vector and raster format?

4. QGIS is not good at 3d expression. In ArcGIS you can watch the 3d directly and adjust height.

5. I used to do some cost path analysis in ArcGIS, but in QGIS I didn’t find some plugins that can offer od-cost matrix yet.