Tag Archives: Helsinki open data

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.

2015 on 1917

Kulosaari (Brändö in Swedish), an 1,8 square km island in Helsinki, detached itself from the Helsinki parish in early 1920’s, and became an independent municipality. The history of Kulosaari is an interesting chapter of Finnish National Romantic architecture and semi-urban development. It all began in 1907 when the company AB Brändö Villastad (Wikipage in Finnish) was established – but that’s another story. In 1949, the island was annexed again by Helsinki. Today, Kulosaari is cut in half by one of the busiest highways in Finland. The idealistic, tranquil village community is long gone. Since late 1997, Kulosaari has been my home suburb.

One of the open datasets provided by Helsinki Region Infoshare, is a scanned map of Kulosaari from 1917. Or rather, a scheme which became reality only in a limited extent. As long as I’ve known a little about what georeferencing is all about – thanks to the excellent Coursera MOOC Maps and the Geospatial Revolution by Dr. Anthony C. Robinson – I’ve had in mind to work with that map some day. That day dawned when I happened to read the blog posting Using custom tiles in an RStudio Leaflet map by Kyle Walker.

Unlike Kyle, I haven’t got any historical data to render upon the 1917 map but instead, there are a number of present day datasets available, courtesy of the City of Helsinki, e.g. roadmap and 3D models of buildings. How does the highway look like on top of the map? What about buildings and their whereabouts today? Note that I don’t aim particularly high here, or to more than two dimensions anyway; my intention is just to get an idea of how the face of the island has changed.

Georeferencing with QGIS is fun. I’m sure there are many good introductions out there in various languages. For Finnish speakers, I can recommend this one (PDF) by Latuviitta, a GIS treasure chamber.

georeferencing

The devil is in the detail, and I know I could’ve done more with the control points, but that’s a start. When QGIS was done with number-crunching, the result looked like this when I adjusted transparence for an easier quality check.

qgistransparence

Not bad. Maybe hanging a tad high, but will do.

Next, I basically just followed Kyle’s footsteps and made tiles with the OSGeo4W shell. I even used the same five zoom layers than he. Then I uploaded the whole directory structure with PNG files (~300 MB) to my web domain where this blog resides, too.

Roadmap data is available both in ESRI Shapefile and Google KML. I downloaded the zipped Shapefile, unzipped it, and imported as new vector layer to QGIS. After some googling I found help on how to select an area – Kulosaari main island in my case – by rectangle, how to merge selected features, and how to save the selection as a new Shapefile.

Then, to RStudio and some R code.

In Kulosaari, there are 23 different kind of roads. Even steps (porras) and boat docks (venelaituri) are categorized as part of the city roadmap.

> unique(streets$Vaylatyypp)

 [1] "Asuntokatu"                             "Paikallinen kokoojakatu"                    
 [3] "Huoltoajo sallittu"                     "Moottoriväyläramppi"                        
 [5] "Alueellinen kokoojakatu"                "Silta tai ylikulku (katuverkolla)"          
 [7] "Moottoriväylä"                          "Pääkatu"                                    
 [9] "Silta tai ylikulku (jalkakäytävä, pyörä "Alikulku (jalkakäytävä, pyörätie)"          
[11] "Jalkakäytävä"                           "Porras"                                     
[13] "Yhdistetty jalkakäytävä ja pyörätie"    "Puistotie (hiekka)"                         
[15] "Ulkoilureitti"                          "Puistokäytävä (hiekka)"                     
[17] "Puistokäytävä (päällystetty)"           "Venelaituri"                                
[19] "Polku"                                  "Suojatie"                                   
[21] "Väylälinkki"                            "Pyöräkaista"                                
[23] "Pyörätie"

From these, I extracted motorways, bridges, paths, steps, parkways, streets allowed for service drive, and underpasses.

Working with the 3D data wasn’t quite as easy (no surprise). By far the biggest challenge turned to be computing resources.

I decided to work with KMZ (zipped KML) files. The documentation explained that the data is divided into 1 x 1 km grids, and that the numbering of the grids follows the one used by Helsingin karttapalvelu (map service). The screenshot below shows one of the four grids I was mainly interested in: 675499 (NW), 674499 (SW), 675500 (NE) and 674500 (SE). These would leave out outer tips of the island in the East, and bring in a chunk of the Kivinokka recreation area in the North.

kartta.hel.fi

First I had in mind to continue using Shapefiles: imported one KML file to QGIS, saved as Shapefile, and added it as a polygon to the leaflet map. It worked, but I noticed that RStudio started to slow down immediately, and that the map in the Viewer became seemingly harder to manipulate. How about GeoJSON instead? Well, the file size do was reduced but still too much data. Still, I succeeded in getting all on the map, of which this screenshot acts as the evidence:

roadmap and 3D buildings

However, where I failed was to get the map transformed to a web page from the RStudio GUI. The problem: default Pandoc memory options.

Stack space overflow: current size 16777216 bytes.
Use `+RTS -Ksize -RTS' to increase it.
Error: pandoc document conversion failed with error 2

People seem to get over this situation by adding an appropiate command to the YAML metadata block of the RMarkdown file, but I’m not dealing with RMarkdown here. Couldn’t get the option work from the .Rprofile file either.

Anyway, here is the map without the buildings, so far: there is the motorway/highway (red), few bridges (blue), sandy parkways (green) here and there, a couple of underpasses (yellow), streets for service drive only (white) – and one path (brown) on the Southern coast of the neighbour island Mustikkamaa, as unbuilt as in 1917.

Note that interactivity in the map is limited to zooming and panning. No popups, for example.

I’ve heard many stories of the time when the highway was built. One detail mentioned by a neighbour is also visible on the map: it reduced the size of the big Storaängen outdoor sports area on the Southern side of the highway. The sports area is accessible from the Hertonäs Boulevarden – now Kulosaaren puistotie – by an underpass.

EDIT 26.3.2015: Thanks to the helpful comment by Yihui Xie, I realized that there is in fact several options to do a standalone HTML file from the RStudio GUI. With File > Compile Notebook... the result was combiled without problems, and now all buildings are rendered in the leaflet too. The file is a whopping 7 MB and therefore slow in its turns, but at least all data are now there. As a bonus, the R code is included as well! RStudios capabilities don’t stop to amaze me.