champagne anarchist | armchair activist

Qgis

Gentrificatie in kaart gebracht

De kaartmakers van de gemeente Amsterdam hebben een kaart gemaakt waarop je de Buurtstraatquote (BSQ) ziet. De BSQ speelt een centrale rol bij de hervorming van de erfpacht, waarmee het sociale grondbeleid van de gemeente wordt uitgehold - maar daar gaat dit artikel niet over. Voor nu ben ik geïnteresseerd in de BSQ als graadmeter voor grondwaarden.

Zoals de gemeente samenvat, zijn «de hoge BSQ’s te vinden in de gewilde locaties in de stad en de lage BSQ’s in de minder gewilde locaties in de stad». De grachtengordel en de omgeving van het Vondelpark hebben hoge BSQ’s; lage BSQ’s zijn te vinden zijn in Zuidoost, Nieuw-West en Noord. Dat viel te verwachten.

Interessanter is de verandering van de BSQ. De gemeente heeft cijfers beschikbaar gesteld over duizenden straten of straatsegmenten, voor de jaren 2014 en 2016. Dat is natuurlijk een korte periode en je kan er niet zomaar van uitgaan dat deze periode representatief is voor lange-termijntrends. Even goed geven de cijfers een interessant beeld.

De grafiek hieronder toont de verdeling van BSQ’s voor meergezinswoningen in 2014 en 2016.

De piek is naar rechts verschoven en de mediaan is gestegen van 28 naar 38. Om politieke redenen is bepaald dat de BSQ nooit lager dan 5 of hoger dan 49 kan zijn, wat verklaart waarom zoveel straten een BSQ van 5 of 49 hebben. Dit impliceert dat de toename van de BSQ waarschijnlijk geen volledig beeld geeft van de stijging van de grondprijzen.

Op de kaart hieronder zie je de ontwikkeling van de BSQ voor meergezinswoningen in verschillende delen van de stad. Straten waar grote veranderingen misschien geflatteerd zijn door de onder- en bovengrens van de BSQ heb ik weggelaten. Dat geldt voor straten die al in de buurt van de maximale BSQ zaten, met name de Grachtengordel en delen van Zuid. Het geldt ook voor straten, vooral in Zuidoost, waar de BSQ in de buurt van de ondergrens van 5 is gebleven.

Rood betekent dat de BSQ met tenminste de helft is toegenomen; oranje een stijging met minder dan de helft en groen dat de BSQ is gedaald. Er zijn enkele rode gebieden buiten de ring: met name IJburg, bepaalde delen van Nieuw-West en Buitenveldert. Buitenveldert grenst aan de Zuidas en heeft te maken met instroom van expats en studenten.

Binnen de ring stijgt de BSQ in gebieden die vaak worden geassocieerd met gentrificatie, zoals de Kolenkit in West, de Vogelbuurt in Noord en de Indische Buurt in Oost. Verassender is Betondorp, een buurt met lage inkomens waar veel ouderen wonen. In 2015 werd deze buurt nog omschreven als «een van de weinige wijken in Amsterdam waar de oprukkende gentrificatie nog niet heeft toegeslagen». Als de BSQ een graadmeter is, dan zou dat wel eens kunnen veranderen.

Voor methode en technische details, zie de Engelstalige versie van dit artikel.

Gentrification mapped

The map makers of the City of Amsterdam have created a map that shows the Neighbourhood Street Quota or BSQ. The BSQ plays a key role in a highly controversial reform that is eroding the city’s social ground lease policy, but that’s not the topic of this article. For now, I’m interested in the BSQ as an indicator of land value.

As the city government puts it, «the high BSQs are found at popular locations in the city and the low BSQs at less popular locations in the city» (for details see Method, below). Unsurprisingly, the centrally located Centrum and Zuid districts have high BSQs and the peripheral areas have low BSQs.

More interesting is how the BSQ has changed. The city government has provided data for thousands of streets or street segments, for 2014 and 2016. Of course, this is a short time period and the patterns may or may not reflect longer-term developments.

The chart below shows the distribution of BSQs for flats (as opposed to single-family dwellings) for 2014 and 2016.

The peak has moved to the right, as the median value has risen from 28 to 38. For political reasons, the BSQ can never be lower than 5 or higher than 49, which explains the large number of streets with a value of 5 or 49. This implies that rises in BSQ don’t fully reflect how much land values have risen.

The map below shows how much BSQs for flats have risen in different parts of Amsterdam. I omitted streets with low or high BSQs where substantial changes in BSQ may have been hidden by the upper and lower limits. At the high end, this applies to the Canal Belt and much of the Zuid District. At the lower end, this applies to many peripheral areas including almost the entire Zuidoost District.

Red streets indicate an increase of the BSQ by more than a half; orange streets an increase by less than a half and the rare green streets a decrease of the BSQ. There are some red areas outside the ring road: mainly the IJburg expansion to the east; some parts of Nieuw-West; and Buitenveldert. Buitenveldert is a neighbourhood south of the Zuidas business district with a growing number expats and students among its residents.

Within the ring road, BSQs are rising in areas that are often associated with gentrification, such as the Kolenkit in West, the Vogelbuurt in Noord and the Indische Buurt in Oost. Perhaps more surprising is Betondorp, a low-income area with many older residents, described in 2015 as «one of the few neighbourhoods in Amsterdam not yet affected by the advance of gentrification». If the BSQ is an indication, that may be about to change.

Method

A list (pdf) of BSQs for 2016 and 2014 was recently sent to the city council. The BSQs are referred to as 2018 and 2017, but are based on data from 2016 and 2014 respectively (or to be more precise: the ‘2017 BSQ’ uses data from 2015 or 2014, whichever is lowest). The map created by the City of Amsterdam uses the ‘2017 BSQ’.

For each house, the municipality calculates an individual land quota using the formula: land value / (land value + theoretical cost of rebuilding the house). The land value is obtained by subtracting the rebuilding cost from the total value of the house (WOZ).

Subsequently, BSQs are calculated as the average land quota per street (or street segment if a street traverses multiple neighbourhoods). This is done separately for single-family dwellings and flats.

The interpretation of the BSQ is a bit tricky: one should expect higher land values to be reflected in higher BSQs, but the exact relationship will depend on the value of the building and whether that also responds to changes in land value (for example, because more expensive materials are used).

In my analysis, I only used BSQs for flats, and only the streets or street segments for which a BSQ is available for both 2014 and 2016 (thus excluding new urban expansions).

For the map, I also excluded streets where an increase of the BSQ by less than half may be hidden by the lower or upper limit of the BSQ: those with a 2014 value of 5 and a 2016 value of less than 8; and those with a 2014 value above 32 and a 2016 value of 49.

In creating the map I also ignored long streets that traverse multiple neighbourhoods and that therefore have been separated into multiple segments. Constructing street segments from line geometries representing the entire street seemed like a lot of work (perhaps there’s a simple way to do this, but I couldn’t find it).

I used Tabula to extract data from the original pdf; this Python script to process the data, create a csv for the chart and create a shapefile for the map; D3.js for the chart and Qgis to create the map (using Open Street Map map data and Stamen Toner Lite for the background).

Nekt Strava de Fietstelweek?

Strava is een populaire app om fietsritten mee op te nemen. Het bedrijf probeert al een paar jaar om zijn gegevens aan lokale overheden te verkopen zodat die ze kunnen gebruiken bij hun fietsbeleid. NDW, een platform van overheden waaronder Amsterdam, heeft zes maanden aan Stravagegevens gekocht om eens uit te proberen wat je hiermee kan.

De overstap naar Strava betekent mogelijk het einde van de Fietstelweek, een jaarlijkse actie om fietsgegevens te verzamelen waar duizenden vrijwilligers aan meedoen. Ik heb de gegevens van de Fietstelweek ooit gebruikt om te analyseren hoe lang je moet wachten bij stoplichten. De Fietstelweek kreeg geld van dezelfde overheden die nu experimenteren met gegevens van Strava.

Eén van de redenen waarom overheden naar alternatieven kijken is dat de Fietstelweek minder deelnemers heeft dan ze graag zouden willen. Daar zit iets in. Neem bijvoorbeeld de onderstaande kaart, met fietsroutes van en naar Amsterdam Centraal Station.

Op zich een interessante kaart. Niet heel verassend is de intensiteit het hoogst in de buurt van fietsenstallingen. De Geldersekade (met de soms chaotische kruising met de Prins Hendrikkade) en de Piet Heinkade lijken belangrijke toegangswegen te zijn. Het lijkt erop dat mensen die met de fiets naar CS gaan wat vaker in het oosten van de stad wonen.

Maar let op: het gaat om kleine aantallen. Zelfs de drukste segmenten vertegenwoordigen niet meer dan 40 ritten. Eén loyale deelnemer aan de Fietstelweek zou letterlijk de kaart kunnen veranderen door de hele week haar fietsrit naar werk op te slaan.

Strava beschikt over veel grotere datasets, maar deze gegevens roepen weer andere vragen op. Strava noemt zich ‘het sociale netwerk voor sporters’ en wil weten of je een racefiets, een mountainbike, een tijdritfiets of een cyclocrossfiets gebruikt (‘anders’ is geen optie). Is Strava wel representatief voor mensen die bijvoorbeeld op hun stadsfiets naar werk gaan?

Het antwoord van Strava op dit soort vragen is dat ze proberen om competitie minder centraal te stellen en hun app socialer te maken, met Facebook-achtige tools. Op die manier hopen ze meer gegevens te verzamelen over ‘normale’ fietsritten Ze zeggen ook dat mensen met de app vaak dezelfde routes rijden als andere fietsers, vooral in de steden.

Maar klopt dat wel? De Strava heatmap (kies rood en rides) voor Amsterdam zou je misschien kunnen interpreteren als een combinatie van recreatieve routes (Vondelpark, Amstel) en fietsers die zo snel mogelijk de stad in of uit proberen te rijden (plus flink wat mensen die hun rondjes op de Jaap Edenbaan hebben opgeslagen als fietstochten).

Misschien valt er een manier te bedenken om de recreatieve en sportieve ritten eruit te filteren en hou je dan nog genoeg ‘normale’ fietsritten over. Aan de andere kant, bijna driekwart van de fietsritten in Nederland is korter dan 3,7 km, en ik vermoed dat zulke korte ritjes zelden op Strava worden gezet.

Er is ook nog een sociaal-economisch aspect. Er is aangevoerd dat Strava vooral wordt gebruikt door mensen die in de rijkere buurten wonen, terwijl andere buurten misschien wel meer behoefte hebben aan betere fietsinfrastructuur.

Natuurlijk is het fietsgebruik sowieso ongelijk verdeeld, en dat zie je ook terug in de gegevens van de Fietstelweek. De kaart hieronder toont de start- en eindpunten van fietsritten in Amsterdam.

De dichtheid is het grootst in het gebied binnen de ring ten zuiden van het IJ. Het aantal fietstochten per 1.000 inwoners correleert ook met woningwaarde: veel fietstochten beginnen of eindigen in rijkere buurten. Zoals gezegd, dit weerspiegelt waarschijnlijk het werkelijke fietsgebruik en wijst dus niet op een probleem met de gegevens.

Om samen te vatten: de Fietstelweek heeft kleinere aantallen deelnemers dan je zou willen, terwijl de gegevens van Strava vragen oproepen over de representativiteit. Strava zou natuurlijk kunnen helpen om die vragen te beantwoorden door een deel van de Amsterdamse gegevens beschikbaar te stellen als open data.

Dit Python-script laat zien hoe de analyse is uitgevoerd.

Dutch governments consider using Strava data

Strava is a popular app to record bicycle rides. For some years, the company has been trying to sell its data to local governments for traffic planning. NDW, a platform of Dutch governments including the city of Amsterdam, has bought six months’ worth of Strava data to give it a try.

The switch to Strava may mean the end of the Fietstelweek, an annual one-week effort to collect bicycle data from thousands of volunteers. In the past, I’ve used Fietstelweek data to analyse waiting times at traffic lights. The Fietstelweek received funding from the same governments that are now experimenting with Strava data.

One reason why they are looking for alternatives is that the number of Fietstelweek participants is lower than they’d like. They seem to have a point. Consider for example the map below, which shows bicycle routes to and from Amsterdam Central Station.

As such, it’s an interesting map. Unsuprisingly, it seems that intensity is highest near the bicycle parking facilities. Main access routes appear to be the Geldersekade (with the sometimes chaotic crossing with Prins Hendrikkade) and the Piet Heinkade. It seems that people cycling to and from Central Station are somewhat more likely to live in the eastern part of the city.

There’s one caveat though: the numbers are small. Even the busiest segments represent at most 40 rides. One loyal Fietstelweek participant recording her commute during the entire week could literally change the map.

Strava has far larger numbers, but its data raises different kinds of questions. Strava calls itself ‘the social network for athletes’ and wants to know if you use a road bike, a mountain bike, a TT bike or a cyclocross bike (no option ‘other’ available). So how representative is Strava data of people who use their city bike for commutes and other practical purposes?

Strava’s response to such questions is that they’re trying to make the app less competition-focused and more social, with Facebook-like features. This should help them collect data about ‘normal’ bike rides. They have also argued that «especially in cities, those with the app tended to ride the same routes as everyone else».

But is that really true? Strava’s heatmap (choose red and rides) for Amsterdam could perhaps be interpreted as a combination of recreational rides (Vondelpark, Amstel) and cyclists trying to get in or out of the city as quickly as possible (plus quite a few people who recorded their laps at the Jaap Eden ice skating rink as bicycle rides).

Perhaps you could find a way to filter out ‘lycra’ rides and end up with a sufficient number of ‘normal’ rides. Then again, almost three-quarters of bicycle rides in the Netherlands are under 3.7 km, and I suspect very few of those short rides end up on Strava.

There’s also a socio-economic aspect. It has been argued that Strava is used most by people living in wealthier neighbourhoods, which aren’t necessarily the neighbourhoods most in need of better cycling infrastructure.

Of course, bicycle use is unequal in the first place, which is also reflected in Fietstelweek data. The map below shows the start and end points of rides for Amsterdam.

Density is highest in the area within the ring road and south of the IJ. The number of trips per 1,000 residents also correlates with house values: more bicycle trips start or end in affluent neighbourhoods. As said, this probably reflects actual patterns in bicycle use and not a problem of the data.

To summarise, Fietstelweek has smaller numbers than one would like, while Strava data raises questions about representativeness. One way for Strava to help answer these questions would be to make a subset of its Amsterdam data available as open data.

This Python script shows how the analysis was done.

Амстердам, mapped by the Soviet Union

For fifty years, the Soviet Union had an ambitious military programme to map large parts of the world. Two collectors, John Davies and Alexander Kent, have written a great book about the secret maps that resulted from this programme.

Most of the maps in their book are of the US and the UK. Their descriptions are so intriguing that I had to find out whether Amsterdam has also been mapped. It turns out it has: on Ebay, I found a reproduction offered for sale by the Jana Seta Map Shop in Riga.

The map consists of four sheets, each more than a metre wide and 90cm high. The left margin of the fourth sheet contains the following text:

If I’m not mistaken, this text contains the following information: the scale of the map; the name of the city; the reference numbers of the 1:100,000 maps with the location of the city; the sheet number (4/4); the status of the map (SECRET) and the year of publication.

So it appears that the map was published in 1985, but that’s not the whole story. At the bottom of sheet 4, the following text is printed:

Sometimes, this text would contain the names of the people who had created the map (frequently women), but that’s not the case here. I think it says here that the map was compiled in 1972 and updated with material from 1980. By the way, a description of Amsterdam printed next to the map also refers to demographic information from 1981.

To check how recent the material is, I created a map of Amsterdam with buildings from 1980 and 1981 colour-coded. This shows that in those years, most construction took place to the south-east of the city, beyond the Bijlmer neighbourhood. The fragment below shows the area between the Academic Medical Centre and the Gaasperplas (click on the image to open it in a new screen).

For comparison, here’s roughly the same area from the Soviet map.

Generally, buildings from 1980 (orange) and before are shown on the Soviet map, while buildings from 1981 (red) are not. This seems to confirm that the map was updated with material from 1980.

The cartographers who created the maps used satellite images, local maps and other public sources, and sometimes information collected on the ground.

Some maps show new buildings but without the corresponding street names. The explanation may be that cartographers had access to recent satellite images showing the buildings, but no local maps were available yet from which the street names could be taken, Davies and Kent explain.

Something similar may apply to the Bijlmerbajes, a former prison which now houses a refugee centre.

The Bijlmerbajes opened in 1978. The map shows the prison buildings: they are east of the tracks, with a ditch in between. However, there’s no explanatory text: the only text is the name of metro station Spaklerweg. It appears that the cartographers did have access to recent satellite images showing the buildings, but no information about their function.

I don’t know whether any Dutch maps which identify the Bijlmerbajes existed in 1980. The Dutch Land Registry has a handy website with historical maps. It contains a map from 1981 identifying the Bijlmerbajes as gevangenis (prison).

The prettiest parts of the map are the harbours, that have been mapped in great detail. That’s not the case for Schiphol Airport, shown below.

The maps used a uniform colour coding. Simply put, green represented objects of military or strategic interest; purple, public institutions and black, manufacturing. Schiphol-Centrum (to the left) and Schiphol-Oost, with an aircraft repair shop (top right) have been marked as objects of strategic interest. Black blocks refer to the now closed Fokker factory, where military aircraft were produced

It’s striking how ‘empty’ Schiphol is. To some extent this is understandable: asphalt and wasteland make up a large part of any airport. However, it also appears that the cartographers didn’t have all that much information about Schiphol. For example, there used to be a depot for jet fuel (which was still delivered in barges) at Schiphol-Oost. If the cartographers had been aware of this, they would probably have included that information in the map.

Moving on to the harbours, here’s a part of the Western Harbour Area.

The Western Harbour Area contains one of the largest petrol harbours of the world. The green objects suggest the cartographers were rather interested in fuel infrastructure.

And here’s part of the Eastern Harbour Area.

There’s a lot to see here. The green triangle with number 29 represents the naval complex at Kattenburg (it has recently been abandoned by the Navy and will be converted into offices and housing). Interestingly, the square at the bottom of the triangle has also been marked as object of strategic interest. In the past this used to be a Navy warehouse, but it was turned into a Maritime Museum in 1973.

Other green objects include the Oranje-Nassau barracks at the Sarphatistraat (number 30, still in use by the army at the time) and the former location of the Nautical College (number 301).

There’s also a little green block between the Waterlooplein and the Nieuwe Amstelstraat (number 5 to the left of the photo). According to the map index, this is an арсенал or arsenal. In a way, that’s correct: the buildings name is Arsenal. The name refers to the fact that the building has been used to store arms in the past, but since 1946 it houses the Academy of Architecture.

Lovers of detail may want to zoom in to the Czaar Peterstraat. Soviet army maps used to write names phonetically, following the local pronunciation. The fact that this streets name has a Russian origin doesn’t change that: the tsar’s name is spelled Peter (Петер), not Pyotr (Пётр).

And here’s yet another strategic location, near the Museumplein.

Objects in this fragment include the American Consulate (number 166, but in a different building than where you’d expect it to be) and a bus stop where KLM busses to Schiphol Airport used to depart (number 187).

Of interest is number 250, located next to the Zuiderbad indoor swimming pool. The object is green, therefore deemed of strategic interest. The description says Служба безопасности or security service, according to Google Translate. That’s intriguing. Could it be that the map reveals an unknown location of the national security service BVD?

Not quite. This used to be the address of a precursor of the Dutch NIA (now part of TNO), an institute that dealt with workplace health and safety. Its former name was Veiligheidsinstituut or Safety Institute. However, the Dutch word veiligheid can mean both safety and security, which explains how the Soviet cartographers could have mistaken the Veiligheidsinstituut for a security service.

Details about Amsterdam

The Soviet city plans come with a general description of the city. To give an idea of the contents, here are some elements from the description of Amsterdam:

  • Because of dikes, rivers and canals and because of the viscous soil, movement of vehicles outside of the roads is almost impossible.
  • The destruction of hydraulic structures can cause catastrophic flooding of the terrain.
  • Along the roadside there are bicycle paths with a width of up to 2m.
  • All nearby settlements are electrified, provided with telephone communication, and have running water and gas.
  • From the air, Amsterdam is easily recognisable by its large size and its location between the IJsselmeer and the North Sea.
  • On some canals, there are many floating houses.
  • The metro lines have a length of 18 km (3.5 of which are underground) and number 20 stations, including 5 underground ones; the distance between underground stations is 0.8 - 0.9 km, between ground stations - 1.1–1.3 km

In addition, the text contains detailed information about manufacturing, research, administration and other topics.

Method

I once started to learn Russian, but I never progressed much beyond я не говорю по-русски. To decipher Russian texts on the map, I used the Cyrillic keyboard of my iPhone for typing short pieces of text, and I scanned longer ones with the FineScanner app, which offers OCR for Cyrillic (this works as long as the text has a white background, but not with texts printed on the map itself). I used Google Translate to translate the texts. The result may not be perfect, but it appears to work pretty well.

I created the map with construction dates using Qgis and Open Street Maps map data, which contains data from the Land Registry (Kadaster).

I can’t rule out that my interpretation of the map (and the Russian texts on it) contains errors. If you have any comments, please let me know.

John Davies en Alexander J Kent, The Red Atlas: How the Soviet Union Secretly Mapped the World. University of Chicago Press, 2017.

Maps of other Dutch cities and detailed information here.

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