champagne anarchist | armchair activist

Road bikes vs city bikes: how Utrecht portrays its cyclists

In the build-up to the Tour de France Grand Départ on 4 July, Utrecht is organising activities to promote cycling and to present itself as a cycling city. For example, they’re publishing a series of portraits of Utrecht cyclists. What kind of bicycles do they have?

Out of 67 bicycles portrayed so far, no fewer than 26 are road bikes, and only 13 are city bikes. Among both categories, about half the bicycles are made of steel. The steel road bikes aren’t the ones with rusty chains you see in the streets (for example in Amsterdam), but well-maintained classic road bikes, including a mixte women’s bike, and hand-made designer bikes.

There’s also a velomobile (I’m not really into recumbents but this one looks cool), a Pedersen floating saddle bike and a bough bike.

Utrecht could have chosen to show lots of old city bikes, and a few newish Cortina or Sparta imitation cargo and grandma bikes with brown comfort saddles, which is more or less what Utrechters use for their daily trips (check the videos by Mark Wagenbuur). But of course, the point of the portraits isn’t to show the average Utrecht bicycle. Rather, they paint a picture of Utrecht cyclists as diverse, generally sort of hip people with fast bikes.

Method

Some portraits describe or show several bicycles. If they are of different types, I’ve counted them separately. The classification of bicycles isn’t always straightforward; for example, in a few cases it’s unclear whether a bicycle should be classified as touring or city bike. The analysis is based on portraits 100 through 40; the rest was yet to be published at the time of analysis. Download the data here.

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Why is the government counting the number of «new townspeople»

The research bureau of the Amsterdam government recently released a dataset about Amsterdam’s neighbourhoods, which contains over 20 variables that in some way deal with the ethnicity of local residents. The Netherlands has always had a somewhat dubious obsession with categorising people by ethnic background (not just on the basis of where they were born, but where their parents were born). Even so, I was a bit surprised by the category new townspeople (nieuwe stedelingen). People are considered new townspeople if they meet the following criteria:

  • Between 18 and 55 years old; and
  • Registered as a resident of Amsterdam after their 18th birthday; and
  • Either both parents were born in the Netherlands, or the person him- or herself or at least one of the parents was born in a Western country.

So who would invent such a weird category? A bit of googling reveals that the term new townspeople is associated with students and knowledge workers (but apparently not from India or Turkey) and that it’s used in combination with terms such as post-industrial economy, creative industry, Richard Florida, Bagels & Beans and pine nut sandwiches. In other words, new townspeople are associated with gentrification. In policy documents, a high share of new townspeople is seen as a positive sign for a neighbourhood.

Sociologist Jan Rath recently criticized the gentrification thing:

It’s become a controversial term, but administrators really do pursue a population policy in the city. Officially it’s a search for the right social mix in a neighbourhood, but in reality it really boils down to reducing the number of houses for the people with the lowest incomes.

In addition to that, local administrators apparently don’t think it’s awkward to measure the success of their policies by counting the number of new townspeople, a bureaucratic term for new residents who are not ethnic minorities.

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Amsterdammers like old canal houses and dislike 1950s architecture

The research bureau of the Amsterdam city government (O+S) has published an Excel file containing a wealth of data about Amsterdam’s neighbourhoods. Among other things, it tells us how beautiful Amsterdammers think houses in their neighbourhood are. The average ratings are shown on the map below.

According to locals, the most beautiful houses are to be found around the Leliegracht (rated 8.7 out of 10) in the western canal belt. The ugliest are at the messy margins of the city, for example around the Weespertrekvaart in the Omval neighbourhood.

It will hardly come as a surprise that there’s a pretty strong correlation between the value of houses and how beautiful locals think they are. Either Amsterdammers have a posh taste in houses, or beautiful houses are expensive because people are willing to pay more for them (probably it’s a bit of both).

It so happened I had recently come across a new dataset from Statistics Netherlands (CBS) containing data on the construction period of houses by 4-digit postcode. I linked this data to the O+S data (for the challenges involved see the Method section below). The scatterplot shows neighbourhoods by share of houses from a specified period, and rating.

A few conclusions can be drawn:

  • In neighbourhoods with a high share of historic (pre–1906) houses, locals tend to think houses are beautiful;
  • By contrast, in neighbourhoods with a high share of post-war (1945 - 1960) houses, such as the western garden cities, locals tend to be more critical of the houses in their neighbourhood;
  • And post–2011 architecture doesn’t appear to be very popular either.

My first reaction to these findings was disappointment in my fellow Amsterdammers. Mainly for these reasons:

  • They don’t seem to particularly appreciate the Amsterdam School architecture, which largely coincides with the 1906–1930 period (or there would have been a positive correlation between rating and the share of houses from this period);
  • On the other hand, they don’t seem to realise how ugly much of the 1980s architecture really is (otherwise you’d expect a negative correlation between rating and share of houses from the 1980s).

A deeper dive into the data resulted in a somewhat more nuanced view. For some of the neighbourhoods, data is available at a more detailed level than the level I used in my analysis.

As for the Amsterdam School: a pretty sensational example is the Tellegenbuurt in the neighbourhood Diamantbuurt, which gets a mediocre 7 out of 10 rating (just above the median rating of 6.9). However, the more detailed data shows that at least the western part of the Tellegenbuurt gets a somewhat better 7.4. Similarly, the iconic het Schip housing block is in the Spaarndammer- and Zeeheldenbuurt, where locals rate the houses a 6.9, but the western parts of the Spaarndammerbuurt proper get a rating of 7.5.

I still think Amsterdammers undervalue the 1906–1930 period, but at least they do seem to show some appreciation for some of the most-acclaimed highlights of the period.

As for the 1980s: this was a period of urban renewal. It resulted in dull housing blocks in otherwise decent-looking neighbourhoods such as the Dapperbuurt, the Oostelijke Eilanden and the eastern part of the Indische buurt. This mixture may explain why these neighbourhoods don’t necessarily get very low ratings.

Method

The ratings of houses were collected in 2013, by asking the question «How do you rate the houses in your neighbourhood? (1=very ugly, 10=very beautiful)». The O+S file containing these ratings is available here and the CBS file containing data on period of construction here.

The main challenge consisted in linking the two datasets. Fortunately, the CBS also has a file containing neighbourhood data with the most prevalent 4-digit postcode (and also information on the share of houses that have that postcode). The link between postcode and neighbourhood is imperfect but not too bad. For example, in 57 out of the 97 neighbourhoods in my final analysis, over 90% of the addresses have the postcode associated with the neighbourhood.

Somewhat surprisingly, the O+S spelling of neighbourhoods is in some cases slightly different from the CBS (why?!). For example, Bijlmer oost (e,g,k) versus Bijlmer-Oost (E, G, K). I created a separate table to link the different spellings.

I used R to merge the files and check for correlations between share of houses from a specific period and rating of the houses (code on Github). One shouldn’t expect too strong correlations for two reasons: first, the share of houses from a certain period will be at best just one among many factors that have an influence on rating and second, because of the noise created by the imperfect link between postcode and neighbourhood.

For share of pre–1906 houses there was the strongest correlation with the rating of the houses (.51). For 1945–1960 the correlation was -.32 and for post–2011 it was -.39. There was an even weaker, but still statistically significant, correlation for the 1960s (-.22).

I initially created a map with Qgis, but then I decided the map needed some interactivity. I created a new version with Leaflet and D3, using this tutorial to figure out the basics of Leaflet and how to combine it with D3. The initial result wasn’t pretty, but then I found the black and white tiles by Stamen (better than the OSM black and white) and now I think it looks better (although I guess maps overlaid with a choropleth will always look a bit smudgy).

A new balance in Amsterdam’s city council?

Last autumn, Amsterdam politicians discussed on Twitter whether the relations between coalition and opposition have changed since the March 2014 election, which resulted in a new coalition.

One way to look at this is to analyse voting behaviour on motions and amendments over the past two years. From a political perspective, proposals with broad support may not be very interesting:

For example, a party can propose a large number of motions that get very broad support, but materially change little in the stance, let alone the policy, of the government. In the litterature, this is sometimes referred to as «hurrah voting»: everybody yells «hurrah!», but is there any real influence? (Tom Louwerse)

In a sense, it could be argued that the same applies to proposals supported by the entire coalition. More interesting are what I’ll call x proposals: proposals that do not have the support of the entire coalition, but are adopted nevertheless. In the Amsterdam situation these are often proposals opposed by the right-wing VVD. The explanation is simple: Amsterdam coalitions tend to lean to the right (relative to the composition of the city council). As a result, left-wing coalition parties have more allies outside the coalition.

Let’s start with the situation before the March 2014 election. The social-democrat PvdA was the largest party. The coalition consisted of green party GroenLinks, PvdA and VVD, but the larger left-wing parties PvdA, GroenLinks and socialist party SP had a comfortable majority. The chart below shows the parties that introduced x proposals. The arrows show who they got support from to get these proposals adopted.

The size of the circles corresponds to the size of the parties; pink circles represent coalition parties. The thickness of arrows corresponds to the number of times one party supported another party’s x proposal. The direction of the arrows is not only shown by the arrow heads but also by the curvature: arrows bend to the right.

The image is clear: PvdA and especially GroenLinks were the main mediators who managed to gain support for x proposals.

And now the situation after March 2014. By now neoliberal party D66 is the largest party and the coalition consists of SP, D66 and VVD. This means that PvdA and GroenLinks are now opposition parties, but it turns out they still play a key role in getting x proposals adopted. GroenLinks initiated as many as half the x proposals.

The most active mediator is Jorrit Nuijens (GroenLinks), followed by Maarten Poorter (PvdA) and Femke Roosma (GroenLinks).

Method

Data is from the archive of the Amsterdam city council. Votes on motions and ammendments as of January 2013 can be downloaded as an Excel file. The file (downloaded on 31 January 2015) contains data on 1,165 (versions) of proposals, put to a vote until 17 December 2014.

A few things can be said about the Excel file. On the one hand, it’s great this information is being made available. On the other hand, the file is a bit of a beast that takes quite a few lines of code to control. The way in which voting is described varies (e.g., «rejected with the votes of the SP in favour», «adopted with the votes of the council members Drooge and De Goede against»); the structure of the title changed in November 2014; Partij voor de Dieren is sometimes abbreviated and sometimes not; and sometimes the text describing voting has been truncated, apparently because it didn’t fit into a cell. Given the complexity of the file, it can’t be exluded completely that proposals may have been classified incorrectly.

The analysis (by necessity) focuses on visible influence. The first name on the list of persons introducing a proposal is considered as the initiator. In reality, it will probably sometimes occur that an initiator will let someone else take credit for a proposal.

The code for cleaning and analysing the data is available here. The D3 code for the network graphs is based on this example.

Peak economist

On Friday, the New York Times published an interesting article by Justin Wolfers about the kind of experts the paper mentions. Don’t worry, he’s aware of the methodological issues:

While the idea of measuring influence through newspaper mentions will elicit howls of protest from tweed-clad boffins sprawled across faculty lounges around the country, the results are fascinating.

To summarize: by his measure, economists have become the most influential profession among the social sciences and their influence rises during economic crises. Or at least so in the New York Times. I looked up data for the Dutch newspaper NRC Handelsblad, which has data available from 1990.

Some conclusions can be drawn:

  • The current ranking is the same as for the NYT, with economists heading the list and demographers at the bottom;
  • Apparently, NRC Handelsblad has always had a pretty high regard for historians, but due to the crisis they lost their top position to economists;
  • There was a peak in mentions of psychologist in 2012, but some of that can be ascribed to reports of scientific fraud by psychologist Diederik Stapel.

For comparison, I tried reproducing Wolfers’ NYT chart for the years 1990 - 2014. Here’s what I got:

The sudden increase for all professions in 2014 is unexpected - see Method for possible explanations. If we leave 2014 aside, what emerges is that «peak economist» (to borrow an expression from Wolfers) seems to have happened earlier in the NYT than in NRC Handelsblad. Perhaps something to do with the fact that the crisis hit the US earlier than Europe.

Method

The NYT data were downloaded from the NYT Chronicle Tool (I had to separately download the data for each search term). Data from NRC Handelsblad were downloaded using the website’s search function. In order to get the total numbers per year I also did a search using «de» («the») as a search term («de» is the most frequently used word in written Dutch).

As indicated in the article, I got a steep rise in the percentages for all professions in the NYT in 2014. I manually checked some of the percentages I got against those in the chart of the NYT Chronicle Tool, and these appear to be correct. The spike is not visible in Wolfers’ chart, but that may be due to the fact that he uses three-year averages.

There may be an issue with the denominator, i.e. the total number of articles. The number for total_articles_published in the data I downloaded from the NYT was pretty stable at about 100,000 between 1990 and 2005. Then it rose to about 250,000 in 2013 (perhaps something to do with changed archiving practices, or with online publishing?). However, in 2014, it dropped to about one-third of the 2013 level.

The NRC Handelsblad data also has some fluctuations in the total number of articles per year, but less extreme and at first sight they don’t seem to coincide with unexpected fluctuations in the percentages of articles mentioning professions.

Code is available here.

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Are the social-democrats getting enough seats in the Dutch Senate

This weekend, the Dutch social-democrat PvdA will decide on the list of candidates for the Senate election this spring. The party isn’t doing too well in the polls, but it may be facing an additional problem, as the charts below illustrate.

Since the beginning of the 1980s, the PvdA has nearly always had a weaker position in the Senate than in the Lower House. The main exception is 2002, when the Lower House election took place within days after the murder of rightwing populist Pim Fortuyn and the PvdA, seen by many as a symbol of the establishment, temporarily lost half its seats.

The relatively weak position of the PvdA in the Senate may be a coincidence, but it could also be related to turnout. In elections for the provincial councils, which in turn elect the Senate, almost half the voters stay at home (compared to a 75–80% turnout in Lower House elections). It may well be that the way in which the Senate is elected has a negative impact on the outcome for the PvdA.

Sources

Data from the Election Council and Wikipedia (e.g., EK and TK). Data and script are available here.

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Sevillanas. The Spanish punk

Update 11 January: Spotify data added.
According to the English Wikipedia page, «Generally speaking, a sevillana is very light hea[r]ted, happy music». There’s certainly some bland stuff around, but many sevillanas are explosive and raw. In fact, sevillanas are the punk of Spanish music.

I wanted to back this claim up by pointing to the length of the songs on the legendary Sevillanas de los Cuarenta album. It’s a known fact that punk is a genre with very short songs: on average 2:58 according to this analysis by blogger Dale Swanson. It’s the shortest of all the genres he analysed. Well, the average song length on the Sevillanas de los Cuarenta album is 2:44.

However, there may be some problems with this argument. First, some of the songs on the album have a haunting quality about them (for example, A flamenca no me ganas by Gracia de Triana), which makes you wonder if they haven’t been played too fast when they were recorded for CD. This may be an issue, but even if you correct for this the songs on Sevillanas de los Cuarenta would still be shorter than punk songs (for details see below, Method).

More problematic is the fact that short songs appear to have been normal in the 1940s. According to this analysis by Rhett Allain, average song lengths rarely exceeded 3 minutes until the end of the 1960s (see also the debate in the comments on possible explanations). So the shortness of the songs on the Sevillanas de los Cuarenta album isn’t that impressive. In fact, a (possibly non-representative) sample of 1970s sevillanas has an average song length of 3:22, which appears to be quite typical for the 1970s judging by Allain’s data.

The Musicbrainz database used by Allain doesn’t seem to contain many sevillanas. However, the Discogs website, which has data on millions of songs, does contain a few hundred sevillanas. Since posting the first version of the article, I realised metadata can also be obtained from Spotify. Spotify has over 2,500 songs with «sevillanas» in the title but only a few hundred songs per genre for other genres (probably the genre tags aren’t applied consistently). Below is the song length of a number of genres in the Discogs and Spotify databases.

For especially jazz and house, Spotify has other durations than Discogs. Other than that, median song durations are very similar. This is actually quite remarkable given the differences between the datasets. In both datasets, sevillanas tend to be somewhat longer than punk songs, but shorter than the other genres in the analysis.

An analysis by year might be interesting, but tricky: first because the release year in the Discogs data may refer to the year in which an album or song was re-released and second because the number of sevillanas tracks with sufficient information isn’t large enough for that level of precision. The Spotify dataset has no information on the release year of tracks (I guess if I really wanted I could have looked up the release date of the album each track is on).

All in all, the average sevillanas may be somewhat longer than a punk song. But you can still argue that a sevillanas song is in fact a series of even shorter songs, as illustrated by the plot of ¡Ay Sevilla! by Los de la Trocha shown above. The typical sevillanas is a series of short bursts of music that can be as abrupt as any punk song.

Method

Scripts for the analyses are available here.

Songs on Sevillanas de los Cuarenta too fast?
Spotify has three versions of A flamenca no me ganas: the one from Sevillanas de los Cuarenta (2:29 on cd) and two others lasting 2:37 and 2:41. This suggests it’s possible that the «correct» version is up to 8% longer than the one on Sevillanas de los Cuarenta. Even if you assume all the songs on the album should last 8% longer, the average length would become 2:56, still less than for punk. On the other hand, it’s doubtful that all songs on Sevillanas de los Cuarenta are too short. For example, Sevillanas del Espartero by Concha Piquer lasts 2:57 on Sevillanas de los Cuarenta, but Spotify has versions lasting only between 2:27 and 2:35.

1970s sevillanas
The sample of 1970s songs is from albums C, D and F of the HISPAVOX Sevillanas de Oro collection (cd versions), containing songs by los Marismeños, Amigos de Gines and others (not all Sevillanas de Oro albums contain the release year of the songs, but these do).

Discogs data
The Discogs data are available through an API and as monthly data dumps. I thought I’d spare myself the trouble of figuring out how the API works, so I opted for the data dump (the one for 1 December 2014). The downside is that the data is 2.8 GB zipped and 19.2 GB unzipped, so downloading and analysing the data takes a while.

The data dump is xml (the API should return json). I’m not really familiar with xml so I used some not very sophisticated, but effective, regex to sort it out. The data is organised in releases (e.g., albums) that have tags (e.g., for the year in which it was released and for genres and styles). The releases contain tracks that have their own tags, including duration. In order to filter out excessive track lengths I ignored any release containing the string mix and tracks with a duration longer than one hour.

Discogs uses hundreds of genre and style tags including some quite specific ones like ranchera and rebetiko, but not sevillanas. I decided to include only tracks with sevillanas in the title. This will exclude some legitimate sevillanas, but I reckon there probably won’t be too many false positives.

Spotify data
I accessed the Spotify data through their web api. As indicated in the article, genre searches resulted in only a few hundred results per genre, which suggests these tags are often omitted.

Plotting a waveform
Based on this discussion, plotting a waveform from a .wav music file using Python should be simple, but saving the plot turned out to be a problem (googling the error message OverflowError: Allocated too many blocks taught me I’m not the only one having that problem but I didn’t find a solution that worked for me). Instead I turned to R and found that the tuneR package will let you read and plot .wav files without a problem.

As trade unions consider merger, the Dutch want their unions to take a much tougher stance

A large majority has voted in favour of the merger - A plan to create a new Dutch union with about 1 million members was put on hold in October, when the plan just failed to get a two-thirds majority at the convention of FNV Bondgenoten, one of the unions involved in the merger plans. A new vote will take place on 26 November.

Representatives of employers’ organisations expressed disappointment at the initial rejection of the merger. They had been hoping the merger would result in a stable trade union that will play a constructive role in the elaborate social dialogue institutions of the Dutch «polder model».

In fact, that’s exactly what Dutch unions have been doing over the past decades, as evidenced by their low strike rates. But with growing inequality and an erosion of the welfare state going on, doubts arise whether social dialogue is enough. Some groups of workers, like cleaners and health care workers, have successfully resorted to more assertive campaign methods to fight for decent pay and better working conditions.

Since 2007, researchers of the University of Tilburg have been asking a panel of about 6,000 respondents what they expect of unions. More specifically, they have asked respondents whether they agree that «Trade unions should take a much tougher political stance, if they wish to promote the workers’ interests». In the latest edition of the study, 44% (strongly) agree and only 13% (strongly) disagree.

If anything, support for tougher unions seems to have grown over the past years. Surprisingly, even among the self-employed and among people who voted for neoliberal parties like VVD and D66 in 2012, more respondents agree than disagree that unions should take a much tougher stance. High-income respondents are among the few groups that are not so keen on tougher unions.

Last weekend, chairman Ton Heerts explained the position of the FNV to the Telegraaf newspaper: «I think we’ve proven over the past year that it’s quite possible to combine substance, dialogue and action. With the current wave of right-wing policies, the emphasis will be more on actions. That’s fine.»

An earlier version of this analysis was published here

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Second jobs - job erosion or appetite for consumption

Last year, a spokesperson of the German federal government suggested the explosive growth of Zweitjobs (second jobs) could have various explanations. Yes, people may be forced to take on second jobs out of financial necessity and because of the flexible labour market; but it could also have something to do with an increased «appetite for consumption» (Konsumlust). The suggestion immediately resulted in 2,000 sarcastic tweets.

The Netherlands has also seen a substantial growth in the number of people with second jobs, as new data from Statistics Netherlands illustrate. The chart shows the total number of employees (blue); employees with non-permanent jobs such as temp jobs and zero-hours contracts (red) and employees with a second job (green, all index 2002 = 100).

The green and red lines show a quite similar pattern. One might try arguing that the crisis caused a dip in the appetite for consumption, but more likely there’s a broader pattern of job erosion going on, temporarily slowed down when employers shedded their «flexible skin» (Dutch jargon for the precarious workers employers use) during the crisis.

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Scooters often faster than cars

Minister Schultz wants to allow Amsterdam to ban scooters from cycle paths and make them use the road, wearing a helmet. This should make cycle paths safer for cyclists and reduce their exposure to air pollution. However, car and scooter lobbyists argue that the speed difference between scooters and cars is too large for scooters to ride safely on the road, with motorists driving 50 kmph.

So do motorists really make 50 kmph in Amsterdam? «Cycling professor» Marco te Brömmelstroet has tweeted a map showing rush hour speeds far below 50 kmph.

As part of its open data initiative, Amsterdam has released some 5 million speed measurements at the «Hoofdnet Auto» (the network of major roads for cars) during the month of January 2014. The histogram above shows that even at these main roads, the majority of measurements recorded a speed below 50 kmph, with a median speed of 31 kmph. Average speeds during afternoon rush hour were about 5 kmph lower than at night.

A 2011 study by cyclists’ organisation Fietsersbond found found an average speed for scooters on Amsterdam’s cycle paths of 36.9 kmph. The map shows roads where motorists drive on average at least 36.9 kmph (thin red line) or 50 kmph (thick red line). Note that the method by which the Fietsersbond measured scooter speed may be different from the method used to measure car speed.

There have been jokes that scooter riders don’t want to use the road because this would force them to reduce their speed. The data of the Amsterdam government show there’s actually some truth to this.

Scripts for processing the data can be found here.

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