A tweet by artist, designer and developer Jill Hubley drew my attention to a traffic flow map of London, created by Bauhaus architect Ludwig Karl Hilberseimer. The map shows the number of buses passing through London’s central arteries in one hour. «The traffic diagram of London shows both the typical congestion in the center and the lack of transportation facilities at outlying points,» he commented. Hilberseimer thought the solution to this transportation problem was to decentralise the city, by creating satellite cities with a population of at most 100,000.
Hubley has been tweeting numerous historical traffic flow maps, including a beautiful 1944 map showing transport along the waterways of Belgium and the Netherlands. What struck me about the Hilberseimer map is its similarity to a series of maps in a traffic study published by the city of Amsterdam in 1976. These maps were discovered by Marjolein de Lange of cyclists’ organisation Fietsersbond, and have been reproduced in the book Bike City Amsterdam she wrote with Fred Feddes.
The Amsterdam maps illustrate how cycling had declined in Amsterdam between 1961 and 1971, and how rising car use had created a congestion problem. It wasn’t until later that the city developed measures to promote cycling, as analysed in Bike City Amsterdam. I tried to create a 2016 version of the cycling map using Fietstelweek data, but it should be noted that the cycling routes of Fietstelweek participants may not be representative of overall bicycle traffic in Amsterdam.
Compared to Hilberseimer’s map, the maps created by the city of Amsterdam have a very clean design: all cartographic details that do not represent traffic data have been omitted. And then there’s the elegant legend. Hubley has tweeted a Swedish traffic flow map from 1977 with a similar type of legend, as well as a map of Florida from 1952, a map of St. Paul - Minneapolis from 1949, and a 1945 map of eastern Germany with a horizontal version of the legend. (Update - interesting variant on this 1963 Lincolnton map.) I wonder whether earlier examples exist.
Were the flow maps in Amsterdam’s traffic circulation plan inspired by Hilberseimer’s Traffic Diagram of London? Possibly, but Hilberseimer wasn’t the first to create a traffic flow map. In fact, both Amsterdam’s map makers and Hilberseimer are indebted to a map created a century before Hilberseimer’s map, by the French civil engineer Charles-Joseph Minard.
In her book The Minard System, visualisation strategist Sandra Rendgen comments:
In this revolutionary map, created in the middle of a debate about where to project the railroads between Dijon and Mulhouse in eastern France, Minard analyzed the street traffic on preexisting roads in the region.
Apparently, the map was so influential in shaping the debate that a fake copy was made ‘in an attempt to prove another route to be more promising’.
Rendgen describes how Minard initially created bar charts to represent traffic along segments of a route. At some point, he decided to project these graphs onto a map, which resulted in the creation of the flow map. Over time, Minard’s flow maps gained in complexity, as he used colour to represent different types of data. Minard is sometimes credited with inventing the flow map, but Rendgen points out that the design was possibly invented more or less simultaneously in Ireland, France and Belgium.
Minard’s charts and maps often contain detailed descriptions of the data and methods he used. He collected data from a range of sources, and emphasised that graphs should accurately represent the data. On the other hand, he was willing to sacrifice geographic detail or accuracy for clarity. Rendgen points to the ‘clean and minimalist aesthetics’ of his work, devoid of decorations or other clutter. It is no wonder that Edward Tufte, the renowned proponent of clutter-free data visualisation, described Minard’s work as an example of ‘graphical excellence’ (in The Visual Display of Quantitative Information).
A recurrent theme in Minard’s explanatory notes is that he aimed to make relationships quickly apparent to the eye. One of these notes has an almost futurist sense of modernity to it: «The figurative maps are thoroughly in the spirit of the century in which one seeks to save time in all ways possible.»
One could argue that the 1976 Amsterdam traffic flow maps are true heirs to Minard’s approach, and especially to his first, monochrome flow map reproduced above. As Rendgen notes, Minard’s map is «extremely stripped down; it features barely any landscape details other than a network of local place names and rivers». Even those subtle geographical hints have been omitted from the Amsterdam traffic flow maps. Of course, this only works because of the very recognisable pattern of Amsterdam’s streets.
Airbnb is not exactly keen to share data that might help analyse its impact on local housing markets. In 2016, the Amsterdam Municipality decided to collect Airbnb data using a scraper - a computer programme that automates the job of retrieving information from web pages.
Amsterdam is not the only government to use web scraping. Increasingly, this technique is used to obtain data about topics ranging from consumer prices to jobs vacancy statistics and business data. Collecting data from the internet has advantages, but it also poses some challenges. It may be difficult to aggregate data coming from different websites, and data found online may not cover all aspects of a phenomenon you’re trying to understand (for example, not all job vacancies are published online). On a more practical level, your web scraper code may break when websites change.
In March 2017, Amsterdam reported that its weekly scrapes of major platforms like Airbnb required little maintenance. But last week, it sent a report to the city council describing how Airbnb has been making changes to its website - perhaps in an attempt to frustrate efforts to collect information about its business practices. Initially, Amsterdam’s digital surveillance department succesfully updated its scraper, but following new changes to the Airbnb website since May 2018, Amsterdam now appears to have given up on scraping Airbnb.
This made me curious about the technical characteristics of the Airbnb website. Here are some observations, based on an (admittedly superficial) examination:
While it appears that barriers to scraping the Airbnb website may be surmountable, it’s quite possible that I underestimate what this would take. If you’d actually build a scraper and would use it to frequently collect information about all local listings, all kinds of new problems might arise.
Meanwhile, other sources of Airbnb data are available. In a previous post, I used data made available by Tom Slee and by Murray Cox’ Inside Airbnb. Slee has since stopped updating his data, but Inside Airbnb is still active. As the Amsterdam Municipality notes in its report, Inside Airbnb has succesfully adapted its scraping technique each time Airbnb changed its website.
UPDATE 13 May - See comments on Twitter: Jens von Bergmann from Vancouver also has a scraper that is working. Following some requests, Tom Slee recently updated his scraper; his code is available on Github.
Bike City Amsterdam, a new book by Fred Feddes and Marjolein de Lange, recounts how Amsterdam developed a cycling policy (more on the book below). An important source for the book is the archive of the Amsterdam branch of cyclists’ organisation Fietsersbond. In addition, traffic data was used to analyse trends.
An interesting dataset consists of counts of the number of cyclists, cars and other road users moving into and out of Amsterdam’s city centre, over the years 1980–2009. Most of the locations where traffic was counted are on the Singelgracht, which encircles Amsterdam’s city centre.
The data represents manual counts on a single day, between 7am and 7pm, of traffic in both directions.
I was asked to think about a way to visualise this dataset, which posed an interesting challenge (and was a lot of fun to do). Below, I’ll discuss a few of the options we considered.
Given the geographical distribution of counting locations, it seemed to make sense to try a circular chart design. In fact, that idea had also occurred to the city’s infrastructure department. In a 2007 fact sheet, they used a radar chart (or cobweb chart) to visualise the Singelgracht bicycle counts.
Incidentally, they didn’t use the term radar chart, but called it a fan (waaier). They used a bicycle metaphor to describe how it works: «from the middle, the counting locations around the city centre are connected like spokes in a bicycle wheel».
The chart looks really nice, but this chart type also has a drawback: there’s an implicit suggestion that the area within the purple line represents the number of crossings, which is in fact misleading (see this article for a discussion of a similar problem). Another limitation is that the chart doesn’t show how bicycle traffic changed - although it would be possible to make a version with separate lines representing 1980 and 2009.
As an alternative, I created what I’ll call a radial lollipop chart (to my knowledge, this chart type didn’t exist yet). The chart library that I use, D3.js, doesn’t seem to have a method to draw the ‘spokes’, or at least I couldn’t find it. Therefore, I wrote a function that calculates the start and end points of the lines. I had long forgotten how to use sine and cosine, so I had to look that up. I’ve published the code here.
Here’s a radial lollipop chart showing how cycling has increased at virtually all the Singelgracht crossings.
And here’s one showing the opposite effect for cars:
I love it when a chart has data points that break out of the chart area - although this is perhaps a bit extreme. The outliers are due to the fact that a large share of car traffic uses the Wibautstraat - IJtunnel route. I could have changed the scale to include those outliers, but then changes on other routes as well as changes in bicycle use would have become much more difficult to discern.
I rather like the radial lollipop chart, but it has a limitation: it shows changes between 1980 and 2009, but not when those changes happened. Car use started to go down before cycling really started to increase, but from the radial lollipop chart you couldn’t tell.
This is why the chart used in the book is an area chart, with colours corresponding to the broad geographical orientation of the crossings. Simple, but effective. And if you want to explore the details, click here for a draft version of the charts: bicycle, car.
On 4 April, the Amsterdam branch of cyclists’ organisation Fietsersbond has handed over its archive to the Municipal Archive. Marjolein de Lange, who coordinated a volunteer project to prepare the archive, came up with the idea to use the material as input for a book - a project she carried out with author Fred Feddes.
This resulted in a very interesting book about activism versus cooperation; the place of cycling in urban planning; and how the magic power of Amsterdam’s cycling culture decided the epic fight for the right to cycle through the passage under the Rijksmuseum. The book, which contains a wealth of great photos; maps and posters, is a must-read for anyone interested in cycling, Amsterdam, or activist poster design. It’s been published both in Dutch and in English. There’s also an exhibition at the Municipal Archive (until 30 June, Vijzelstraat 32, access is free).
In an article for the recently created Data Visualisation Society, R.J. Andrews suggests using a jagged baseline to indicate a broken y-axis (i.e., an axis that doesn’t start at zero). The idea - inspired by some beautiful charts dating back to WWI - is to suggest that the bottom part of the chart has been torn off. I like the idea - but I found it isn’t easy to implement.
Contrary to the view of some chart fundamentalists, using a y-axis that doesn’t start at zero can be perfectly ok in some situations. Still, one might want to alert the reader that the zero line is missing. One way is to add a little zigzag or some other symbol to the y-axis, as shown here. And then there’s Andrews’ suggestion to use a jagged baseline.
I tried to implement this in a chart that shows the number of flights at Schiphol Airport. For background: Schiphol has all but reached the cap of 500,000 flights per year, agreed on after negotiations between local residents and the aviation industry. There’s currently a heated debate on whether Schiphol should be allowed to grow further. Experts expect that maintaining the cap will result in more efficient use of the available slots (e.g. fewer short-distance flights, fewer low-cost flights, larger aircraft and fewer empty seats).
Creating a jagged baseline is a bit of a hassle: you have to remove the regular baseline, move the axis labels down a bit and create a new, jagged baseline.
And then there are some design issues. Having the baseline and the ‘regular’ chart lines look too similar may cause confusion. In fact, all of Andrews’ examples have very pronounced chart lines, which are clearly distinct from the baseline. If you prefer a more subtle approach, another solution is to use a light colour for the baseline.
Then again, it also matters whether there are gridlines. After some experimenting, I think the jagged baseline only works well with gridlines added; without them it looks a little weird. But see for yourself if you agree.
I’ve written a Python script to download and clean Schiphol Airport traffic data; find it on Github.
I’m rewatching The Wire. It’s a great series anyhow, but for researchers, episode 9 of the first season (2002) is especially interesting. It features detective Lester Freamon instructing detectives Roland Pryzbylewski and Leander Sydnor how to investigate the assets of drug kingpin Avon Barksdale.
They use microfilm instead of the Internet. They don’t have databases like Orbis, Companyinfo or OpenCorporates, and they don’t seem to calculate social network metrics. Yet the general principles behind Freamon’s methodology still make perfect sense today:
Start with the nightclub that Barksdale owns. Look up Orlando’s, by address, you match it, and you see it’s owned by - who?
Turns out it’s owned by D & B Enterprises. Freamon tells Prez to take that information to the state office buildings on Preston Street.
Corporate charter office.
They have the paperwork on every corporation and LLC licensed to do business in the state. You look up D & B Enterprises on the computer. You’re going to get a little reel of microfilm. Pull the corporate charter papers that way. Write down every name you see. Corporate officers, shareholders or, more importantly, the resident agent on the filing who is usually a lawyer. While they use front names as corporate officers, they usually use the same lawyer to do the charter filing. Find that agent’s name, run it through the computer, find out what other corporations he’s done the filing for, and that way we find other front companies.
This is pretty much the same approach you’d take when investigating shady temp agencies: trace connections via (former) shareholders, board members, company addresses and related party transactions. And, of course, try to figure out where the profits go.
On that aspect, Freamon also has some wisdom to share:
And here’s the rub. You follow drugs, you get drug addicts and drug dealers. But you start to follow the money, and you don’t know where the fuck it’s gonna take you.
As a self-taught programmer, I sometimes feel a bit uneasy about the code I write. Sure, it may work, but there’s probably a more efficient and more elegant way to do it. These doubts notwithstanding, I’ve just published my first Python package: limepy.
Its purpose is simple: it helps you process and summarise LimeSurvey data. LimeSurvey is a survey tool, somewhat similar to Surveymonkey. It’s different in that it’s open source, and probably more versatile.
If you download survey data as a csv, the answers to question types such as multiple choice questions or blocks of questions (‘arrays’) will be spread out over multiple columns. One task of limepy is to make sure all the data for a specific item end up in one table.
Limepy will also help you with a number of other tasks, like downloading survey data, creating a codebook, printing answers to open-ended questions and printing the answers of an individual respondent.
This is becoming a bit of a tradition: me writing about people who make a New Year’s resolution to quit Facebook. The story is simple: around the turn of the year, there’s a peak in people googling how to quit smoking, but there’s an even larger peak in people trying to figure out how to delete their Facebook account.
But this year, the story is a bit more complicated (and more interesting).
Google Trends data isn’t available yet for the last days of the year, so there’s no new peak in searches for “quit smoking” yet. Other than that, the yearly pattern is dwarfed by a huge peak in search volume for “delete Facebook” in the week starting on 18 March. What happened?
The Guardian has helpfully created an overview of Facebook-related incidents during 2018; I’ve added a few stories that also seemed relevant (for sources, see Method below; thanks to Vicki Boykis for the suggestion to annotate the Google Trends chart).
No surprise: the largest peak in “delete Facebook” searches happened a few days after the publication of the Cambridge Analytica story on 17 March. The news resulted in a veritable #deletefacebook campaign, although according to Mark Zuckerberg, «I don’t think we’ve seen a meaningful number of people act on that.»
Arwa Mahdawi has argued that deleting your Facebook account isn’t a bad New Year’s resolution, even though it probably won’t change how the company operates: «Facebook’s abuse of power isn’t a problem that we can solve as individuals. Technology giants must be regulated.»
So how much impact did the controversy have on Facebook? One way to try and answer this is to look at the share price.
The pattern for Facebook is rather interesting. The share price dropped after the publication of the Cambridge Analytica story, but quickly picked up again. But then it took a plunge on 25 July, resulting in ‘the biggest-ever one-day wipeout in U.S. stockmarket history’.
One possible interpretation is that investors initially thought the Cambridge Analytica story wasn’t going to harm Facebook’s profits. But when Facebook published its Q2 earnings report, they were shocked to learn that user growth had stalled.
But the chart also shows that all major tech companies saw their share prices go down. This suggests there’s more going on than users leaving Facebook. In addition to broader economic trends, a likely explanation is that investors fear more government regulation of major tech companies in response to the controversies they are involved in (and also to their dominant market position). While this may not be the whole story, it does seem to support Mahdawi’s view about the key role of regulation.
Note that Google Trends data should be interpreted with caution because Google doesn’t provide much detail on the methodology used to produce the data.
For periods longer than three months, only weekly data can be downloaded. For the 2018 chart I wanted daily data. As suggested here, I downloaded three-month batches with overlapping data and then used the overlapping dates to calculate a ratio to adjust the scales. Here’s the code:
import pandas as pd import numpy as np def stitch(df1, df2): df1.index = df1.date df2.index = df2.date overlapping = [d for d in df1.date if d in list(df2.date)] ratios = [df1.loc[d, 'delete facebook'] / df2.loc[d, 'delete facebook'] for d in overlapping] ratio = np.median(ratios) for var in ['delete facebook', 'quit smoking']: df2[var] *= ratio df = pd.concat([df1, df2[~df2.date.isin(overlapping)]]) return df df = dfs for df2 in dfs[1:]: df = stitch(df, df2)
I used this Guardian article as my main source on Facebook-related incidents in 2018. I added a few from other sources: in April, Facebook announced 87 million people had been affected by the Cambridge Analytica scandal. Subsequently, it announced that it would notify people who had been affected. Dutch comedian Arjen Lubach organised a Bye Bye Facebook event (reminiscent of the 2015 Facebook Farewell Party). In September, Pew found that one in four Americans had deleted the Facebook app from their phone; and later that month a Chinese hacker threatened to delete Mark Zuckerberg’s Facebook account.
If all goes well, the Dafne Schippers bicycle bridge in Utrecht should reopen on Monday, after a short closure for maintenance. I have a special affinity with this bridge: it opened on the day I started working in Leidsche Rijn, west of the Amsterdam-Rhine Canal, and it’s part of my favourite cycle route to work.
Who else use this bridge? With the usual caveats, data of the Fietstelweek can provide some insights. The charts below show, for each direction of traffic, at what time cyclists use the bridges across the canal.
There’s a morning peak in cyclists crossing the canal from Leidsche Rijn (west) to the city centre (east), and a peak in cyclists going the opposite direction around 5 pm. This suggests that the bridges are popular among commuters from Leidsche Rijn. That doesn’t really come as a surprise: if you cycle to Leidsche Rijn during the morning rush hour, you ride past huge numbers of cyclists going in the opposite direction.
The map below shows the routes of cyclists using the bridges. From top to bottom: Hogeweidebrug (or Yellow Bridge), Dafne Schippers bridge and De Meern bridge.
It appears that many cyclists use the bridges to go to the area around Central Station. Users of the De Meern and Dafne Schippers bridges tend to use nice routes that converge along the Leidseweg. Users of the Yellow Bridge use the not-so-nice route along Vleutenseweg, or the slightly better route along the railway track.
Research has shown that cyclists don’t always prefer the shortest route to their destination; the quality of the cycle tracks also plays a role.
Yet the map suggests that many cyclists opt for the shortest route, even if a nicer alternative is available. For example, few cyclists from the northern part of Leidsche Rijn seem to use the Dafne Schippersbrug, or the route along Keulsekade (the latter avoids long waits at traffic lights).
A nice map circulating on Twitter (here, here and here, via) shows where food delivery workers are organising. Many of their logos proudly feature bicycle parts. The Finland-based Foodora campaign is the exception; their logo appears to have been inspired by Alexander Rodchenko’s КНИГИ poster. Also note the elegant logo of Collectif des coursier-e-s / KoersKollectief.
While their fight is about the future of work, some of these groups are independent of established trade unions - and some don’t consider themselves trade unions in the first place. Riders have used wildcat strikes and other forms of direct action, as well as initiatives such as crowdfunding a strike fund. With employers like Deliveroo trying to «disrupt» the labour market, it makes sense that their workers don’t play by the rules either, it has been argued.
Unfortunately, I couldn’t find an example of the Swiss fiery backpack logo.
UPDATE - added logos from Scotland and Finland
Last week, city council member Sofyan Mbarki (Social-Democrats) proposed a motion to ban holiday rentals in Amsterdam neighbourhoods such as the Haarlemmerbuurt, the Kinkerbuurt and the Wallen. A concentration of holiday rentals results in rising house prices, lower social cohesion, increasing pressure on the housing market and inequality, he argued. The motion has support from a majority of the council.
The city government is inclined to implement the motion, but alderman Laurens Ivens (Socialist Party) wants to study the legal aspects. He considers the neighbourhoods mentioned in the motion good candidates for a ban on holiday rentals, but he doesn’t rule out that other neighbourhoods may be selected.
So what neighbourhoods might qualify? One criterion might be Airbnb density, which is shown on the map below (for caveats see Method below).
Unsurprisingly, neighbourhoods with high Airbnb density overlap with areas where residents complain about holiday rentals: Centrum-West, Centrum-Oost, Westerpark, Oud-West/De Baarsjes and De Pijp/Rivierenbuurt (source).
Airbnb frequently claims that it contributes to tourist dispersion because many hosts are located outside the city centre. However, the map suggests that Airbnb is in fact heavily concentrated in neighbourhoods such as the Wallen, the Jordaan, the Pijp and the Kinkerbuurt. While some of these neighbourhoods are outside the city centre, the pattern appears to be concentration rather than dispersion.
While these neighbourhoods would be likely candidates for a ban on holiday rentals, Ivens may also want to anticipate future developments. A number of neighbourhoods still have a relatively low Airbnb density, but have seen their density double or even almost triple over the past three years: Transvaalbuurt, Hoofdweg e.o., Van Galenbuurt and Westindische Buurt.
UPDATE - It was rightly pointed out that Airbnb density partly reflects housing density. An alternative measure would be Airbnb relative to addresses or population. However, this would result in high values for some areas with low population density where holiday rentals don’t appear to be perceived as much as a problem as in some of the more densely populated areas.
Both Murray Cox’s Inside Airbnb and Tom Slee provide data collected by scraping the Airbnb website. While this data has some limitations, it’s probably the best publicly available data source on Airbnb. Since Tom Slee stopped collecting data last year, I used Inside Airbnb data for the current article. A discussion of methodological aspects related to that data is here.
In addition, I used land surface data from Statistics Netherlands (CBS). This data is for 2017.
I calculated an indicator for Airbnb density in the following way:
Note that the indicator for the number stays will not be equal to the actual number of stays, for a number of reasons:
According to Airbnb, the number of stays in Amsterdam is 2.5 million. Based on that number, the actual number of stays would be about 3 times as high as the indicator for the number of stays I calculated. Given the considerations listed above, that’s more or less what one would expect.
Python script here.
The 21 March city council election saw a bit of a voter revolt. Four new parties got elected onto the city council, thanks primarily to voters in the less affluent, peripheral parts of the city. The election outcome reflects Amsterdam’s social divide.
As a result, the composition of the city council changed considerably. So how are the established parties and the new parties getting along?
Before trying to answer that question, let’s have a look at collaboration in the previous city council. There was a left-wing majority in the council, but the government was relatively right-leaning. There was an effective opposition, with GroenLinks (Green Party) and PvdA (Social-Democrats) frequently collaborating to file motions and amendmends.
The chart below shows collaboration in the current city council. The city now has a more left-leaning coalition of GroenLinks, D66, PvdA, and SP. The pattern of collaboration has changed considerably.
The chart suggests that there are three clusters in the city council. One contains the coalition parties GroenLinks, D66, PvdA, and SP. The second contains right-wing / conservative parties VVD, CDA, FvD and PvdO. And the third contains DENK, BIJ1 and ChristenUnie. PvdD (Party for the Animals) appears to be a bit of an outsider by this measure.
Are opposition parties able to exert influence, despite their divisions? An interesting measure is whether they succeed in getting proposals adopted despite a part of the coalition voting against. So far, this has happened twice.
One case was a motion from Diederik Boomsma (CDA), asking to provide parking permits to people who have a private garage but have turned it into something else. Coalition party GroenLinks voted against, arguing that people who have made the decision to use their garage for other purposes are now turning to the city to solve their parking problem.
The second one was a motion from Sylvana Simons (BIJ1) asking to the local government to support teachers in their fight for fair wages. PvdA voted against, arguing that the alderwoman had already taken a stand.
UPDATE 11 March 2019 - A survey by Dutch trade union FNV found that 25% of participating municipalities apply algorithms to personal data to label welfare recipients as potential frauds. 9% said they hire a commercial organisation to do the analysis. FNV’s vice president Kitty Jong condemned the practice.
A number of Dutch cities have contracted a company named Totta data lab to predict which welfare recipients may have committed fraud (the cities were somewhat secretive about this approach, but newspaper NRC wrote about it last spring). Totta has trained algorithms on a considerable amount of personal data: 2 to 3 hundred variables over a period of 25 years.
Such analyses carry the risk that existing biases are reproduced:
Luk [A Totta spokesperson] says that in some municipalities more fraud is found among people who have a partner (e.g., they don’t report income), whereas in others it is people without a partner (failing to report they live together). «But it’s quite possible that only that group has been investigated and we build our algorithms on that.»
Luk says they sometimes add ‘deviant’ citizens to the suspects, apparently in an attempt to look beyond the usual suspects.
Another problem is the lack of transparency regarding how this type of algorithms work. Totta doesn’t disclose its algorithms because it wants to protect its business interests; further, it can be difficult to interpret and explain how algorithms work. As a result, the government is unable to explain what criteria it uses to prepare decisions that affect citizens. Recently, the Dutch Council of State expressed concerns over digital decision-making by the government.
Proponents of algorithms argue that they help to detect more fraud while reducing the burden for innocent citizens. In fact, there may not be such a clear distinction. The organisation of welfare agencies said that alleged welfare frauds are often people who mean no harm, but who get into trouble as a result of complex and ambiguous welfare rules.
Still, Amsterdam city council member Anne Marttin (VVD) finds the approach interesting. She asked if Amsterdam uses algorithms and data mining to detect welfare fraude. The answer is no. This is why:
The city government is aware of the use by other municipalities of algorithms and/or data mining to fight welfare fraud. The city does not use such instruments to deal with or prevent welfare fraud. […]
Our services for welfare recipients are based on trust. Further, the city government attaches great importance to the privacy of citizens and the way in which their data is used by the government, for example to develop algorithms. The city government thinks it’s very important that the use of data mining and algorithms doesn’t have a negative impact on the privacy and the legal protection of citizens.
Amsterdam plans to ban scooters from most cycle tracks. Currently, cycle tracks are still used by the so-called snorfiets category which has a speed limit of 25km/h - although most ride (much) faster. The measure will make cycle tracks safer for cyclists, and it will also result in cleaner air on cycle tracks.
The city has produced a map showing the new snorfiets regime. By my calculations, snorfietsen will have to use the road on a total of 180km (blue on the map) and they will be banned from another 71km of routes where there’s only a cycle track (marked in red). However, they’ll still be allowed to use about 93km of cycle tracks (green), at least for now.
To decide where to ban scooters from the cycle track, the city used data from the Fietstelweek, the large-scale initiative to collect smartphone location data from cyclists. This is interesting, since governments have complained that the number of participants in the Fietstelweek is declining (they started experimenting with Strava data instead).
Perhaps that’s why the city of Amsterdam used the 2016 edition of the Fietstelweek (rather than 2017) to assess how busy cycle tracks are. It used its own traffic counts to validate the Fietstelweek data. The Mathematics Centre of the University of Amsterdam deemed the method used ‘reliable and suitable’.
I’ve created a map to show how the new snorfiets regime compares to Fietstelweek data. The width of the pink lines corresponds to the number of cyclists who used a route; the green dotted line shows where snorfietsen will still be allowed to use the cycle track.
It appears that the city has done a decent job at avoiding the busiest cycling routes (in some cases, this is because these routes don’t have separate bicycle tracks to begin with).
That said, some problems remain. One example is the cycle track along the IJ north of Central Station, which can be very busy and where some snorfiets riders overtake other traffic in a dangerous way. And there’s the Amsterdamse Brug and Schellingwouder Brug (the bridges to the northeast of Amsterdam), where cycle tracks are too narrow for snorfietsen.
In the future, the city intends to ban scooters from all mandatory cycle tracks within the A10 Ring Road. Obviously, they want to do this without compromising the safety of snorfiets riders. This will be easier on routes where car speed is already low.
The map below shows the current average car speed on major roads. The green lines indicate where snorfiets riders will initially be allowed to continue using the cycle track.
Of course I don’t know what the situation is when you’re reading this, but likely some of the highest car speeds (on sections where snorfietsen will initially be allowed to use the cycle track) will be on the Gooiseweg, entering the city centre from the southeast, and the aforementioned Amsterdamsebrug and Schellingwouderbrug. On those bridges, many motorists exceed the speed limit. The city wants to change the road design to invite lower speeds before banning snorfietsen from the cycle track.
Elsewhere, it should be relatively easy to make the remaining cycle tracks scooter-free. Of course, a more practical solution would be to abolish the snorfiets category altogether.
If you wish to respond to the city’s plans, you can use this form. The deadline is 24 September.
I used Qgis to create the first map and Leaflet for the second. I used GeoPandas and Shapely to calculate lengths. On the first map, the width of the pink may be slightly distorted due to varying distances between cycle routes in opposite directions.
[This is a translation of an article from 2016] - I think the Joep bicycle - and the women’s version Ari - were launched in 2008. Joep Salden, owner of a bicycle shop in Utrecht, designed a minimalistic, functional bicycle, without any unnecessary accessories. The only concession was a bicycle bell. «You’ll need one; on this bicycle you’ll overtake anyone», Salden said when I bought my greyish green Joep in 2009.
I’m happy with my Joep and I’m not the only one: Utrecht alderman Lot van Hooijdonk owns one too. TestKees, the bicycle tester of cyclists’ organisation Fietsersbond, tested a number of fast city bicycles in 2009. His conclusion at the time:
Joep and Ari mainly stand out because of the minimalistic assemblage and the beautiful classic look. The frame and the parts go well together. (…) It’s clearly faster than the VanMoof and much faster than the luxurious city bicycles with gear hub and suspension that have been so popular in the Netherlands for years.
For Salden, the fact that his bicycles look good came second. «I appreciate that people are enthousiastic about how it looks, but for me the most important thing is for them to ride off thinking: that’s a smooth ride!», he said in an interview. The bicycle was supposed to last at least ten years.
Coincidence or imitation: by now, there are various bicycles on the market with designs and colours reminiscent of Salden’s bicycles. Take the citybike, since rebranded courier bike, introduced by the Hema department store in 2011. On the face of it, they look a lot like the Joep and Ari - even though the execution is inferior, with wide aluminium tubes and a comfort saddle.
In Amsterdam, I was once addressed by the owners of a bicycle shop at the Weesperplein. They said my Joep was a beautiful bicycle, but also an imitation of the Achielle bicycles they sold. But I don’t think it’s true Salden has imitated Achielle. That said, Achielle has beautiful Sam and Saar bicycles that show similarities to the Joep and Ari.
Interestingly, Salden had his frames built in Belgium. Achielle is also based in Belgium, and has its origins in a family business of frame builders. It wouldn’t surprise me if the Joep frames used to be built by Achielle.
[Update: on Twitter, Achielle has since stated that they used to build the Joep bicycles and that the frames are the same as those of the Sam and Saar.]
Someone else once said my Joep is reminiscent of the VanMoof bicycle produced in Amsterdam, but I have to disagree. Tastes differ, but I think the Joep is restrained and elegant, whereas the VanMoof is neither.
Currently, I use my Joep in Utrecht, where I work. It isn’t as shiny anymore as when I bought it, but it’s still a beautiful bicycle. What’s more, it still runs very smoothly, even though it has seen little maintenance.
Meanwhile, I needed a new bicyle in Amsterdam. I reckoned I’d just buy another Joep. But the website of Salden’s bicycle shop, Het Fietspad, was no longer online and its phone number had been disconnected.
At the location of Het Fietspad, there’s now another bicycle shop, Cycleworks, with beautiful old road bicycles hanging from the wall. They told me that Salden is out of business for good. In fact, he has been for a while, as I found out later. Alas!
Meanwhile, I’ve placed an order for a shiny black Achielle Sam with path racer handlebars. Also quite nice.
For more than fifteen years, Amsterdam has been trying to convince tourists to visit areas outside the city centre. There is a concern that the inner city is approaching the limit of how many tourists it can handle.
To explore the effect of these policies, I analysed changes in the maps in Lonely Planet guides. Over the past years, sights have been added in areas outside of the inner city - mostly areas that had already been affected by gentrification. Still, the large majority of sights are still in the traditional tourist areas, in the city centre and some parts of the Zuid district.
It appears that the effect of tourist dispersion policies is modest at best - and not nearly enough to compensate for the growth of tourism. Reducing the impact of tourism may well require a different approach - for example targeting hotel capacity and low-cost flights to Schiphol Airport.
In its coalition agreement, the new city government said that the positive aspects of tourism are increasingly overshadowed by its negative effects, putting the liveability of some neighbourhoods at risk. One of the ways to deal with this is spreading tourists over the city (and the surrounding region). Amsterdam is to be primarily a place where people live and do business, and only in the second place a tourist destination.
The idea to disperse tourists is not new. In 2016, Amsterdam launched a campaign to promote areas outside the inner city. Interestingly, the campaign caused a bit of a controversy when politicians noticed the Nieuw-West district had been left out of a promotional map. Amsterdam Marketing responded that ‘in our professional opinion’, the district is currently ‘less suitable to be offered as a primary alternative to the city centre’. They argued that neighbourhoods must first be embraced by locals, which suggests that city marketing follows gentrification.
In 2009, Amsterdam planned to promote the eastern parts of the city as ‘the new (2nd) Museum Quarter’; the Northern IJ Waterfront as ‘Creative City’, the Westerpark as a variation on Berlin’s ‘Kulturbrauerei’; the Eastern Harbour Area as Docklands; de Pijp as ‘Quartier Latin’ and Oud-West as ‘Notting Hill’.
And as early as 2001, the tourism board warned that the inner city had almost reached the limit of how many tourists it can handle. «But where should they go? To IJburg for architecture; fun shopping at the Arena Boulevard in Zuidoost and visit the former GVB tram depot in Oud-West.»
A common denominator of the campaigns is that they target repeat visitors. As the tourism board explained in 2001, «we don’t want to send first-time foreign visitors to the outskirts».
To get an idea of the impact of these policies, I analysed changes in the sights shown on maps in Lonely Planet guides (for caveats, see Method below). If tourists turn to new parts of the city, you’d expect these areas to show up on those maps. Further, in 2009, the tourism board started seinding information about sights outside the city centre to publishers of travel guides. «Inclusion in the guides is not guaranteed, but this often happens.»
2006 is a bit of an outlier. A number of sights outside of the city centre were added, only to disappear again in the next edition (see below, Sights that were dropped). If you zoom in on specific neighbourhoods, you’ll notice more changes. For example:
The table below shows the percentage of sights per district:
|Wijk 00 Amstelveen||0||3||0||0||0|
There has been an increase in especially Oost and Noord, but the large majority of sights are still in Centrum and in Zuid (which includes the Museumplein).
In each edition, new sights are added and others are dropped. The latter category includes sights that don’t exist anymore, such as the Netherlands Media Art Centre, the Vakbondsmuseum (trade union museum) and temporary locations of the Stedelijk Museum. Other sights apparently fell out of grace with the authors.
The authors of the various editions have their own preferences and interests. For example, Andrew Bender, author of the 2006 edition, appears to be a bit of a health enthousiast. He added many sports facilities and fitness centres, which explains why his edition had more sights outside the city centre. Most of these were dropped in the next edition. In 2012, Karla Zimmerman and Sarah Chandler added many hofjes (~almshouses). Again, most of them didn’t make the next edition.
I used the following editions of the Lonely Planet Amsterdam guide:
2000: Rob van Driesum, Nikki Hall
2006: Andrew Bender
2012: Karla Zimmerman, Sarah Chandler
2016: Catherine Le Nevez, Karla Zimmerman
2018: Catherine Le Nevez, Abigail Blasi
I analysed sights in the legends of the maps at the end of the guides. The maps also include categories like eating, drinking, sleeping and entertainment. I focused on sights, reckoning that this category would likely present less problems when you want to geocode information from old maps. Note that the classification of especially the 2000 edition is somewhat different from later editions.
It’s possible that errors occured in geocoding or in copying data from the guides. If you spot any errors, please let me know.
Obviously, Lonely Planet maps are not a perfect measure of tourism dispersion. On the other hand, if there had been major shifts in the areas tourists visit, it seems rather unlikely they wouldn’t be reflected in the sights Lonely Planet shows on its maps.
This weekend, Amsterdam’s new North-South metro line will open. To celebrate the occasion, Straatkrant Z! offers a free copy of Eric Hammink’s beautiful circular Metro and Tram map of Amsterdam. Z! is a newspaper sold by homeless people.
Seven years ago, Hammink designed the first version of his map, modelled after the pattern of the city’s canals. At the time, there was talk about Amsterdam’s public transport company GVB adopting the map, but apparently they haven’t. A missed opportunity.
The map is also used in Hammink’s iPhone route planner app.
Note that the map above isn’t really a good illustration here because I used a different data source to create it.
Getting results of Dutch elections at the municipality level can be complicated, but what if you want to dig a little deeper and look at results per polling station? Or even per candidate, per polling station? For elections since 2009, that information is available from the data portal of the Dutch government.
The data is in Election Markup Language, an international standard for election data. I didn’t know that format and processing the data posed a bit of a challenge. I couldn’t find a simple explanation of the data structure, and the Electoral Board states that it doesn’t provide support on the format.
For example, how do you connect a candidate ID to their name and other details? I think you need to identify the Kieskring (district) by the contest name of the results file. Then, find the candidate list for the Kieskring and look up the candidate’s details using their candidate ID and affiliation. But with municipal elections, you have to look up candidates in the city’s candidate list (which doesn’t seem to have a contest name).
If you plan to use the data, here are some practical tips:
Further, note that the data for the 2017 Lower House election is only available in EML format for some of the municipalities. I guess this has something to do with the fact that prior to the election, vulnerabilities had been discovered in software to count the votes, so they had to count the votes manually.
Here’s a Python script that converts EML files to csv. See caveats there.
Eight days from now, Amsterdam will have a new metro line traversing the city from north to south. But what about the orientation of the city’s streets?
Geoff Boeing - who created a Python package for analysing street networks using data from OpenStreetMap - just published a series of polar histograms of American and ‘world’ cities. Amsterdam isn’t among them, but Boeing made his code available, so I used that to create charts for the largest cities in the Netherlands.
While the pattern isn’t nearly as monotonous as in most American cities, I’m still surprised how many streets in Amsterdam run from north to south or from east to west. The Hague has a strong diagonal orientation; Rotterdam doesn’t seem to have a dominant orientation and Utrecht is a bit in between.
With Boeing’s code, you can also do the analysis specifically for roads that are accessible to cyclists, but for Amsterdam that doesn’t make much difference since most roads are.
15 July 2018 - There was some really interesting discussion on Twitter in response to my post from last Friday (I use Twitter names to refer to people; most sources are in Dutch).
Both Sanne and Egon Willighagen asked how the chart treats curved streets. I have to admit I hadn’t checked, but the docstring of the
add_ege_bearings function explains that it calculates the compass bearing of edges from origin node to destination node, so that implies that streets are treated as if they were straight lines.
Is that a problem? Probably not for many US cities, for they seem to have few curved streets. As for Amsterdam: most people’s mental image of the city is probably dominated by the curved canals of the city centre. However, many neighbourhoods consist of grids of more or less straight streets. So perhaps curved streets have little impact on the analysis after all.
Hans Wisbrun argues that the chart type is nice, but also deceptive. The number of streets is represented by the length of the wedges, but one may intuitively look at the surface, which increases with the square of the length. In a post from 2013 (based on a tip from Ionica Smeets), he used a chart by Florence Nightingale to discuss the problem.
Rogier Brussee agrees, but argues that a polar chart is still the right choice here, because what you want to show is the angle of streets.
In a more general sense, I think the charts are an exploratory tool that’ll give you an idea how street patterns differ between cities. If you really want to understand what the wedges represent, you’ll have to look at a map.
That’s what Stephan Okhuijsen did. He noted that the chart for The Hague appears to reflect the orientation of the city’s coastline. Not quite, Christiaan Jacobs replied. The orientation of the city’s streets is not determined by the current coastline, but by the original beach ridges.
I don’t know much about geography (or about The Hague for that matter), but a bit of googling suggests Jacobs is right. See for example this map (from this detailed analysis of one of The Hague’s streets), with the old sand dunes shown in dark yellow.
See also links to previous similar work in this post by Nathan Yau (FlowingData).
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.
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).
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.