champagne anarchist | armchair activist

Bike share in Amsterdam: who benefits

Over the past weeks, Amsterdam has been flooded by rogue bike share bicycles that can be rented with a smartphone app. Big business, according to newspaper het Parool. Amsterdammers responded to this commercial junk by placing bicycles at bulky waste disposal sites. The municipality has announced it will remove all bike share bicycles, and subsequently regulate the market.

What should a new policy look like? The use of public space must be properly regulated, as well as the quality of the bicycles. It would be nice if availability and usage data would be made available as open data through an API. Further, many people are asking whether the bicycles are meant for tourists or for Amsterdammers. The next question that should be asked: will they benefit all Amsterdammers?

American research has shown that residents of lower-income neighbourhoods are interested in bike share. Nevertheless, the bicycles are primarily used by rich, white residents. Some cities and operators do try to make the system accessible to all residents.

In Amsterdam, bike hire operator Donkey Republic focuses mainly on the central areas of the city - at least, that’s what their map suggests (the map doesn’t show actual bike locations, but it does show how they present themselves). Competitor Hello Bike focuses exclusively on the Zuidas business district.

Of course, this is not specific for bike sharing. More people ride bicycles in the richer central areas of the city than in the peripheral areas anyway. But if permits for bike share operators are to be introduced, you might as well require them to make their product attractive for and accessible to all Amsterdammers.

Airbnb’s agreement with Amsterdam: some insights from scraped data

Airbnb is under fire. The platform would harm the liveability of Amsterdam neighbourhoods and drive up house prices. In December last year, Amsterdam and Airbnb signed a Memorandum of Understanding (MOU) to deal with abuses. According to Airbnb, the agreement is already bearing fruit. Airbnb thinks Amsterdam should focus its enforcement efforts on other platforms. But Amsterdam wants to introduce a registration requirement for holiday lettings, a measure Airbnb vehemently opposes.

In this article, I analyse some of the changes that occurred since the announcement of the MOU. I use data from Murray Cox (Inside Airbnb) and Tom Slee, who have scraped the Airbnb website various times between May 2014 and May 2017 (scraping is automated retrieval of data from websites). Note that data about Airbnb is always controversial. Read this article to understand why the data collected by Cox and Slee is an important addition to the data provided by the company itself.

Sixty-day limit

Amsterdam residents may rent out their home sixty days per year. According to the new agreement, Airbnb will block advertisements when they exceed that limit. However, this applies only to entire homes and not to rooms, because these may be B&Bs, where the sixty-day cap doesn’t apply.

According to Airbnb, there has been a substantial decrease in the number of homes offered for rent more than sixty days per year. This would be evidence that the MOU is already reducing illegal offerings.

The chart below shows how many rooms and entire homes were available more than sixty days per year, using data from Murray Cox.

The chart appears to confirm what Airbnb has claimed: the number of entire homes available more than sixty days has gone down. However, this started in the first half of 2016, well before the MOU was signed (let alone implemented). So it appears that it was caused by something else.

Perhaps it was the threat of stricter enforcement by the government itself. On 16 February 2016, Amsterdam announced that it was creating its own scraper to collect information from home rental platforms such as Airbnb. In March, the Green Party and Social-Democrats filed motions to step up enforcement.

Room type changes

Does this mean the agreement between Amsterdam and Airbnb wasn’t a turning point? Perhaps it was - but in a different way.

Aggregate data about Airbnb listings are the result of a complex interplay of developments. Some listings are taken off the platform, and new ones are created. In addition, some hosts change the type of their listing - from room to entire home, or vice versa. This is shown in the chart below (using data from Tom Slee).

Until recently, few hosts changed the type of their listings. But since the announcement of the MOU, hundreds of listings have been changed from entire home to room. As indicated above, in the MOU, Airbnb promised to block advertisements for homes that have reached the sixty-day limit. Could it be that people changed their listings into «rooms» to evade the sixty-day limit?

I analysed listings that were changed from home to room between early March and early April 2017 (data from Murray Cox). In early April, over three-quarters of these listings were available more than sixty days per year. This would be consistent with the theory that hosts changed these homes into rooms because of the cap.

It’s possible that these hosts actually stopped renting out entire homes and started to rent out a single room instead. In that case, you’d expect them to have lowered the price and changed the description. However, the price was almost never lowered. Often, the host didn’t even change the description. In some cases, the description still explicitly says that guests have the entire home to themselves.

Incidentally, this is not the first time enforcement led to a large-scale conversion of homes into rooms. This has also happened in New York.

Conclusions

With the available data, it’s not possible to know with certainty what exactly happened over the past months. That said, there are indications the agreement between Amsterdam and Airbnb may be less effective than it seemed:

  • There has been a decrease in the number of entire homes offered for rent more than sixty days. However, this started well before the agreement was signed. It could be a result of (the threat of) enforcement by the government itself.
  • After the announcement of the agreement between Amsterdam and Airbnb, hundreds of entire homes were categorised as rooms. This could be a way for hosts to evade the sixty-day cap for entire homes.

Method and data

Both Murray Cox and Tom Slee frequently scrape the Airbnb website. Cox’ data is more detailed (including, for example, the texts used in advertisements and availability information). Slee collects his data more frequently, at least so for Amsterdam. Both Cox and Slee have made their data available as open data (thanks!).

Cox and Slee are not the only ones who collect data from the Airbnb website; there are commercial providers as well. In addition, the Amsterdam Municipality has started scraping the websites of Airbnb and other platforms. It appears Amsterdam only shares this data with city council members on a confidential basis.

As for room type changes: the data probably underestimates the actual number of changes, especially for the earlier periods. The reason is that you can only detect changes if an avertisement is in both the old and the new dataset. The longer the period between two measurements, the higher turnover will be (listings disappear, new ones are added) and therefore the higher the chance of missing room type changes.

Therefore, I did an additional calculation, correcting for the amount of overlap between the old and the new measurement. The result can be seen here. The picture is slightly different, but the conclusion stands: as of the end of 2016, there was a clear increase in home-to-room changes.

I used Python for the analysis. Here’s the code. As always, comments regarding the analysis and interpretation of the data are welcome.

Academic support for Mélenchon, mapped

On Sunday, the first round of the French presidential election will be held. Left-wing candidate Jean-Luc Mélenchon has surged in the polls and rightwingers have called his programme devastating. On the other hand, over a hundred economic scientists have said he offers a serious and credible alternative to the destructive austerity policies of the past decades.

Given Mélenchon’s criticism of Germany’s economic policy and his support for Greece, one might expect academic support for his programme to be concentrated in the south of Europe. However, the map shows his academic supporters are also in countries like the UK and Germany.

Read more about Mélenchon’s programme here and here.

Method

I geocoded the affiliations of the list of supporters using this tool and Bing’s map api. Sometimes Bing gets the location of the institution right, sometimes it gives the location of the city where it’s located and sometimes it fails. I’ve corrected a few coordinates manually but I can’t rule out I missed any errors.

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Reject all evidence: How George Orwell’s 1984 went viral last January

On Sunday 22 January 2017, Trump adviser Kellyanne Conway introduced the term alternative facts to justify disputed White House claims about how many people had attended Trump’s inauguration. The term alternative facts was quickly associated with the newspeak and doublethink of George Orwell’s novel Nineteen Eighty-Four. Sales of the book became ‘hyperactive’ during the following week.

I looked up some 150,000 tweets about Orwell’s ‘1984’ to see how interest in the novel developed during that week (note that analysing tweets is a somewhat messy business - see Method below for caveats).

But first, a basic timeline. On Friday 20 January, the inauguration took place. Afterwards, people started tweeting photos showing empty spots in the audience. On Saturday, the White House claimed the photos were misleading and that the inauguration had drawn the «largest audience to ever witness an inauguration». On Sunday, Conway appeared on NBC’s Meet the Press and defended the White House claim as alternative facts.

Alternative facts

The chart below shows tweets about Orwell’s 1984 and how many of those tweets specifically mention alternative facts. Immediately after Conway’s Meet the Press interview, the first tweets appeared that made the connection between alternative facts and 1984 (the green line in the chart). The real peak occured on Tuesday, when major media started to discuss the connection.

The alternative facts quote can explain some of the interest in ‘1984’, but there was also a peak in Orwell 1984 tweets even before the interview with Conway took place.

Amazon sales

Meanwhile, sales of the book ‘1984’ on Amazon started to rise. On Sunday, the day of the interview, it reached the top 20. On Tuesday, the Guardian reported it had reached number 6 and in the evening of that same day, it became the number 1 best-selling book on Amazon.

At some point, people started to discuss the rising book sales on twitter, as the chart below shows.

Tweets about sales of ‘1984’ didn’t really take off until Tuesday, and largely coincided with talk about the alternative facts quote.

Reject all evidence

That still leaves the question what the earlier Orwell 1984 tweets were about. Interestingly, almost all these earlier tweets contain the following quote from ‘1984’, which describes how the authorities redefine truth:

The Party told you to reject all evidence of your eyes and ears. It was their final, most essential command.

The chart below shows tweets containing this quote.

On Saturday evening, the White House had held its press conference at which it claimed a record number of people had attended the inauguration. The first reject all evidence tweet I could find was posted before that press conference, but the quote didn’t catch on until after the press conference. Within days, the quote was tweeted over 50,000 times.

In short, Conway’s remark on Sunday about alternative facts boosted interest in ‘1984’, but didn’t start it.

Meanwhile, the 1984 tweets probably reflect a broader phenomenon. Various media have discussed how dystopian novels like ‘1984’ are ‘chiming with people’ (get your reading list here).

Method

I used Python and the Tweepy library to search the Twitter API for orwell 1984. This method has limitations. Twitter provides a sample of all tweets and no-one knows exactly how much is missing from that sample. Further, searching for orwell 1984 may overlook tweets only mentioning orwell or 1984, or even nineteen eighty-four, as in the official book title.

The search for orwell 1984 yielded some 150,000 tweets. If the text contains both alternative and facts (this includes tweets containing #alternativefacts) I classified them as being about alternative facts; if they contain amazon or sales or bestseller or best-seller, I classfied them as being about sales. If they contain reject and evidence and eyes, I classified them as containing the quote «The Party told you to reject all evidence of your eyes and ears. It was their final, most essential command».

I used 9 am as the time at which Meet the Press was aired. For the time of the original White House claim about attendance at the inauguration, I used this recorded live feed which was announced to start at 4:30 pm; the actual press conference starts after about 1.5 hrs, i.e. 6 PM.

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Python script to import .sps files

In a post about voting locations (in Dutch) I grumbled a bit about inconsistencies in how Statistics Netherlands (CBS) spells the names of municipalities and why don’t they include the municipality codes in their data exports. This afternoon, someone who works at CBS responded on Twitter. She had asked around and found a workaround: download the data as SPSS. Thanks!

CBS offers the option to download data as an SPSS syntax file (.sps). I wasn’t familiar with this filetype, I don’t have SPSS and I couldn’t immediately find a package to import this filetype. But it turns out that .sps files are just text files, so I wrote a little script that does the job.

Note that it’s not super fast; there may be more efficient ways to do the job. Also, I’ve only tested it on a few CBS data files. I’m not sure it’ll work correctly if all variables have labels or if the file contains not just data but also statistical analysis.

That said, you can find the script here.

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