How do you map a city with no centre? Commuting patterns in the San Francisco Bay area

The Golden Gate Bridge, San Francisco Bay. Image: Justin Sullivan/Getty.

Dr Alasdair Rae is a senior lecturer in the geography department of the University of Sheffield.

I’ve recently been writing and thinking about polycentric urban regions: partly because I’m interested in how places connect (or not) for one of my research projects, and partly because I’ve been experimenting with ways to map the connections between places in polycentric urban regions.

There was quite a lot of the latter in Peter Hall and Kathy Pain’s ‘The Polycentric Metropolis’ from 2006 – but given that the technology has moved on a little since then I thought I’d explore the topic in more detail. Mind you, I’ve also been looking back on Volumes 1 to 3 of the Chicago Area Transportation Study of 1959, as a reminder that technology hasn’t moved on as much as we think: their "Cartographatron" was capable of mapping over 10m commuting flows even then, although it was the size of a small house and required a team of technicians to operate it.

Anyway, to the point… What’s the best way of mapping polycentricity in an urban region? 

For this, I decided to look at the San Francisco Bay Area since it has been the subject of a few studies by one of my favourite scholars, Prof Robert Cervero of UC Berkeley. Also, a paper by Melanie Rapino and Alison Fields of the US Census Bureau identified the Bay Area as the region with the highest percentage of "mega commuting" in the United States – that is, people who travel 90 or more minutes and 50 or more miles to work. 

Therefore, I decided to look at commuting flows between census tracts in the 9 counties of the Bay Area, from Sonoma County in the north to Santa Clara County in the south. I’ve used a cut-off of 30 miles here instead of the more generous 50 mile cut-off used by Rapino and Fields. I also mapped the whole of the United States in this way, but that’s for another day.

Are you in the big blue blob?

The series of maps below illustrate both patterns of commuting in the Bay Area and the different approaches I’ve taken in an attempt to capture the essence of polycentrism in the area. I don’t attempt to capture the misery of some of these commutes (for that I’d need a different kind of technology).

But, I do think the animations in particular capture the polycentric nature of commuter flows. If you’re represented by one of the dots in the images below, thanks a lot for taking part!

Let’s start with a simple representation of commutes of over 30 miles from San Francisco County (which is coterminous with the City of San Francisco). The animated gif is shown below.

The most noticeable thing here is the big blue blob(©) making its way down from San Francisco to Palo Alto, Mountain View and Cupertino in Santa Clara County. In total, the blue dots represent just over 15,000 commuters going to 803 different destination census tracts.

I’m going to take a wild guess and suggest that some of these commutes are by people who work at Stanford, Google and Apple. But it probably also includes people working at NASA Ames Research Center, Santa Clara University and locations in San Jose. 

These patterns aren’t particularly surprising, since there has been a lot of press coverage about San Francisco’s bus wars and commutes of this kind. However, there is a fairly significant dispersal of San Francisco commuters north and east, even if the numbers don’t match those of the big blue blob. By the way, from San Francisco it's about 33 miles to Palo Alto, 39 miles to Mountain View, 42 to Cupertino and 48 to San Jose. 

The first example above doesn’t reveal anything like the whole story, though. There are actually quite a lot of commuters who travel in the opposite direction from Santa Clara County to San Francisco. And more widely, the commuting patterns in the Bay Area – a metro area of around 7.5 m people – resembles a nexus of mega-commuting.

This is what I’ve attempted to show below, for all tract-to-tract connections of 10 people or more, and no distance cut-off. The point is not to attempt to display all individual lines, though you can see some. I’m attempting to convey the general nature of connectivity (with the lines) and the intensity of commuting in some areas (the orange and yellow glowing areas). 

 

Even when you look at tract-to-tract connections of 50 or more, the nexus looks similar.

 

If we zoom in on a particular location, using a kind of "spider diagram" of commuting interactions, we can see the relationships between one commuter destination and its range of origins. In the example below I’ve taken the census tract where the Googleplex is located, and looked at all Bay Area Commutes which terminate there, regardless of distance.

In the language of the seminal Chicago Area Transportation Study I mentioned above, these are "desire lines" since this represents "the shortest line between origin and destination, and expresses the way a person would like to go, if such a way were available" (CATS, 1959, p. 39) instead of, for example, sitting in traffic on US Route 101 for 90 minutes.

According to the data, this example includes just over 23,000 commuters from 585 different locations across the Bay Area. I've also done an animated line version and a point version, just for comparison.

Commuting connections for the Googleplex census tract.

 

Animated spider diagram of flows to Mountain View.

 

Just some googlers going to work, probably. 

Looking further afield now, to different parts of the Bay Area, I also produced animated dot maps of commutes of 30 miles or more for the other three most populous counties – Alameda, Contra Costa and Santa Clara. I think these examples do a good job of demonstrating the polycentric nature of commuting in this area, since the points disperse far and wide to multiple centres.

Note that I decided to make the dots return to their point of origin – after a slight delay – in order to highlight the fact that commuting is a two way process. The Alameda County animation represents over 12,000 commuters, going to 751 destinations, Contra Costa 25,000 and 1,351, and Santa Clara nearly 28,000 commuters and 1,561 destinations. The totals for within the Bay Area are about 3.3m and 110,000 origin-destination links.

Alameda County commutes of 30+ miles.

 

Contra Costa County commutes of 30+ miles.

 

Santa Clara County commutes of 30+ miles.

 

Finally, I’ve attempted something which is a bit much for one map, but here it is anyway; an animated dot map of all tract-to-tract flows of 30 or more miles in the Bay Area, with dots coloured by the county of origin. Although this gets pretty crazy half way through I think the mixing of the colours does actually tell its own story of polycentric urbanism. For this final animation I’ve added a little audio into the video file as well, just for fun.

A still from the final animation. You can see the whole thing here.

What am I trying to convey with the final animation? Like I said, it's too much for a single map animation but it's kind of a metaphor for the messy chaos of Bay Area commuting (yes, let's go with that). You can make more sense of it if you watch it over a few times and use the controls to pause it. It starts well and ends well, but the bits in the middle are pretty ugly – just like the Bay Area commute, like I said.


My attempts to understand the functional nature of polycentric urbanism continue, and I attempt to borrow from pioneers like Waldo Tobler and the authors of the Chicago Area Transportation Study. This is just a little map-based experimentation in an attempt to bring the polycentric metropolis to life, for a region plagued by gruesome commutes.

It’s little wonder, therefore, that a recent poll suggested Bay Area commuters were in favour of improving public transit. If you're interested in understanding more about the Bay Area's housing and transit problems, I suggest watching this Google Talk from Egon Terplan (54:44).

Dr Alasdair Rae is a senior lecturer in the geography department of the University of Sheffield. 

This article was originally posted on his blog, Under the Raedar, and is reposted here with the author's permission.

 

NOTES: The data I used for this are the 2006-2010 5-year ACS tract-to-tract commuting file, published in 2013. Patterns may have changed a little since then, but I suspect they are very similar today, possibly with more congestion.

There are severe data warnings associated with individual tract-to-tract flows from the ACS data but at the aggregate level they provide a good overview of local connectivity. I used QGIS to map the flows. I actually mapped the entire United States this way, but that’s going into an academic journal (I hope).

I used Michael Minn’s MMQGIS extension in QGIS to produce the animation frames and then I patched them together in GIMP (gifs) and Camtasia (for the mp4s), with IrfanView doing a little bit as well (batch renaming for reversing file order). Not quite a 100% open source workflow but that’s because I just had Camtasia handy. The images are low res and only really good for screen. If you’re looking for higher resolution images, get in touch. 

 
 
 
 

Why is it acceptable to kill someone? On the mysterious history of Britain’s road death toll

A London speed camera, 2004. Image: Getty.

A decade ago I became fascinated by a graph. This one:

I had been tracking the underlining data for years. The figures were easy to remember. Every year it was 3,500, plus or minus a percentage point or two.

Yet when the 2008 data was released, it had fallen to 2,538. This was 1,000 less than the figure in 2003. I plotted the above graph, and as I said, I became fascinated.

Because this is a really important graph. This is a plot of the number of people killed on Britain’s roads each year.

In Great Britain, collectively, we used to kill nearly 3,500 people on our roads every year. Consistently or, dare I say it, boringly: 3,500 deaths a year, 10 a day. It was accepted, in a, “Well yes it’s bad, but what can you do about it” kind of way. There was no clamour for change. Newspapers weren’t running headlines about the deaths mounting up, as they do with knife crime.

Meanwhile a train crash would be front page news for a week. Take the train that derailed at Hatfield on 17 October 2000, a tragedy in which 4 people died. That led to huge media interest, massive upheaval on the railways, and, ultimately, as the re-nationalisation of Railtrack, whose failings had caused the crash. Yet more than twice as many people will have died on the roads that day. Nothing was written about those deaths. Nothing changed.

In 2000, four people died in train crashes, while 3,409 died on the roads.

Here are those figures again.

1997 – 3,599 people killed on our roads

1998 – 3,422

1999 – 3,423

2000 – 3,409

2001 – 3,450

2002 – 3,431

2003 – 3508

But, in 2004 the figure dropped below 3,400 for the first time, to 3,221. Then in 2005 to 3,201.

2006 – 3,172

2007 – 2,946

Below 3,000! This was change. Significant change: 500 lives a year were not being lost. If you use Britain’s roads, your life may have been one of them.

2008 – 2,538

2009 – 2,222

When the 2010 figures came out I was amazed by the headline figure: 1,857.

That’s still far too high, of course, but it was 1,701 lower than seven years earlier.

This was a major story that deserved a ton of coverage, which it failed to get. Having shown no concern for when we were killing 3,500 people, it wasn’t overly surprising that the fact we were now killing 1,700 fewer wasn’t celebrated.

At any rate, the graph had flat-lined for years, then, in half a dozen years, it halved. Why?

The lack of media coverage resulted in an absence of answers. One commentator, Christian Woolmar, observed that there was no clear answer to why this had happened. But he went on to point out that there had been a fall in the average road speed over this period.

My anticipation of the 2011 figures troubled me, because I expected them to go up. Obviously I didn’t want them to: I desperately want zero deaths on our roads. But something happened in 2010 that I was sure would lead to more fatalities and bring a halt to the falling trend.

I was right. In 2011 we killed 1,901.

Sometimes, being right is shit.

The news was better in 2012. The fatality rate was 1,754. So was the 2011 figure just a blip, due to some significant snowfalls that year? No: the trend was over.

The number of people killed on our roads has remained stuck in the 17 hundreds. 

2013 – 1,713

2014 – 1,775

2015 – 1,732

2016 – 1,792

2017 – 1,793

2018 – 1,782

We have returned to a flatline on the graph – and if anything, I’m more fascinated now than I was before. Road deaths flatlined at 3,500 for years, then fell sharply, then flatlined again at half the rate.

This can’t have happened by accident. I wished I could explain it. I wish we could repeat it. No: I wish the second flatline hadn’t happened, and the fall had continued. If the rate of fall had continued, we’d have reached zero deaths on the road by now. You’d be right to question whether this is possible – but if you can half the number in a few years, why can’t we eradicate them altogether? The railways are an example of what is possible. The last time a passenger died in a train crash on Britain’s railways was in 2007.

It was time to figure out the answers to two questions. Why did the death toll fall? And why did it stop falling?

The obvious reason for a reduction in deaths on the road is the improvement in car safety features. This could create a gradual fall in the death toll as new, safer cars replaced older ones. But I’m not sure it can explain a 40 per cent fall over a 4 year period.

There’s a way to check whether cars on the road became almost twice as safe between 2003 and 2010: you can compare the figures with the rest of the EU. Car safety features are international, and any new feature would have appeared around the same time across the continent.

So I found the EU figures for 2000 to 2017, indexed for 2000 and plotted the graph for multiple countries. It was a busy graph. For clarity the following graph only includes Britain, Germany, France, Spain and Italy along with a straight line drop for comparison.

The good news is that things are improving across Europe – but no country had quite the same trajectory as Britain. They all have a fall much closer to a straight line of the sort you’d expect a general improvement in car safety would produce.

One thing I did notice is that, from 2013, these five countries stop falling. The technology based solutions of recent years, such as automatic emergency braking, don’t appear to be saving lives as of yet.

So, yes, cars are safer – but that doesn’t seem to explain why British roads suddenly became 40 per cent safer between 2006 and 2010.


In 1999, the New Labour government announced that it was going to reduce deaths on our roads. The target was a 50 per cent reduction by 2010. As you now know, it succeeded. This was a major achievement for a government. The kind of thing you would bang on about all the time. “Deaths on our roads halved by Labour!” But the party wasn’t in government when the 2010 figures were released – and it’s hard to take credit for your achievements from the opposition benches.

That it was government policy is not a full explanation, and how this happened is a little opaque. From what I can gather there was a wide ranging approach. The fire and rescue service changed their practices: because they recognised that survival rates were directly dependent on how quickly people got to hospital, this became the priority. Disturbing a police crime scene was allowed if it saved a life. Accident black spots were located, highlighted and safety measures implemented. Throughout that period road safety campaigns focused on speed, with “Speed Kills” being the dominate message for that decade. The government also changed the laws on speed cameras.

RoSPA, the Royal Society for the Prevention of Accidents, has a lot to say about speeding and speed cameras. Its “Speed Camera Factsheet” states that, “Cameras are a very effective way of persuading drivers not to speed, and thereby reducing the number of people killed and seriously injured.” It reports that an independent review published by the Department for Transport (DfT) in 2005 said that “cameras significantly reduce speeding and collisions, and cut deaths and serious injuries at camera sites”, adding that cameras sites were delivering 100 fewer deaths per year.

Cameras first appeared in 1991, and revenue from court fines and fixed penalties went to the Exchequer. However in 2000 a trial scheme saw local councils keep the fines to pay for the cost of speed and red-light cameras. The pilot was so successful that, in 2001, legislation enabled this to happen across the country. The cost of providing and operating cameras moved from the local authority to the law breaking motorist.

The golden age of the speed camera had begun.

There was a tweak to this legislation in 2007. Fines reverted back to the Exchequer’s piggy bank. The DfT switched to funding cameras through a road safety grant. The intention was to create a greater mix of road safety measures agreed between local authorities and the police.

The number of people killed on British roads in 2007: 2,946

The number of people killed on British roads in 2010: 1,857

So perhaps the creation of the Road Safety Grant had a significant impact.

The second question: why did the death toll stop falling?

In 2010 I was unaware of Labour’s target to halve deaths on the roads. But, the change in government was enough for me to predict that the fall was over.

When the Tory/Lib Dem government negotiated its way into power in May 2010, the press declared that it was the end of the horrible nanny state – a return to personal freedom, liberty and the rule of common sense.

The way that this was to play out in real practical terms was on our roads. The evil speed camera was in the firing line. The narrative was that these cameras were just there so councils could extract cash from the poor public. Completely ignored were the facts that the fines were only handed down to dangerous, law-breaking drivers, and that councils no longer got the cash from fines.

Soon after the election the coalition government said that “Labour's 13-year war on the motorist is over” and pledged to scrap public funding for speed cameras. The Road Safety Grant to local authorities was cut from £95m to £57m. This meant that the government was now receiving an estimated £40m more raised in fines than it was spending on road safety. The cut to the grant reduced the camera maintenance budget by 27 per cent. It removed all the funding for new cameras, speed humps and other safety measures.

And the golden age ended.

Councils across the country announced their change of policy. Oxfordshire County Council switched off its speed cameras on 1 August 2010. Money was saved; lives were lost.

Eight months later, on 1 April, Oxfordshire’s cameras snapped back into life when the council reversed its decision because deaths on the county’s roads had immediately increased.

Turning off speed cameras sent out the message that we were no longer taking speeding seriously. The road safety campaigns changed their focus. The message that Speed Kills fell away and was replaced by drink- and drug-driving messages. It’s easy to miss that these campaigns move from encompassing virtually every driver to targeting a minority. A switch from confronting a socially acceptable behaviour to re-enforcing something already unacceptable. The state is no longer challenging everyone to be safe – only the small minority of bad people.

Yet speed still kills. The World Health Organisation states that an increase in average speed of 1 km[h typically results in a 3 per cent higher risk of a crash involving injury, with a 4–5 per cent increase for crashes that result in fatalities.
The majority of safety measures installed before 2010 remain in place and are saving lives. But with the funding gone councils are no longer installing new measures and the death toll is no longer falling.

So you can make a strong case that the pattern of road deaths was the result of government policy.

Which begs the question of our government: why has it accepted that it’s OK to kill, or be killed, on our roads?