Modelling Movement in the City: The Influence of Individuals

'Modelling Movement in the City: The Influence of Individuals' was the title of a talk I gave at the AGILE conference in Avignon, France last week.  For the conference I actually initially prepared a poster that never ended up seeing the light of day - except for now that is.

The poster presents some recent work I carried out through agent-based simulation, demonstrating how different behavioural models influence the formation of macroscopic patterns.  As you can see from the results, the impact of mere basic assumptions hold a significant impact upon the unfolding network picture.

Click here to download:
AGILE_Poster.pdf (4.31 MB)

Probably now going to write this up as a journal paper, but hopefully putting the poster up here won't mess with any copyright stuff - please let me know if it might!

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Mapped: London's 'Rudest' Boroughs

A couple of weeks ago, I put up a post detailing how swearing on Twitter increases during the course of the average day.  It seemed people get more angry and sweary outside of work time, rather than during.

To delve a little deeper in this topic, I've now had a look at where Twitter gets angry.  For each of London's 33 boroughs I have carried out the same analysis - this time for a month's worth of tweets - looking at the percentage of tweets containing swear words in each borough.  The results follow some interesting trends...

Londonsrudestboroughs
At least in the Twittersphere, inner London appears to be the veritable paradise of civility relative to the bile-filled tweet streams emanating from outer London.  The biggest offenders appear to be located to the east of city, with east London fairing considerably worse.  Yet the leafy boroughs of Barnet, Sutton and Bromley perform badly too.

Right, so let's first look at what doesn't seem to be going on here.  First off, the influence of this idea that people mostly swear from the comfort of their own sofa does not seem to hold very true.  There does not seem to be a very strong relationship between swearing density and residential locations.  If there were then you'd see higher scores in the likes of Haringey, Richmond, Hammersmith and Fulham and Newham. Nor does swearing follow any sort of deprivation index, again Haringey is relatively poor compared to the likes of Sutton, Bromley and Barnet, which fair much worse.

So what is going on?

In my opinion, what I think we are seeing is a reflection of demographic and cultural trends across these boroughs.  Taking demography in the first instance, according to the 2009 figures on nationality demographics at the borough level, those London boroughs with the highest percentages of British-born citizens are Havering, Bexley, Bromley and Sutton, respectively*.   It would make sense that the higher the percentage of British-born citizens in an area - on average those probably more likely to use an English swear word in a tweet - the greater the number of swearing tweets there are likely to be.  True, but I don't think this tells the whole story.

Looking beyond these four boroughs, Kingston and Richmond also report high percentages of British citizens living within their boundaries - yet we don't see similar volumes of sweary tweets coming from these boroughs.  How can this be so?  Make of this what you will, but beyond the demographic variation, the data appears to highlight a cultural variation across London in attitudes towards swearing in tweets.  Simply put, the data seems to suggest that the good residents of eastern and southern boroughs of outer London are generally more inclined to throw a swear word into a tweet than their counterparts over to the western side of London.

As I say, this is just my theory - there is a whole lot more you could do to this data to gain a better understanding of the trends observed here (unfortunately I don't have the time to do so!).  I'd be very interested to hear any alternative ideas about what might be going on though.

Overall, I hope these analyses begin to give you an insight into the extent to which Twitter data (and other data sources like it) can be used to reveal and explain social, spatial and temporal trends.

* Newham, Westminster and Kensington and Chelsea score highest for non-British born residents

 

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Cities of the Future: Towards Technical or Natural Optimisation?

Amanda Erickson put up a nice, simply visualisation of what life might be like in a future of driverless, automated cars.  Check it out below.

Two things sprang to mind while watching this - first, how terrifying this might be for a passenger in one of these cars, and second, haven't I seen this sort of thing somewhere else before?

Well, yes, I showed the following video in a lecture last month as demonstration of self-organisation.  To me, the patterns look similar - at the higher level you see chaos, but when you observe the actions of individual's there is usually a rational stream of thought behind the actions they are taking - normally to get to their exit road.  Judge for yourself.

I think the stark similarity seen between these two videos raise interesting questions about what we consider as progress in the urban realm.  Bare with me as I attempt to explain.

The driverless or automated car is often seen as the natural future of private transportation*, with one of its main benefits being the apparent offer of optimal organisation of traffic flows (e.g. no congestion).  And indeed when look at the first video, everything works and works well, perhaps even optimally.  But then you look at the second video, and you essentially have the same thing, created solely through the activity of individuals.

It is strange therefore that a fully optimised technical system is generally deemed necessary and superior.  When people are left to their own devices, to 'sort it out between them', people invariably do.  Traffic in Hanoi is not just the only example of this type of self-organisation - the Internet itself is a creation of human ingenuity.  Following Monderman's ideas on Shared Space, perhaps all of these traffic regulations, signage and restrictions actually reduce our need to think about what we are doing.  They reduce and remove our ability or will to self-organise, and to the deficit of us all.

So why don't 'natural' answers to technical problems receive a better press?  I suspect it is an issue of trust in the citizen.  That threat that one person may mess up, and mess it up for the rest of us.  Instead of facing the risk and accepting it as part of the solution, we surround ourselves with unnecessary and invasive mechanisms that carry out the task for us.  They may cost a lot of money and not be any better than our current solution, but they feel like progress.  It feels like things are getting better.  So, yes, perhaps automated cars are indeed a thing of the future.

As ever, very interested to hear your thoughts on this.

* I've personally never been so sure - mainly because of the safety element, and that fact that many people actually enjoy the process of driving...

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When does Twitter get angry?

I've been spending a bit of time with Twitter data of late - perhaps not a healthy activity - but it is amazing what a rich data source of social and spatial behaviour it is.

Someone asked to me today whether it was possible to identify when and where Twitter gets angry.  Well, here is my answer to the first part - the when.

The graph below shows the variation, across the day, in the prevalence of swearing in the 'Twittersphere'.  The data used represents tweets during two weeks in March 2012 covering London only - so maybe this is just when London gets angry...

In the graph we have the percentage of all tweets containing ALL types of swearing in blue, in red we have the prevalence of the f-word (by far the most common swear word), then finally the percent appearance of the s-word is shown in green.  Time is along the bottom.

Swearingtweets

Putting the slightly frivolous nature of this work aside for a second, the data does demonstrate some interesting trends.  There is a clear upward trend in 'anger' as the day goes on, reaching a peak at around 10pm.  But why is this?  Why do we swear more in the evening, when we should be relaxed and enjoying our precious free time?  Are we (we being Twitter users only, of course) swearing at the TV?  Arguing with our friends over Twitter?  Or are enough of us getting drunk and losing our inhibitions?

We also see a smaller peak at around 5pm - now this is more easily explained.  The 'thank f**k work is over' tweet one might surmise.  An even smaller peak at around 9am suggests the opposite effect.

But I think this simple analysis gives us some insight into the way we use social media throughout the day.  During the day we think about work.  We tweet and communicate about work.  Yet in the evening, Twitter becomes a different place.  We let our guard down, and once we're outside of the constraints of work, perhaps we begin to use Twitter in a different way.  Places like Twitter allow us the space to exclaim and let off our true feelings, whatever they may be, that might otherwise be constrained in other environments.

Twitter gets a lot of stick for its high volume of frivolous content - probably with good reason - but at a higher level some subtle but interesting social trends can start to be observed.

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London Driver Survey

As part of building a fuller understanding of the way people move around the city by car, I've developed a survey to start delving into some of the lesser understood issues.

The survey looks at the extent of use of GPS and similar devices, behaviour around congested areas of the network and usage of traffic information.  The results will contribute towards the building of a better model of driver behaviour.

You can find the survey here - http://goo.gl/UDrFI

Please pass it on to all of the motorists in London that you know!

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My Lecture on Agent-based Modelling

I've just completed a lecture at the UCL Energy Institute on agent-based modelling and thought, hey - maybe some of my blog readership would be interested in this!

Please find the PDF below - it should be quite straightforward, although without the whizz-bang of the demonstrations and videos.  You can find the simulations I describe in the Model Library within NetLogo.

Enjoy, and please let me know if you have any questions.

Click here to download:
Ed_Manley_ABM_Talk.pdf (3.91 MB)

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LongLatMe: Location-embedded SMS

So, let's say you want to meet up with your friends.  You text - "Where are you?".  "We're at the Bar Bar on 59th Street", they reply.  Now you need to look the place up, and navigate your way there.

Instead, why can't your friend just send you a location object within the SMS, encoding their current coordinates.  The ability to locate exists, all that needs to be developed is a generic method for integration with all current mapping applications, allowing you to easily route your way to their location.

Does this exist?  And, if it doesn't, then why not?  I'd be amazed if Google haven't thought to implement this with Android.

Edit: This exists (well, of course it does!) though with not as good a name.  You can find out about 'GeoSMS' at this wikipedia page...

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Agent-based Modelling + Traffic Flow Modelling = Large-Scale Urban Simulation

At the upcoming AAG conference in New York, I'll be presenting a recent prototype that links agent-based simulation with current traffic flow models.

The basic premise is that any cognitive decision associated with movement around cities should be modelled at the level of the individual.  However, it is not always necessary that all movement be represented individually.  Doing so potentially wastes limited computational power, especially important where modelling many complex agents.

Instead, my new simulation utilises traffic flow modelling to constrain the movement of individual agents.  Individuals choose where they move individually, but physical movement itself is modelled collectively.  The higher the traffic flow on a single route, the slower each agent on that route will travel.  This approach is more efficient and allows a much larger scale of complex agent-based simulation.

I'll provide more detail at AAG next Sunday, but the basic result is as below.  

The simulation demonstrates traffic flows across central London.  There are 30000 agents of varying behavioural characteristics moving around this space.  Their movement decisions impact on the state of the network.  

Hybrid Agent-based Simulation of Central London with improved visualisation from Ed Manley on Vimeo.

KEY:  The redder colours represent high traffic saturation aka queues and congestion, the blues and greens represent quiet or free flowing traffic conditions.

 

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A Simple Idea for Making Route Directions More Human

For many, route planners are vital in finding your way around the city.  Type your destination into Google Maps or one of the many other websites or apps available, and you'll be returned a list of directions from your location.  Simple, right?

Hmm well, let's have a look at an example.  Taking two well known locations in London, we'll have a look at the walking directions provided by Google Maps - Buckingham Palace to the Tate Modern - here we go.  Great George Street, fine, Bridge Street, ok, follow the A302, errr, something about the Millenium Bridge, and we're there, maybe.

OK, if you're a Londoner, how would you describe the route to someone?  I suspect it might go something like this...

Right, so from Buckingham Palace, head down towards Parliament, keep left of Parliament and go over the bridge.  At the end of the bridge, turn left, go past the Millenium Wheel, carry on along the river.  You'll pass the National Theatre and the OXO Tower, then the Tate Modern is opposite St Pauls.

So why can't Google Maps or anyone else include these instructions?  They have the data on the locations of these places.  They have the direction of movement of the individual, so can have an idea of what is in front of them...

"Yes, but what about obstacles stopping people from seeing these places?!", I hear the perceptive reader ask.

Well, Google and Flickr hold ample amounts of georeferenced photography that would allow them to calculate viewsheds of these locations.  The locations and groupings of these photos show that St Pauls can not be seen from Parliament, for example, and indicate the places where these locations are viewed best.  Furthermore, the volume of photos provide an indication of the popularity or salience of the location, and could even be provided with directions so that even the least familiar tourist knows what to look for.

Considering the volumes of crowdsourced data they hold, I feel like Google are missing a pretty simple trick here.  So, come on, Google, why not improve this feature and make a walk through the city more interesting to everyone.

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Mapping Taxi Routes in London

One major aspect of my research is spent looking into how people choose their routes around the city.  And to aid me in this, I managed to acquire a massive dataset of taxi GPS data from a private hire firm in London.  I've spent the last few months cleaning up the data, removing errors, deriving probable routes from the point data and extracting route properties. 

It's been a big job, but worth it.  I now have the route data of over 700,000 taxi journeys, from exact origin to destination, over the months of December, January and February 2010-11.  I'm now moving on to the actual analysis of this data, and am beginning to answer some of these questions concerning real-world route choice.  In the meantime, I thought I'd share one striking image that I extracted through this work.

The image below represents an aggregate of journeys on each segment of road on the London road network.  The higher levels of flow are illustrated in red, falling to orange, yellow, then white, with the lowest flow values shown in grey.

Copy_of_taxidatasmalelr

The most popular routes are along Euston Road, Park Lane and Embankment, which may be somewhat expected, but make for a stark constrast with respect to the flow of most traffic in London.  The connection with Canary Wharf comes out strongly, an indication of the company's portfolio, though route choice here is interesting with selection of the The Highway more popular than Commercial Road.

Real insight will come with the full analysis of the route data, something that should be completed in January.  Until then, though, I'll just leave you with this pretty something to look at.

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