Call for papers: Advances in Spatial and Transport Network Analysis

The Int Journal of Geo-information has opened a call for papers for special issue on "Advances in Spatial and Transport Network Analysis". This issue is edited by Henrikki Tenkanen, Elsa Arcaute, Marta C. Gonzalez and myself. This is a great opportunity to create a dialogue between network and social scientists, transport geographers, engineers etc working on transport and mobility networks. This dialogue raises new challenges, though, as discussed in this thoughtful recent paper by Tim Schwanen.

Here is a short snippet of the cfp:

"This Special Issue is dedicated to papers focusing on recent advances in the development of new measures and methodologies to evaluate and analyze the performance of transportation networks. These measures might include, but are not limited to, environmental costs or exposures (e.g., CO2 , noise, pollution); monetary costs (the price of access), complexity, and resilience of multimodal transportation networks; or focus on qualitative aspects of travel, where travel might be seen as a gain instead of cost (such as exposure to aesthetic or green environments). Methodologically, we welcome works using novel ways to measure transport network connectivity, performance, and accessibility, including recent advances in machine learning and AI. Special attention will be paid to papers studying transport-related questions with interdisciplinary approaches."

Public investment and state sponsored speculation

Great piece with a critical take and neat data analysis of how the New York’s High Line project affected real state property prices. It was written by The Dark Matter Labs & Centre for Spatial Technologies teams, who use this case to draw some interesting reflections on public investment  and state sponsored speculation.

credit: Dark Matter Labs & Centre for Spatial Technologies

Visualizing space-time networks

I've said this on Twitter before but I should say it here as well. Craig Taylor and the Ito World team have some of the best data visualizations of geospatial data related to cities and transport networks.

Just a few days ago, Craig tweeted some of his latest work with neat visualizations of drive-time network for catchment area analysis. Here is a video comparing different cities in the UK and a brief explanation on how to read the dataviz.

"30 minute drive time analysis from major UK cities visualised as 3d coral geometry. 

The thickness of artery is proportional to the number of networks connected to it indicating busier routes. The falloff in height is linked to the proximity to the centre. 

Corals aren’t normalised in scale as the purpose of this is visualising the form and pattern the networks create. Animation is a boomerang motion scaling from 0 to 30 min and back again. Congestion/traffic not accounted for."

click at the bottom of the video to watch it in full screen and high definition

Yep, there are some obvious parallels here with Time Geography and in particular with the representation of space-time prisms. The static version of the space-time trees gives a sharper visualization of the data.

The space-time tree, or 3d coral geometry as Craig said.

and the inverted original dataviz, "the drive time web"

The Price of Anarchy in Transportation Networks

Great paper with an interesting application of Braess’s paradox to transportation: closing roads can reduce travel delays.

Youn, H., Gastner, M. T., & Jeong, H. (2008). Price of anarchy in transportation networks: efficiency and optimality control. Physical review letters, 101(12), 128701.

Abstract:

Uncoordinated individuals in human society pursuing their personally optimal strategies do not always achieve the social optimum, the most beneficial state to the society as a whole. Instead, strategies form Nash equilibria which are often socially suboptimal. Society, therefore, has to pay aprice of anarchy for the lack of coordination among its members. Here we assess this price of anarchy by analyzing the travel times in road networks of several major cities. Our simulation shows that uncoordinated drivers possibly waste a considerable amount of their travel time. Counterintuitively, simply blocking certain streets can partially improve the traffic conditions. We analyze various complex networks and discuss the possibility of similar paradoxes in physics.

Hyejin Youn also has some more recent and equally interesting papers on urban scaling laws, in case you're interested.

credit: Youn et al (2008)

Related paper:

• Case studies on the Braess Paradox in abstract and microscopic models

Google has a call on urban mobility

Google has a call on urban mobility, via Cecilia Mascolo

Returners and explorers dichotomy in human mobility

Pappalardo, L., Simini, F., Pedreschi, D., Barabasi, L. et al. (2015). Returners and explorers dichotomy in human mobility. Nature Communications, 6. doi:10.1038/ncomms9166

Abstract:

The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.

[image credit: Pappalardo et al. 2015]

How far can you go from any European capital to anywhere else by train?

Stephan Franziskus runs an interesting blog where he has written a great post about the history Isochrone Maps. These maps use color gradients and contour lines to visualize the places one can reach from a single destination within different time windows (isochrones).

obs. We have already posted about these maps in this blog to show some isochronic maps of American railways in the 1800s, a similar map departing from Rome during the times of the Roman Empire, and a contrast between Old and New techniques of isochrone maps.

Stephan Franziskus shows a very precious map, created by Francis Galton (yeap, the same Galton who demonstrated the central limit theorem using a 'bean machine'), which shows the number of days to get to different places in the world if you were to departure from London in 1881.

More recently, Peter Kerpedjiev has applied the same idea to estimate the places one can reach from different European capitals within different time windows using only trains and walking. There is a very good piece by Lazaro Gamio published in The Washington Post covering Kerpedjiev's work. You might like it.

click on the image to enlarge it

[image credit: Peter Kerpedjiev, Lazaro Gamio, WP]

Mapping the Transit System of Rio with GTFS data

Here is my first experience mapping a transit system using GTFS data!

The map shows the bus public transport system of the city of Rio de Janeiro as of November 2014. The blue lines represent the bus routes, where color intensity and width vary according to service frequency per day. I have also included the city's street grid in the background (gray) to give a visual idea of the transport system coverage.

click on the image to enlarge it

The map was created in R (ggplot) using data from OpenStreetMap (OSM) and GTFS data provided by Fetranspor/Va de onibus (this is basically the same transit data people have access to in their mobile apps). This great map by James Cheshire was the main inspiration, although I couldn't get to such a nice result as James did.

I've created this map a couple of months ago but still need to organize the R code before sharing it here with you. I am also working on an interactive version of this map, but this may take a little longer  I've been facing some deadline issues lately  Stay tuned.

Related Links

• Taxi vs Public Transit in major US cities
• Modeling Transport accessibility with open data 
• Real-time map of 202 transit systems worldwide

Traffic Density in US Roads (Data !)

Eric Fischer (Mapbox) has has created an interactive map showing traffic density measurements for US roads. The data comes from the Highway Performance Monitoring System (HPMS /DOT) and you can download it here. (via Keir Clarke)

View full screen map

The multilayer network of public transport in the UK, now available

Riccardo Gallotti and Marc Barthelemy have recently published an interesting paper on the Anatomy and efficiency of urban multimodal mobility.

As a by-product of their research, they have also made a great contribution to transport studies in the UK. This is because the data they gathered on the UK public transport system are now made available. This includes a really large multi-modal system with flights, rail, ferry, coach, subway, bus, tram, light rail, etc

Thanks to Gallotti and Barthelemy, you can easily have access to all these data for free, here !

[image credit: Gallotti and Barthelemy, Rendered with MuxViz]

[image credit: Gallotti and Barthelemy, Rendered with MuxViz]

One Day on Waze

'One Day on Waze' is a series of data visualization videos showing 24 hours worth of data on Waze. The cities include: Boston, Jakarta, Los Angeles, Miami, New York, Rio de Janeiro, San José, and Tel Aviv.

One Day on Waze from Justin Chen on Vimeo.

This is the video of Rio, where it's possible to spot some ugly red bottlenecks.

One Day on Waze - Rio de Janeiro from Takashi Kawashima on Vimeo.

Related Links:

• How we move in cities
• A day in the life of a city
• Visualizing Disabled Freedom Pass trips on the London Underground 
• NYC Taxi Trips Data from 2013

What a neuroscientist is doing at Uber

Bradley Voytek writes about what a a neuroscientist is doing at Uber* (ht MR).  In short, Voytek is finding network patterns, mapping different types of users, etc etc (ie data science at its broadest sense).

Some of San Francisco’s Uber Networks

[image credit: Bradley Voytek]

*By now, you should have heard about Uber. If you haven't, I would recommend you to check this Freakonomics episode where they talk about Uber, what it is, its promises to the future of urban transport and some of its controversies. 

Assorted Links

1. What happens when you try to bring your Nobel Prize through airport security?
   
   
2. 1907-1915 - Russia Before the Revolution, in Color
   
   
3. Spain is the elevator capital of the world. ht Matt Phillips
4. Transport costs and inequalities in vaccine coverage in Africa
   
   
5. Heads Up: UC Berkeley seeks applications for faculty positions in Transportation Planning and in Housing and Urban Development
   
   
6. Vertical ballet
   
   
7. Sad pictures of the rapid deterioration of Detroit, captured by Google Street View
   
   
8. A camera filter that makes it possible to see carbon emissions, literally

FLIR: The Invisible Made Visible from Oceanic Preservation Society on Vimeo.

Assorted links

1. LSE Report - Cities and Energy: Urban Morphology and Heat Energy Demand
   
   
2. Experimental evidence of massive-scale emotional contagion through Facebook  (via MR)
   
   
3. 18th-century London paintings meet Google Street View
   
   
4. Did General Motors lead an automotive conspiracy to undermine streetcar lines in the US? Apparently not
   
   
5. The 10 world cities with the highest murder rates
   
   
6. Are UK migrating to the US and becoming lawyers in Illinois? This and other spurious correlations.
   
   
7. Free Statistical Software, SAS University Edition (thanks Bernardo Queiroz for the tip)
   
   
8. The first ever government census of schools in Mexico: “The Theft of the Century”
   
   
9. Range of average lengths of PhD dissertations across the disciplines

[click on the image to enlarge it]

[image credit: Marcus Beck]

NYC Taxi Trips Data from 2013

A while ago, we have pointed out to some data visualization of taxi trips in New York. Recently, Chris Whong and Andrés Monroy got access to a copy of the taxi records from 2013 and published them on the web. You may download the data here (ht Bernardo Furtado).

And you may also play a little with this great visualization of the data created by Eric Fischer (@enf).

For a more theoretical discussion, take a look at this post by Daniel Brownstein: Cabstopping: Data Visualization and the Re-Mapping of Urban Space

Chart of the Day

Comparing the UK Department for Transport forecasts of road traffic with actual road traffic (via George Monbiot). Interestingly, it repeats the same systematic error the US Department of Transportation has been making for years.

The full story is here: 

Since the 1980s, the Department for Transport has consistently forecast traffic growth along a steep trajectory. But the distance covered by car drivers in England is now 7% lower than it was in 1997. The total volume of traffic has flatlined since 2002, nixing every prediction the department has made. Last year, 32 transport professors wrote to the secretary of state pointing out that, in the absence of traffic growth, "the basis for major infrastructure spending decisions appears to be changing".

The only thing likely to induce more traffic growth, they argued, is building more trunk roads, and that would put intolerable pressure on the city streets into which they feed. The facts might have changed, but the policy remains the same. The department continues to make the same failed forecasts, using the same failed model. The desire to build – and to appease the construction industry and motoring lobby – comes first, and the forecasts are made to fit.

[image credit: Better Transport]

The Landscape of Transport Studies

In a recent Editorial for the journal Transport Reviews, David Banister presents some interesting numbers about the growth of transport-related journals.

I have searched through the Ulrich’s database and have identified 85 transport journals that are refereed and are available in print. [..] the numbers of transport journals has more than doubled from 1990 – 2010, and a further 14 more have been started in the last three years.

David also reports that over the years 1945–2013 there have been 58,000 papers published with the word transportation in the title.

Irrespective of the numbers, it is again the growth that stands out. Less than 1% of these papers with the word transportation in the title were published prior to 1960, and just over 8% by 1980. Between 1980 and 2000, this figure increased to 36%, and the remaining 64% have been published since 2000. 

Related Link;

• Tim Schwanen comments on Elsevier`s list with the 25 Most Popular Papers on Transport by 2013 (The list is here, and it`s open access).

Visualizing Disabled Freedom Pass trips on the London Underground

Gareth Simons has done an amazing work visualizing Disabled Freedom Pass trips on the London Underground. More details on his website.

obs. @transportforall has also produce nice map showing how the Tube network looks like if you can’t use stairs. Simple map with strong message.

Related Link:

• A Guide To Alternative London Tube Maps

Rise in Transport Costs in Brazil

In June 2013, the rise in Bus fares were the spark for huge protests in more than 80 cities in Brazil. In this report (published in Portuguese last year) we analyze some of the main pricing and funding issues of public transport in Brazil. The rise in transport costs in the country is just of them, and in which govermental policies have played an important role subsidizing gasoline consumption and the purchase of new cars.

For more details and a further discussion on this issue, you may read the report here.

And for now, here are some figures and a chart showing the increase in inflation and its transport components between 2000 and 2012 for the largest Brazilian metropolitan areas.

IPCA (Inflation): 125%

Bus fares: 192%

Gasoline: 122%

Owning a vehicle: 44%

[click on the image to enlarge it]

Label Translation:

• IPCA = consumer price index
• Tarifa de ônibus = Bus fare
• Tarifa de Metro = Subway fare
• Gasolia = Gasoline
• Veículo próprio = Owning a vehicle (car or motocycle), including purchase and maintenance expenses.

The motorcycle Kuznets curve

Nishitateno, S and Burke, P (2014) The motorcycle Kuznets curve. Journal of Transport Geography. Volume 36, Pages 116–123.

Abstract

The evolution of motorcycle ownership is a crucial issue for road safety, as motorcyclists are highly vulnerable road users. Analyzing a panel of 153 countries for the period 1963–2010, we document a motorcycle Kuznets curve which sees motorcycle dependence increase and then decrease as economies develop. Upswings in motorcycle ownership are particularly pronounced in densely populated countries. We also present macro-level evidence on the additional road fatalities associated with motorcycles. Our results indicate that many low-income countries face the prospect of an increasing number of motorcycle-related deaths over coming years unless adequate safety initiatives are implemented.

Fig. 4. Regression predictions for the motorcycle share of the motor vehicle fleet, for countries with mean and 90th-percentile log population density *.

[image credit: Nishitateno and Burke, 2014]

* (corresponding to 66 and 371 people per square kilometer, respectively). Prediction lines use a mean country fixed effect and the year-2010 time effect.