Commuting patterns in Germany's metropolitan areas

Interesting article about the commuting patterns in Germany's major metropolitan areas. If you don't read German like myself, use Google Translator. 

credit: Paul Blickle for Zeit

Assorted Links

1. You know that Pythagoras's theorem? It was in fact discovered by the Babylonians a 1000 years before Pythagoras was born
   
   
2. How the Layout of Urban "Cells" Affects The Function and Success of Neighborhoods
   
   
3. China has passed the mark of 100 cities above 1 million people and this is good
   
   
4. Interesting paper showing that long commutes undermine school performance of students
   
   
5. Great title for a paper: "Lost in transportation: Information measures and cognitive limits in multilayer navigation"
   
   
6. In case you lost the chance to see the solar eclipse last week, don’t despair. This interactive globe shows when the next eclipse in your country will happen
   
   
7. Scientists Used CRISPR to Put a GIF Inside a Living Organism’s DNA
   
   
8. Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble

Posterior distribution of projected change in life expectancy at birth from 2010 to 2030

Commuting hinterland of major UK cities

Alasdair Rae (who has a great blog by the way) has posted a nice short collection of maps comparing the commuting hinterland of some major UK cities.

City Footprints: follow-up blog on commute patterns for lots of cities https://t.co/7fSGdPL18q cc @RileyResearch pic.twitter.com/MkuhCx79Xd

— Alasdair Rae (@undertheraedar) 27 May 2016

Assorted Links

1. Night lights and regional income inequality in Africa
   
   
2. Will autonomous vehicles really be safer than conventional cars? Here is why answering this question is not so simple via Tyler Cowen
   
   
3. Real-time visualization of Ebola evolution and geographic spread
   
   
4. Higher Gender Wage Gap is associated with higher Domestic Violence rates
   
   
5. The Global Metro Monitor 2014
   
   
6. Some Beautiful Abandoned Places In Europe
   
   
7. How Americans Get to Work, neat interactive dataviz by Nathan Yau
   
   
8. Nice tool for comparing the size of countries, ht Pablo Astudillo
   
   
9. A terrific visual and interactive explanation of Gridlock vs. Bottlenecks, by Lewis Lehe

Commuting times and housing costs in eight major cities

Alan Smith has put together a nice series of charts comparing commuting times and housing costs compared in eight major cities for a piece in the FT. Good read. I thank Jakob Engel for the pointer.

Related Links:

• Average Commute Time Worldwide
• The Evolution of Commute Times in Brazilian Metropolitan Areas (1992-2013)
• Commuter Density in Four World Cities

Assorted Links

1. A special issue from Science Magazine on The science of inequality
   
   
2. How much does it cost to study in an Ivy League university?
   
   
3. Meet the Robin Hood of Science and download any academic paper for free at Sci-Hub
   
   
4. Beautiful photographic work on China's Ghost Cities
   
   
5. Student hacks Raspberry Pi to run college bar at Oxford (ht Jakob Engel). The piece is from 2013, but I've heard the system is still working fine.
   
   
6. How cold can Chicago get?
   
   
7. A map of NYC street trees by species
   
   
8. 3 years later, Syrian refugee camp booms into makeshift city
   
   
9. A fairly detailed map of commuting trips in UK cities, by Oliver O’Brien

Commuter Density in Four World Cities

Ricky Burdett (LSE Cities) has just published a nice piece on the Guardian discussing how four cities have quite different ways to respond to economic, political and environmental shifts. It's a good article to disclose their research projects, which you may find here.

This is already becoming a mantra:

"Higher densities can facilitate more sustainable public transport, walking and cycling, making it more efficient to provide services and promote urban vitality. These advantages depend, however, on high-quality urban design and the effective city management to minimise overcrowding, stress and pollution."

Related Links:

• Urban Density Patterns in 9 Global Cities
• Population distribution in European capitals
• The Rise and Fall of Manhattan’s Density

Assorted links

1. Epic Extreme commute: From New Zealand to rural Iceland !(ht Vanessa Nadalin)
   
   
2. The economics of population imbalance
   
   
3. The paradox of Space and Time explained with  8-Bit Philosophy
   
   
4. 7 ways to make your Google Scholar Profile better
   
   
5. The Urban Fabric of English Cities and the Urban Footprint of Scottish Cities
   
   
6. Find Where The Streets Have Your Name, literally
   
   
7. Penguins are being raped by seals. Warning, strong images NSFW (thanks to my poor sick friend Leo Monasterio)
   
   
8. Locations of every photo from International Space Station via Flowing Data

[image credit: Nathan Bergey]

The Evolution of Commute Times in Brazilian Metropolitan Areas (1992-2013)

 shameless self-promotion alert  Tim Schwanen and I published a working paper in 2013 where we analyzed how commute times have evolved in Brazil between 1992 and 2008, with special attention to differences between metropolitan areas, income levels and gender (Portuguese version here). 

The Brazilian newspaper O Globo took our study and made a great work of data visualization a couple of days ago, when they published a much improved and interactive version of Chart #2 in our study. This is their static version of the chart, comparing the evolution of average commute time across Brazilian metropolitan areas between 1992 and 2013. Click here for the interactive version (or here).

[click on the image to enlarge it]

[image credit: Daniel Lima with data from Pereira and Schwanen, 2013]

I would love to make some comments on these findings since we have a marked contrast between different areas, both in terms of trends and levels. I will spare you from reading my typos here, though. In case you are interested, you may read the original publication, here.


Related Links:

• Average Commute Time Worldwide
• Public incentives to private transportation in Brazil

Average Commute Time Worldwide

This chart was taken from this study here. In general, commute times are relatively long in Brazilian metropolitan areas, especially when their wealth/population size is taken into account.

Obs.1: If you know of data sources (preferably household surveys) with commute time information for other metropolitan areas across the globe, I would to receive a message from you.

Obs.2: If you spot a name in the chart you've never heard of, it's probably a Brazilian metropolitan area.  

[image credit: Pereira and Schwanen, 2013]

Related Link:

• Parking Rates Worldwide
• Wendell Cox recently wrote a post about the 10 cities with the worst traffic in the world, according to the Tom Tom Traffic Index.  

Journeys to Work in the UK

James Cheshire and Oliver O’Brien alert us that the UK Office for National Statistics (ONS) have released the Travel to Work Flows based on the 2011 census. James and Oliver have also developed a nice interactive map you can "play" with: Commute.DataShine. Rob Fry also calls attention to another great interactive visualization here, by ONS.

[image credit: James Cheshire and Oliver O’Brien]

New 'workplace zone' Census stats for the UK

Robin Edwards calls attention to a new set of statistical areas developed by the Uk Office of National Statistics to report workplace data from the 2011 Census, called Workplace Zones. He has made some nice maps that give a glimpse of the potential these areas can have to urban and transport studies. If only we had these data when we were writing this paper. :(

Related Link: The U.S Census Bureau has made available an online mapping tool that displays demographic info of states, counties, and census tracts. It`s called Census Explorer and it also presents some data on retail and a commuting edition for 1990-2000-2012.

[image credit: Robin Edwards]

Assorted links

1. Contribute to a survey on the connection between the built environment and personal health: It took me 3 minutes to answer this short questionnaire. The survey is organized by our loyal reader Benjamin Littenberg.
   
   
2. Why do birth rates peak at different times in different places?
   
   
3. Longer commutes in Britain (via Peter Gordon)
   
   
4. An ageing economy will be a slower and more unequal one
   
   
5. Great piece by Simon Jenkins on the coming Mega Events in Rio
   
   
6. Space Syntax of street connections in the Greater South East of England
   
   
7. Gary S. Becker has passed away.  Many interesting links about his work at MR
   
   
8. The Pollution Burden in California by census tracts (via Stephen Miller)

[image credit: LA Times]

When Social Networks meet Public Transport

Among the Best Scientific Visualizations of 2013 (by Brandon Keim), one particular paper attracted my attention as the authors analyze the encounter network of bus passengers in Singapore. Here is the paper:

[image credit: Sun et al, 2013]

Understanding metropolitan patterns of daily encounters." By Lijun Sun, Kay W. Axhausen, Der-Horng Lee, Xianfeng Huang. Proceedings of the National Academy of Sciences, Vol. 110 No. 34, August 20, 2013.

Abstract:

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters’ bounded nature. An individual’s encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of “familiar strangers” in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or “structure of co-presence” across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and—particularly—disclosing the impact of human behavior on various diffusion/spreading processes.

Detailed Animation of Travel Surveys

Despite the amazing potential of new technologies (eg social media, mobile phones) to inform us about spatial patterns in urban mobility, traditional Household Travel Surveys are still a key source of data with preciously detailed information on socio-economic and demographic characteristics.

Recently, Fletcher Foti (Ph.D. student at Berkeley) created a striking detailed animation daily travel patterns using the most recent surveys from the San Francisco Bay Area, Los Angeles, and New York. You may check the animation here and have more information at Emily Badger's post.

ps. If you are interested in travel surveys, you should take a look at the Related Links, below.

[Image Credit: Fletcher Foti, Synthicity]

Related Links:

• Metropolitan Travel Survey Archive (US) !
• National Household Travel Survey (US)

Bicycles access and school enrollment

Análise Real (a Brazilian economics Blog) called my attention to this paper by Profs. K. Muralidharan and N. Prakash where they analyze an Indian program "... aimed to reduce the gender gap in secondary school enrollment by providing girls who continued to secondary school with a bicycle that would improve access to school."

By the way, their results show the Cycle program has impressive results when compared to more traditional programs (i.e. conditional cash transfer). 

This paper is going to the top of my reading list! Here is the paper. And here is a video briefing on the study.

Assorted links

1. OECD Health Data 2013
   
2. The infrastructure behind Dutch cycling (via MR)
   
3. How people get to work across England and Wales?
   
4. Red Bull Illume Photo Contest 2013
   
5. Many transit visualizations in one place (via David Levinson)
   
6. INDEPTH launches a repository of (longitudinal) demographic and health data for low/middle-inc.countries (via Emilio Zagheni)
   
7. A century of jittery oil production and prices
   
8. Effect of a conditional cash transfer programme on childhood mortality in Brazil (via @HansRosling)
   
9. Chinese Food and Population Density by Neighborhood in NYC

13th WCTR + Commute Time in Brazil

Dear readers, next week I'll be in Rio for the 13th WCTR. I'm presenting a working paper Tim Schwanen and I have recently published. In this study we explore 18 years of commute time trends in Brazil. I was planning a dedicated post to talk about this study but I've been quite busy these days. 

So here it is. You may download it here:

Commute Time in Brazil (1992-2009): Differences Between Metropolitan Areas, by Income Levels and Gender

[Portuguese version]

ps. It would be great to meet some of you in case there is anyone out there that's also attending the conference. If there is anyone out there, just leave a comment of drop me an email.

A universal model for mobility patterns

This is one of the best papers I have read in the last few years. Neat stuff! 

Simini, F; Gonzalez. MC; Maritan; A; Barabasi, AL. (2012). A universal model for mobility and migration patterns. Nature 484, 96–100. (ungated link)

Abstract:

Introduced in its contemporary form in 1946, but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.

ps. I've postponed this post for far too long because I was planning to start a study with some testing on the radiation model using Brazilian data (commuting, migration, national flights). However, I might face some (good!) turbulence in my professional/academic life in the next few months and I don't know when I'll get time for that.

Commuting landscapes (Fig 4)

[Image Source: Simini et al, 2011]

Using Cell-Phone Data to Improve our Cities

Imagine what questions you could answer if you had access to movement data collected from millions of cell-phone users. These kind of data bring and endless potential for research in several areas such as transportation, epidemiology, social networks and civil defense. MIT is holding this week the NetMob 2013 Conference, a conference focused on mobile phone data analysis. There are several interesting papers that are really pushing the envelope.

A paper by Francesco Calabrese and colleagues stands out as they have developed an interactive system to optimize public transit networks using mobile phone data under the Orange D4D Challenge*. Still, I believe there's something to improve in using such kind of data in a way that differentiates people from higher and lower income groups.

*Orange, a big telecom carrier, lauched a challenge last year called "Data for Development" (D4D). In short, you could send them a 250-words research project and get access within a week to a really large mobile phone dataset containing 2.5 billion records (calls and text messages) exchanged between 5 million anonymous users in Ivory Cost. Many papers presented at NetMob 2013 Conference were developed using Orange D4D data.

By the way, the Senseable City Lab (MIT) is also working on a related project (Signature of Humanity) trying to look at mobility patterns comparing New York, London, and Hong Kong. Here's a promotional video:

Related Links:

• Crowdsourcing road congestion data
• Big Data from cheap phones