Sunday, November 30, 2014

Mobile Lives

A good idea for Xmas gift.

[image credit: Liam Walsh/The New Yorker, via Arthur Charpentier]

Related quote of the day.

Thursday, November 27, 2014

The remains of a ghost town 28 years after Chernobyl

Danny Cooke has used a drone to give a bird's eye view of Pripyat (Ukraine), a beautiful and sad footage of the remains of a ghost town 28 years after the Chernobyl nuclear disaster, which happened in 1986. A great piece of work.

***Soundtrack 'Promise land' by Hannah Miller

Wednesday, November 26, 2014

Real-time map of 202 transit systems worldwide

The team at geOps have used GTFS feeds to create an incredible real-time map of 202 transit systems around the world (mostly USA and Europe but there are a few cities in Mexico, Australia and Israel). If you zoom in enough you can see the vehicles moving, click on stops and vehicles to get information etc  (via @BrendnCasey).

You can check the map here and read some more information here, in German.

Related Links:

Monday, November 24, 2014

What a neuroscientist is doing at Uber

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. 

Sunday, November 23, 2014

Quote of the Day

Soundtrack for the weekend:

Saturday, November 22, 2014

Incorporating Cognitively Perceived Urban Space in Economic Models

Furtado, B. A. (2011). Neighbourhoods in urban economics: incorporating cognitively perceived urban space in economic models. Urban Studies, doi:10.1177/0042098010391288.
This paper proposes that urban economic analysis would benefit from the use of cognitively perceived neighbourhoods, which are discussed within urban studies. Georeferenced data should be aggregated by spatially bounded units that are identifiable by citizens in order to enrich one-dimensional distance as the sole variable to bring urban complexity into economic models. Multivariate analysis is applied to data from Belo Horizonte, Brazil, to formulate four indices, ranked by neighbourhoods that together represent a spatially complex, non-linear influence on urban real estate markets. The results of the indices by neighbourhood are then tested against a traditional specification in an econometrics exercise that does not include the concepts and indices put forward. The definition of neighbourhood used and the empirical results provide a thorough description of urban fabric that can fully and more accurately represent urban influence in economics while avoiding abstract distance measurement.

Friday, November 21, 2014

Urban Picture

[image credit: ?]


Sunday, November 16, 2014

Modeling Transport accessibility with GTFS, OpenStreetMap and OpenTripPlanner Analyst

David Levinson points out to this great presentation by Kevin Webb talking about Modeling Transport accessibility with open data. Kevin and his team at Conveyal combine GTFS data, OpenStreetMap and OpenTripPlanner Analyst to make simple and yet sophisticated analysis of transport accessibility.

I feel very enthusiastic about Kevin's presentation because part of my PhD thesis will be closely connected to some of the questions he raised. In one of my papers, I will analyze the distributional aspects of how the addition of new transport infrastructure/services in Rio de Janeiro (particularity related to mega events) will impact urban accessibility across the metropolitan area. I'll post some updates about my PhD research in the near future as I suffer to make some progress on it.

There are dozens of other thoughts, papers and Links on GTFS and transport accessibility I would like to share here (including the research conducted by Andrew Owen, D. Levinson and other researchers), but I'm sure we will have many more posts to do this in the near future.

Related Links:

[image credit: Andrew Byrd and OpenTripPlanner Team]

Tuesday, November 11, 2014

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:

Friday, November 7, 2014

What is the city, according to Shakespeare

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Thursday, November 6, 2014

Monday, November 3, 2014

Public incentives to private transportation in Brazil

Brazil fact of the day:

The amount of governmental subsidies to private transportation in Brazil totaled R$ 19.38 billion in 2013. This figure is larger than all transport investments for the World Cup (R$ 14.38 billion) and it is almost twice the investments made in public transport in the whole country in the same year (R$ 10.2 billion).

This is from a nice piece published yesterday by Cassia Almeida and her team in O Globo, one the the biggest Brazilian newspapers . You may read the story [in Portuguese] here.

Sunday, November 2, 2014

Urban Picture

Great picture by Chris Hadfield (NASA) showing the bright lights of Cairo and hundreds of smaller cities along the Nile river. The lights on the northeast show of Gaza, Tel-Aviv and Jerusalem.

[click on the image to enlarge it]
[image credit: Chris Hadfield/NASA via Leo Mirani]