Sunday, March 24, 2019

GTFS rotuer in R

For those interested in public transport research and planning, there is a new R package to do public transport routing using GTFS data. The gtfs-router package uses the power of C++ and it is quite efficient. This is only the release version of the package (0.01), though. There is a lot of room for improvements but I hope gtfs-router becomes a strong competitor to OpenTripPlanner in a couple of years.

The package was created by Mark Padgham (twitter), who is the author of/contributor to various other transport-related packages such as dodgrbikedata and osmdata. Kudos to Mark!

ps. The capabilities of R for transport research and planning are greater than never. There is a very vibrant and active community with dozens of other packages. Perhaps this should be the topic for a future post.

image credit: Rstudio and smtd

Thursday, March 21, 2019

Ipea Seminar - Infusing Urban and Regional policy with Geographic Data Science

Today we are having Dani Arribas-Bel (Geographic Data Science Lab, University of Liverpool) presenting a seminar at the Institute for Applied Economic Research (Ipea). Daniel (websiteTwitter) will be talking about his research agenda on "Infusing Urban and Regional policy with Geographic Data Science". The seminar will start at 3pm (local time). My apologies this post comes in short notice. This week has been hectic.

Daniel is part of the development team of  PySAL and he is a prolific researcher and also a great enthusiast of open data/software and research reproducibility. In fact, many of his papers (code and data) and teaching material are available on his Github repo.

Summary of the presentation:
The recent explosion in availability of new forms of data poses significant opportunities to how we analyse cities and regions, both in academic and policy contexts. Over the last decade, three families of data have emerged in this context. One is digital traces of individual activity. From credit card transactions, to mobile phone calls, to thoughts and feelings we decide to share through social media, more and more bits of our life are being stored digitally as data that a computer can understand. The second comes from an increasing number of sensors, from traffic controllers to nano-satellites orbiting the Earth, which are constantly recording information about the environment. The third one has existed for longer but has not been available until recently: a few years ago, governments started releasing data on their internal operations that used to be parked in (closed) silos. This presentation will walk through several examples where new forms of data are applied to tackle new questions or obtain new perspectives on long-standing challenges of regional and urban analysis. As part of this whirlwind tour, we will also spend some time trying to understand what the main challenges, methodological advances, and risks that "accidental data” pose are, and will emphasise the tremendous opportunities they unleash.

credit: Dani Arribas-Bel

Tuesday, March 19, 2019

The race for the largest city in the world over the past 500 years

In previous posts, I've pointed out to an incredible open dataset with comprehensive population estimates of human settlements and cities for the past 6,000 years. The talented John Burn-Murdoch used some of these data to create this nice animation showing the changing ranks of the 10 largest cities in the world since 1500. The full code for the animation is available here.

Friday, March 15, 2019

Creating a simple world map of cities in R

Mike (from cool but useless) asked on twitter if there is any package in R with with lats/longs of lots of cities. In fact there is. Here is a simple code to create a world map of cities with population larger than 40K in R using maps::world.cities and ggplot2.

ps. These population data in the maps package refers to 2016 estimates. If you want city population data from previous years, you might remember that we have previously posted about this open dataset with 6,000 years of global urbanization.

Wednesday, March 13, 2019

Racial inequity in who pollutes and who gets exposed to pollution

A recent paper led by Chris Tessum (University of Washington) and published in PNAS brings novel estimates of racial-ethnic disparities in air pollution cause and exposure in the US. They find that air pollution is disproportionately caused by consumption by white Americans, but disproportionately affects Black and Hispanic Americans. The air pollution input-output model used in the paper is freely available and there's an experimental live version of the model running, here. (thanks Marko Tainio for pointing to this paper)

Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 pollution. We use these results to explore “pollution inequity”: the difference between the environmental health damage caused by a racial–ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a “pollution advantage”: They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a “pollution burden” of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002–2015 for all three racial–ethnic groups, but pollution inequity has remained high.

credit: Tessum et a..

Saturday, March 2, 2019

Off The Road

It's Carnaval season in Brazil and I'll be in small fisherman village mostly disconnected. The level of activity in the blog will remain lower than usual this week (my Twitter is probably be less affected though).

Marau, Bahia, Brazil