Say Goodbye to 2015

Merry OXmas

Merry OXmas everyone! 

via Thomas Bauwens

Quote of the Day: Anxiety

Anxiety will paralyze you, but dispair will break the spell. #phd

— Juliano Spyer (@jasper) December 21, 2015

R Links

1. Check the new updates for ggplot2 2.0.0
   
   
2. Micromap R package to link micromaps to charts via Patrick Gerland
   
   
3. Analyze Google Trends in R
   
   
4. Modeling Incomes and Inequalities in R
   
   
5. 11 Tips on How to Handle Big Data in R
   
   
6. Combine the power of ArcGIS and R via Alex Singleton
   
   
7. Great guide on Manipulating and mapping US Census data in R by Zev  Ross
   
   
8. An interactive map of vehicle collisions in Edinburgh using Shiny, via Keir Clarke

How the most segregated cities in the US and Brazil compare

Using the Dissimilarity Index to measure racial segregation, this his is how the most segregated cities in the US and Brazil compare. A remarkable difference, which I would love to understand more before making any comments.

This chart comes from a great piece on racial segregation in Brazil [only in Portuguese], via Maurício Santoro

[image credit: Nexo, Daniel Mariani, Murilo Roncolato, Simon Ducroquet e Ariel Tonglet]

Google has a call on urban mobility

Google has a call on urban mobility, via Cecilia Mascolo

Journal Spatial Demography

I'm not sure if I have mentioned here in the blog the relatively new journal Spatial Demography. In any case, it promises to be a great journal for population studies, with an incredible editoral board and some very interesting publications.

The journal also brings some more practical publications, like how to use R for Spatial Analysis (parts 1 and 2) and how to Measure Residential Segregation, these two tutorials authored by Corey Sparks.

Urban Picture

A nice little piece of Rio, by Flavio Veloso

Soundtrack: Black Rio

Assorted Links

1. Cities bask in spotlight at Paris climate talks
   
   
2. Why drivers in China intentionally kill the pedestrians they hit ht Leo Monasterio
   
   
3. How complete is OpenStreetMap data coverage?
   
   
4. How the United States generates its electricity
   
   
5. A real-time map of cyber attacks via Flowing Data
   
   
6. Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars? via Cesar Hidalgo
   
   
7. A crowdsourced and open database for  wheelchair-accessible places
   
   
8. Seven days of carsharing in Milan (project website) 

Research Grants

via James Palmer

Brazil Racial Dotmap

A few weeks ago, we made a blog post about the racial dot map of Rio de Janeiro, inspired by Bill Rankin's maps on segregation and the racial dot map of the US.

Fábio Vasconcellos has just pointed out on his twitter to the Racial Dotmap of Brazil, with 190 million dots distributed distributed within Brazilian census tracts. The map was created using a Python script in QGIS, which is available on GitHub.

Each dot represents a person. Blue dots represent white people, while green and red dots represent brown and black people.

[credit: Pata]

Here is the US racial dot map. The two maps are not 100% comparable since the racial categories used in both maps are not exactly the same, but they give a rough comparison of how white and black populations are spatially distributed in both countries.

[credit: Dustin Cable]

India expected to overtake China′s population by 2026

Aron Strandberg has a great series of tweets on demographic trends.

Projection: #India overtaking #China as the world's most populous country. @conradhackett @MaxCRoser pic.twitter.com/epSN0pNw9k

— Aron Strandberg (@aronstrandberg) November 26, 2015

Nigeria is also expected to overtake the US population at some point after 2050.

Projection: #Nigeria closing in on the US as the world's third most populous country @conradhackett @MaxCRoser pic.twitter.com/gKQLRUX0xj

— Aron Strandberg (@aronstrandberg) November 26, 2015

32 Laps around the Sun

Hi all,

it's my birthday today. I'm completing my 32nd lap around the Sun, so I just dropped by to say thanks. Life has been wonderful and I'm truly grateful for being so lucky about so many things, specially about having so many amazing people around me.

image credit: ?

Tracking global bicycle ownership patterns

 Weighted mean percentage household bicycle ownership

click on the image to enlarge it

This map comes from a new paper:  Oke, O., et al. (2015). Tracking global bicycle ownership patterns. Journal of Transport & Health.

Abstract:

Over the past four decades, bicycle ownership has been documented in various countries but not globally analyzed. This paper presents an effort to fill this gap by tracking household bicycle possession. First, we gather survey data from 150 countries and extract percentage household bicycle ownership values. Performing cluster analysis, we determined four groups with the weighted mean percentage ownership ranging from 20% to 81%. Generally, bicycle ownership was highest in Northern Europe and lowest in West, Central and North Africa, and Central Asia. We determine worldwide household ownership patterns and demonstrate a basis for understanding the global impact of cycling as a sustainable transit mode. Furthermore, we find a lower-bound estimate of the number of bicycles available to the world׳s households. We establish that at the global level 42% of households own at least one bicycle, and thus there are at least 580 million bicycles in private household ownership. Our data are publicly available and amenable for future analyses.

The data and supporting code (in Python) are available here.

 

THIS is a proper conference call for papers

[via Thais Rocha]

Climate change and the state of carbon consumption

Consumption, Consumption, Consumption. Nice videographic, by The Economist.

China and India are perceived to be the worst emitters of carbon dioxide, but that view is not so clear when adjustments are made for population, GDP levels, and carbon consumption per person.