Map of Population Density Lines in R

If you are familiar with this famous Joy Division cover, you might remember that last year we shared a link that shows you how to reproduce the cover using R ggplot2. If you are a big fan of Joy Division and R, you should know that there is an R package just for that (by @mikefc).

About three years ago in 2014, James Cheshire created the Population Lines Print, a stylized map using lines to show population density in the world. It uses roughly the same data visualization style used in the Joy Division cover. 

credit: James Cheshire

How can you create a nice-looking map like this? Ask no more. James has recently shared the R script and a bit of the history behind his map. Henrik Lindberg has also generously written a gist with a simple and reproducible code to create a map with the same style showing the distribution of the population density in Europe, using R and ggplot2.

	# data from http://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/population-distribution-demography/geostat
	# Originally seen at http://spatial.ly/2014/08/population-lines/
	# So, this blew up on both Reddit and Twitter. Two bugs fixed (southern Spain was a mess,
	# and some countries where missing -- measure twice, submit once, damnit), and two silly superflous lines removed after
	# @hadleywickham pointed that out. Also, switched from geom_segment to geom_line.
	# The result of the code below can be seen at http://imgur.com/ob8c8ph
	library(tidyverse)
	read_csv('../data/geostat-2011/GEOSTAT_grid_POP_1K_2011_V2_0_1.csv') %>%
	rbind(read_csv('../data/geostat-2011/JRC-GHSL_AIT-grid-POP_1K_2011.csv') %>%
	mutate(TOT_P_CON_DT='')) %>%
	mutate(lat = as.numeric(gsub('.*N([0-9]+)[EW].*', '\\1', GRD_ID))/100,
	lng = as.numeric(gsub('.*[EW]([0-9]+)', '\\1', GRD_ID)) * ifelse(gsub('.*([EW]).*', '\\1', GRD_ID) == 'W', -1, 1) / 100) %>%
	filter(lng > 25, lng < 60) %>%
	group_by(lat=round(lat, 1), lng=round(lng, 1)) %>%
	summarize(value = sum(TOT_P, na.rm=TRUE)) %>%
	ungroup() %>%
	complete(lat, lng) %>%
	ggplot(aes(lng, lat + 5*(value/max(value, na.rm=TRUE)))) +
	geom_line(size=0.4, alpha=0.8, color='#5A3E37', aes(group=lat), na.rm=TRUE) +
	ggthemes::theme_map() +
	coord_equal(0.9)
	ggsave('/tmp/europe.png', width=10, height=10)

view raw europe.R hosted with ❤ by GitHub

and you get this:

credit: Henrik Lindberg

UPDATE: Carson Sievert‏ shows how you can add two (2!) more lines of code to make this map interactive.

I made @galka_max Europe map interactive with 2 more lines of #rstats code https://t.co/KZp0w5YoFH pic.twitter.com/a4GaFJRJVn

— Carson Sievert (@cpsievert) 26 April 2017

An unorthodox approach to spatial clustering

I left a question on gis.stackexchange about an unorthodox approach to spatial clustering that came to my mind a couple of days ago. I would be glad to hear if you have any thoughts on this. If you have any comments/answers, this time I'll ask you to write them on the gis.stackexchange website.

There is a boom in bicycle research

Jennifer Dill (twitter) has recently written a blog post with a quick overview on the research on bicycles found in the Web of Science since the 1930s. 

Although we don't know how the 'bicycle literature' has been growing relative to all publications in transport/mobility, there is a good sign there is boom in bicycle research! The method used by Jennifer to identify the publications is not supper systematic but it's insightful anyway. Jennifer's blog post is a quick and interesting read.

credit: Jennifer Dill

Quote of the day: programming

"Computers are stupid and they will act like they don't know what you're talking about" #SfAMECS #Bioinformatics pic.twitter.com/OR1hjv1Z2s

— Jennie French (@jenniefrench95) 19 April 2017

Assorted Links

1. A rank of North American Urban Planning Scholars Using Google Scholar Citation Profiles, by Tom Sanchez (twitter)
   
   
2. On a similar note, an interesting commentary on the power of the unrelenting impact factor of academic journals and some interesting arguments to why the impact factor of journals should not be used for evaluating research
   
   
3. Isoscope:  nice work by Flavio Gortana (twitter), showing how isochrones by car vary across space and time
   
   
4. How One Brilliant Woman Mapped the Ocean Floor’s Secrets via Sabrina Lai
   
   
5. Possibly the greatest scene in documentary history via David Levinson (twitter)
   
   
6. Bill Rankin's new work on Mapping Slavery Against US Incarceration
   
   
7. A 15-min video giving an interesting summary of the largest US health insurance experiment in history - ht Carl Schmertmann
   
   
8. Beautiful data visualization of music, by Nicholas Rougeux
   
   
9. The Vertical City: The Price of Land and the Height of Buildings in Chicago 1870-2010, by Gabriel M. Ahlfeldt (twitter)

If you ask me how my PhD is going...

A glimpse into the future of autonomous vehicles

Cesar Hidalgo (Twitter) has shared this mesmerizing video by People's Daily, China. It shows hundreds of autonomous robots sorting parcels in a warehouse of a Chinese delivery company.

It makes you think about many things related the technology density of the Chinese economy and the coming changes in the labor market of China and beyond. The obvious thought that occurred to me, though, is how these warehouses can be thought as a super simplified beta-version of integrated systems of autonomous vehicles we might see in cities in 100 years form now... maybe 50.

Two new libraries for working with Spatial data in R

A quick heads up for those interested in using R for spatial analysis and mapping data. There are two relatively new libraries you might want to check out:

• The first one is the ggspatial library, created by Dewey Dunnington. ggspatial is a great package that makes it super simple to create maps using ggplot. It uses a single geom_spatial() for all types of spatial objects (polygons, lines, dots, etc.) and it dispenses the use fortify(), making things much faster.





• Another library some of you might find useful is sf (as in simple features), created by Edzer Pebesma. This library makes it much faster to conduct several spatial operations, like reading/writing data, intersecting spatial objects, computing shortest distance matrices etc. The sf library is integrated with ggplot2 for creating maps with geom_sf() and it also makes it easy to connect to spatial data bases. This promises to be a real game changer for spatial analysis in R.

A biograhical note

by Roz Chast for the New Yorker

Google Self-Driving Bike

This is from last year, but we can always use it on April 1st :)