Assorted Links

1. Paper: "exposure to socioeconomic inequality in an everyday setting negatively affects willingness to publicly support a redistributive economic policy" by Melissa Sands
   
   
2. Peak Artificial Intelligence
   
   
3. Interactive historic map of every UK railway line that's ever existed, via Tim Dunn
   
   
4. Oxford DEMIG databases with unprecedented coverage of migration & migration policy data now available for free download, via Hein de Haas
   
   
5. Using machine learning in R to predict Automobile Collision and Bike Share Demand and subsidy via Ken Steif
   
   
6. Awful AI: a curated list to track current scary usages of AI - hoping to raise awareness, HT Will Geary
   
   
7. The software used to create the black hole in the movie 'Interstellar' is a full implementation of Einstein's equations in 40,000 lines of C++, and rendered thousands of 23-megapixel IMAX frames on a 32,000-core render farm at about 20 core-hours per frame via John Arundel
   
   
8. What to consider when choosing colors for data visualization, by Lisa Rost

credit: Lisa Rost

Assorted links

1. Automation and AI will increase spatial/regional inequalities. Paper by Morgan Frank and colleagues
   
   
2. Explore, select and download data of the global population projections by age, sex and education, a great collaborative work of researchers from the Wittgenstein Centre for Demography and Global Human Capital
   
   
3. The team led by Max Roser at Our World in Data is hiring a postdoctoral researcher in the area of global health and population sciences
   
   
4. Greyhound: a powerful platform to interactively explore and analyze massive point clouds with Trillions of points in the browser, via Howard Butler
   
   
   
   
5. The dynamical structure of political corruption networks. A paper by Haroldo Ribeiro, Luis Alves et al., analyzing 27 years of political corruption scandals in Brazil
   
   
6. Citywide effects of high-occupancy vehicle restrictions: Evidence from “three-in-one” in Jakarta, by Rema Hanna et al.
   
   
7. Want to find statistically significant hierarchical modules in weighted networks? paper here and code here, by Tiago Peixoto, who is the author of the graph-tool Python library