Nathan Yau – the guy behind the wonderful visualizations of FlowingData.com – has been looking into job market data more and more lately. For his latest project, he took data of the Current Population Survey (2011-2016) a survey run by the US Census Bureau and Bureau of Labor Statistics. This survey covers many topics, but Nathan specifically looked into people’s current occupation and what they were doing the year before.
For his first visualization, Nathan examined the percentage of people switching jobs (a statistic he dubs the switching rate). Only occupations with over 100 survey responses are shown:
Next Nathan looked into job moves within job categories, as he hypothesizes that people who decide to switch jobs look for something similar.
The above results in the main question of the blog: Given you have a certain job, what are the possible jobs to switch to? The following interactive bar charts gives the top 20 jobs people with a specific job switched to. In the original blog you can specify a job to examine or ask for a random suggestion. I searched for “analyst” in the picture below, and apparently HR professional would be a good next challenge.
Nathan got the data here, prepared it in R, and used d3.js for the visualizations. I’d have loved to see this data in a network-kind of flowchart or a Markov-chain. For more of Nathan’s work, please visit his FlowingData website.
Jack Zhao from Small Multiples – a multidisciplinary team of data specialists, designers and developers – retrieved the Language Spoken at Home (LANP) data from the 2016 Census and turned it into a dot density map that vividly shows how people from different cultures coexist (or not) in ultra high resolution (using Python, englewood library, QGIS, Carto). Each colored dot in the visualizations below represents five people from the same language group in the area. Highly populated areas have a higher density of dots; while language diversity is shown through the number of different colors in the given area.
Good news: the maps are interactive! Here’s Sydney:
Eastern Asian: Chinese, Japanese, Korean, Other Eastern Asian Languages
Southeast Asian: Burmese and Related Languages, Hmong-Mien, Mon-Khmer, Tai, Southeast Asian Austronesian Languages, Other Southeast Asian Languages
Southern Asian: Dravidian, Indo-Aryan, Other Southern Asian Languages
Southwest And Central Asian: Iranic, Middle Eastern Semitic Languages, Turkic, Other Southwest and Central Asian Languages
Northern European: Celtic, English, German and Related Languages, Dutch and Related Languages, Scandinavian, Finnish and Related Languages
Southern European: French, Greek, Iberian Romance, Italian, Maltese, Other Southern European Languages
Eastern European: Baltic, Hungarian, East Slavic, South Slavic, West Slavic, Other Eastern European Languages
Australian Indigenous: Arnhem Land and Daly River Region Languages, Yolngu Matha, Cape York Peninsula Languages, Torres Strait Island Languages, Northern Desert Fringe Area Languages, Arandic, Western Desert Languages, Kimberley Area Languages, Other Australian Indigenous Languages
The US Census Download Center contains rich information on its countries demographic data. Here you can find a piece of R code that uses the highcharter package in R to create an interactive map showing the median household per country.