derPlau

Explore occupations with related skills in an intuitive path-like representation of multi-dimensional "Skillspace."

Start by entering an occupation name, or just explore the data.

See which occupations are growing, which pay the most, and which require a given degree of training or education. Explore the relationships among these factors by setting bubble size to reflect one variable and color another.

Selecting an occupation will highlight its ten nearest occupations, based on 218 skill, knowledge, and work activity measurements from the O*Net database. Click on one of these "neighbors" to bring up its neighbors in turn.

A "breadcrumb trail" shows your past selections; clicking on a breadcrumb will take you back to that occupation.

Seasonal patterns make up the "heartbeat" in this animation of unemployment rates in Wisconsin counties from 2000 through 2013; times of recession are also thrown into sharp relief as the state gets darker.

Mousing over the graphic pauses the animation; mousing over a county gives detail of the county for that point in time. Sliders allow direct selection of time points and speed changes.

An animated exploration of "Skillspace." 218 "skills" measures were collected from the O*Net Occupational Database and mapped onto nearly 800 occupations tracked by the Bureau of Labor Statistics and the Wisconsin Department of Workforce Development. The 218 skills were then compiled into summary dimensions, the most important five of which are shown here in rotating order.

Another port of a graphic to d3.js. Summarizing the ranges of clusters of occupations in multi-dimensional skillspace with "gauges" on five summary (Principal Components) axes. Each axis has two opposing directions; for example, an occupation might involve work that is more physical or more intellectual in nature (Principal Component 1, the top gauge in each block of gauges). Positions of clusters on these axes are highlighted by the background gradient, which clearly delineates between the left (e.g., physical) and right (e.g., intellectual) ends of the dimension. Of course, some clusters may span across the center of one or more axes, implying that the cluster is not strongly defined by those axes.

My first foray into d3.js. This variant of a bubble-chart adapts the bubble concept to a tabular format. In this case, (simulated) Wisconsin employment in major industry groups and skills-based occupational clusters is shown depicted to order-of-magnitude approximation. A glance at a row gives an idea of the overall importance of an industry to employment (so extraction industries like mining have relatively low employment; Health & Social Service industries have relatively high employment); a glance at a column gives a similar picture of employment by occupation. A deeper look reveals detailed patterns of employment: the manufacturing industry employs primarily Equipment Operations & General Physical Labor occupations; Clerical occupations are employed at a modest level in most industries, but disproportionately large levels in Finance and Health/Social Services.