If you make charts on the internet, angry email about those charts is inevitable. Especially if your charts sometimes use a y-axis that starts at a number other than zero. You see, an old book called How to Lie With Statistics has convinced people that truncated axes are a devilish tool of deception.
Big Data is what people talk about these days but what about “small data”? Clark Stevens looks at challenges of working with small data sets specifically with Benchmarking. Do you know the minimum population size you need in order to give a level of accuracy you are comfortable with and is statistically significant?
What time of day people tweet ‘brunch’, ‘breakfast’, or ‘lunch’? Ben Jacobson looks at twitter data to find the time range. To find the range, he first created spline interpolation on the histogram of tweet counts. Then, using the derivative on spline, points with maximum and minimum slope of the tangent are determined.
1880 US Census took 8 years to complete and without the invention of the Hollerith tabulating machine (punch cards) 1890 census would have taken 10 years. Hollerith’s company eventually became part of what we know as IBM. In this visualization, you can look at the evolution of tools and technologies which have now evolved into what we now know as “big data”.
Are the brexit voting results better represented using a cartogram or choropleth map? In cartogram the topology, i.e. the relationship between object, is retained while the geometry, i.e. the shape of objects, is distorted. In this post Gregor Aisch from vis4.net describes some drawbacks of cartogram and explains why a choropleth might be a better option.
Data storytelling can be designed by using a variety of techniques: scrolling, steppers, animations, etc. Like everything else in data viz, it’s not the one tool is better/worse. Rather it depends on finding the best technique for the situation and being cognizant of potential strengths/weaknesses. In this medium post, there are some guidelines and resources to make this selection with examples.
Can Data Science help predict the next mass shootings? In the wake of mass shootings, many people wonder how they could have been prevented. Were there warning signs that should have been heeded? Was the person mentally ill? Did he or she hold extremist views? The sad truth is that the only personal factors that reliably correlate with mass shooters are being young and male.