Dear Chart of the Day Fan:
Here's my chart for today. I'll talk about it shortly after 3:30 p.m. Eastern (12:30 p.m. Pacific) on the Bloomberg Businessweek r
Smart Growth C est Bon!
Compact infill development can create affordable, inclusive and attractive cities, like Montreal, plus belle ville au monde.
Todd Litman | December 31, 2020, 6am PST Share
Planning is challenging but rewarding because it often involves new issues and perspectives. For example, when analyzing urban unaffordability and inequity problems, some of my friends like to apply Marxist analysis: they want to blame rich foreigners, Big Finance, and global corporations for driving up housing prices and squeezing lower-income families out of attractive and economically successful neighborhoods. This video, titled
Push, is an example. It argues that housing problems are caused by fiscalization, and should be solved by declaring housing a human right.
An intuitive visual guide to a powerful ML algorithm applied to predict voting patterns among the US States.
Have you ever wondered why certain states tend to vote Republican while others vote Democrat? Is there a way to predict which way a state will vote in any given year? Letâs say we want to predict whether a state is
âredâ or
âblueâ based on a few variables that we observe about it. To do this, weâll use Decision Trees.
Red and Blue Map, 2016; Image from Wikipedia
Decision trees are both a classification and regression supervised learning model used to predict the value of a target variable (Y) using information from several input variables (Xs).