(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) The spread of Coronavirus is reminding us of just how often people travel around, especially as various locations become quarantined and international travel corridors get shut down. So let’s take a look at some basic data on travel patterns here of relevance to us here in Vancouver. Then we’ll put them back in the context of Coronavirus. TLDR: travel data is really interesting, don’t be frightened of travellers, and there’s still a lot we don’t know about coronavirus
(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) Wealth and income are different things. Wealth is measured in terms of assets minus debts at any given point in time. It can accumulate or deplete over a lifetime and across generations. By contrast, income represents some variation of how much money one makes over a given time period (usually a year). Most people get this on some level. But since both income and wealth deal with people and their money, the terms are also often used interchangeably.
(Joint with Nathan Lauster and cross-posted at HomeFreeSociology) Property Tax Snacks Residential Property Taxes have been rising in Vancouver. As always, we’re seeing a lot of sturm and drang about the rise. But we think it’s ultimately a good thing. Why? Here’s three perspectives. From a fiscal perspective, property taxes pool our resources to enable our government to pursue projects and provide for the common good. They’re a big component of how we take care of each other and set priorities.
Canada is a large country, with some reasonably densely populated regions, and large areas that are sparsely populated. That makes it hard to map things. CensusMapper, our project to flexibly map Canadian census data, struggles with that. The choropleth maps can be quite misleading. The same problem comes up when mapping Canadian election data. This map makes it virtually impossible to get a good reading of the distribution of votes. There are a couple of ways around this.
These days I run a fair bit of spatial analysis. And there are three problems that regularly come up: Getting data on compatible geographies Ecological fallacy Spatial autocorrelation None of these problems is insurmountable, but they are all annoying to various degrees. Often I might ignore them on my first analysis run, but these problems need to be dealt with sooner or later. Which can eat up significant amounts of time.