I am now separating my public scholarship to keep this blog focused on GIS-related topics. I continue blogging critically about the COVID pandemic response at https://clausr2020.substack.com/. My other web and social media sites can be found via https://linktr.ee/clausr.
The preparations for, and start of, the Fall 2021 has brought the corona crisis to a new level of intensity. I have not had time to write any of the many blog posts I have in mind or already drafted. Instead, I want to provide a quick summary and update of recent work.
A number of faculty from across Canada and various disciplines spanning the natural and social sciences and humanities have formed Canadian Academics for Covid Ethics. The group had already published several pertinent letters and op-eds that you can find on the web site.
In addition, I interviewed with Argentinian journalist Agustina Sucri for an extensive article titled “Carta de académicos a los no vacunados“, appeared with Dr. Angela Durante on the Richard Syrett Show – News Talk Sauga 960 AM (September 2, 2021, recording from 1:02), and was profiled by Richard G in Fearless Canada’s Covid Stories and Testimonials.
Last but not least, I joined the Canadian Covid Care Alliance and co-authored a letter-to-the-editor of the Toronto Star with Drs. Steven Pelech and Julie Ponesse, in response to the Star’s disturbing August 26 front page hate messages.
More work is in progress.
Yet Another Review of the Terminology Used to Describe Techniques for Making Multiple Variables Comparable
Ok, here we go again. I wrote in this blog on 30 November 2013 about “Normalization vs. Standardization – Clarification (?) of Key Geospatial Data Processing Terminology using the Example of Toronto Neighbourhood Wellbeing Indicators“. Note the question mark in that title? Its length and that of my title and subtitle today, and the choice of words used in them, will tell you a lot about the challenge at hand: clarifying, reviewing, and settling – once and for all! – the meaning of terms like “normalization”, “standardization”, and “rescaling”. The challenge is related to the processing and combination of multiple variables in GIS-based multi-criteria decision analysis, for example in my ongoing professional elective GEO641 GIS and Decision Support, and extends to many situations in which we utilize multi-variate statistical or analytical tools for geographic inquiry.
In two other blog posts, I discussed the need to normalize raw-count variables for choropleth mapping. On 26 March 2020, I wrote about “The Graduated Colour Map: A Minefield for Armchair Cartographers“. The armchair cartographer’s greatest gaffe: mapping raw-count variables as choropleth or graduated-colour maps. In a post dated 3 November 2020 on “How to Lie with COVID-19 Maps … or tell some truths through refined cartography“, I go into more detail about why to use “relative metrics” on choropleth maps. These metrics can take the form of a percentage, proportion, ratio, rate, or density. They are obtained by dividing a raw-count variable by a suitable reference variable. In class, I used the example of unemployment, where the City of Toronto provides the number of unemployed people in each its 140 neighbourhoods.Continue reading “Normalization and Rescaling as Horizontal and Vertical Operations in Your Attribute Data Table or Spreadsheet”
A few years ago, some American and international cartography and GIS experts banded together to hold low-key community mapping events under the Maptime label. The international site and the MaptimeTO Twitter account of the Toronto group are dormant, but the idea is alive and well – let’s start a Ryerson University map club under the MaptimeRU banner!
In class the other day, we had a look at “The True Size Of…” web app, which illustrates the size distortion of countries under the Web Mercator projection. Some students already knew the example of Greenland. In most online maps, Greenland looks about as big as the continent of Africa, but its size is greatly inflated under the Mercator projection due to its far-northern latitude. When you pull it towards the equator for size comparison, it shrinks to as little as 7% of Africa, and that is the actual ratio of their land surfaces.
Size comparison maps are popular talking points but they are surprisingly tricky to make in geographic information systems (GIS). After all, we usually aim to map things at their actual location on planet earth’s surface. John Nelson, cartography and user experience specialist at world-leading GIS company Esri, recently posted a blog and video tutorial on “How to make one of those size comparison maps” in ArcGIS Pro. As possible kickoff for a recurring MaptimeRU meetup, I will sit down with interested Geographic Analysis students during study week and replicate John’s instructions as well as try the same in the free and open-source QGIS package.Continue reading “MaptimeRU Kickoff – Web Mercator and Size Comparison Maps with ArcGIS Pro, ArcMap, and QGIS”
For coronaphobics and lockdown believers, the United States serve as the poster child for how not to handle the pandemic. The Johns Hopkins University COVID-19 dashboard (Fig. 1) shows cumulative “case” counts by US counties using proportional circles – a suitable cartographic choice, although the bright red colour on dark background is questionable, as discussed elsewhere. The ten-and-a-half million cumulative cases and nearly a quarter-million deaths as of November 10th, place the US at the top of the COVID-19 world rankings. But are these numbers actually big? And what can we gather from the spatial pattern of cases?
… or tell some truths through refined cartography
In his seminal book “How to Lie with Maps”, Professor Mark Monmonier illustrates how map makers can intentionally or inadvertently convey falsehoods using misguided data selection and cartographic design options. In an era of widely accessible, easy-to-use online mapping tools, misleading maps are becoming ubiquitous. Maps of COVID-19 statistics, along with associated graphs and data tables, which have become a focus of public attention this year, are no exception. Therefore, I want to take another look at the pitfalls of the popular choropleth map.
The granularity at which you look at COVID-19 may determine your attitude towards Sars-CoV-2
Scale is one of the most fundamental concepts in Geography. My PhD student just completed her dissertation on “The Consequence of Scale: Process and Policy Implications of Composite Index Modelling Using the Conceptual Framework of GIS-MCDA”, in which she compares biodiversity indices computed at different scales within a city, for example smaller census tracts vs larger social planning neighbourhoods. In Geographic Information Systems (GIS), we usually work with aggregated data, and the scale of aggregation can range from census blocks through postcode areas and neighbourhoods/wards to cities, counties, provinces, and countries. Results of data analytics are known to depend on several aspects of scale, including the observation/measurement scale, at which data are collected; modelling scale, at which data are analyzed; and operational/policy scale(s), at which decisions are made and implemented.Continue reading “Issues of Scale in the Corona Crisis”
The COVID-19 lockdown has brought with it an abundance of online professional development opportunities – a welcome escape from the terrors caused by the novel coronavirus (or by the house arrest and social distancing regime itself, if you concur with my view ;). On April 29, cartographer Daniel P. Huffman of Madison, Wisconsin, organized “How to do Map Stuff: A Live Community Sharing Event” with virtual workshops offered by volunteers from around the world, see https://somethingaboutmaps.wordpress.com/2020/03/19/how-to-do-map-stuff/.
Along with several interesting presentations, I listened in to Minnesota-based cartographer Ross Thorn, who went through the process of “Creating an Interactive Fantasy Map” using QGIS and MapBox. The recording is now posted on Youtube at https://www.youtube.com/watch?v=2nmLibB3lGs (starts around minute 9:30). Rather than create a set of islands from scratch, Ross “floods” a digital elevation model (DEM) so that mountains or hills turn into islands while lower elevations are transformed into the open seas… The remainder of that tutorial focused on vectorizing the island boundaries and adding land-use polygons as well as settlement locations with attached information.Continue reading “The Great Escape – 3D Fantasy Map Tutorial”
It is heartening to hear Ontario’s Premier Doug Ford explain that “we must listen to what the data tells us” about the threat of the novel coronavirus. Commitments from politicians to evidence-based decision-making are refreshing, even though it is well understood that the data (a plural word) do not actually speak to us, unless we ask the right questions of them. In the case of COVID-19, numerous analysts – myself included – have been playing with ways to visualize, interpret, and even predict the curves of confirmed infections, tests conducted, deaths, and cases resolved. Unfortunately, it is becoming increasingly clear that the underlying data are fundamentally flawed and should not be used for public information nor for executive decisions that drastically interfere with our freedoms to live a healthy life, move around, assemble, or conduct business.
number of fatalities
case-fatality rate = ———————————
number of cases
Do not use choropleths for your COVID-19 counts, ever!
In a hilarious contribution to Medium, Dr. Noah Haber et al. issued a call to “Flatten the Curve of Armchair Epidemiology“. They analyze the transmission of “well-intended partial truths” about COVID-19 and caution of hidden “viral reservoirs throughout the internet”. To flatten this curve, they recommend fact-checking before posting and go as far as endorsing social-media distancing measures. As with general COVID-19 tips based on armchair epidemiology, misinformation can also be spread through the numerous COVID-19 maps that are widely circulating through the Web. In this article I want to focus on one particular instance of armchair cartography: wrongly mapping COVID-19 count data using choropleth symbology.