Étiquette : visualisation (page 1 of 8)

How the Virus Got Out

How the Virus Got Out

“The most extensive travel restrictions to stop an outbreak in human history haven’t been enough. We analyzed the movements of hundreds of millions of people to show why.”

Source : How the Virus Got Out – The New York Times

A graphic with no description

“A group of former IMF chief economists warned last weekend that a global recession had already begun, but although economic activity is slowing sharply, much official data are out of date before they are even published, given the time they take to collate.  To make up for the lack of official information, the FT has compiled a set of alternative, high-frequency measures of economic activity for different sectors which give an early indication of what to expect when official data start to become available in the coming weeks. ”

Source : Real-time data show virus’s hit to global economic activity | Financial Times

Epidemic Calculator

“At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging.
Gabriel Goh created this calculator as an attempt to address this gap in understanding. This calculator implements a classical infectious disease model — SEIR (Susceptible → Exposed → Infected → Removed), an idealized model of spread still used in frontlines of research e.g. [Wu, et. al, Kucharski et. al].”

Source : Epidemic Calculator

Infodemics

“About 100M public messages have been collected and analyzed to understand the digital response in online social media to COVID-19 outbreak. Specifically, we used machine learning techniques to quantify: collective sentiment & psychology: lexicon-based and rule-based emotional and psychological state social bot pollution: The fraction of activities due to social bots and the exposure of the Twitterverse to unreliable news News reliability: the fraction of URLs pointing to reliable news and scientific sources”

Source : COVID19 Infodemics Observatory

Genomic epidemiology of novel coronavirus

“This phylogeny shows evolutionary relationships of hCoV-19 (or SARS-CoV-2) viruses from the ongoing novel coronavirus COVID-19 pandemic. This phylogeny shows an initial emergence in Wuhan, China, in Nov-Dec 2019 followed by sustained human-to-human transmission leading to sampled infections. Although the genetic relationships among sampled viruses are quite clear, there is considerable uncertainty surrounding estimates of transmission dates and in reconstruction of geographic spread. Please be aware that specific inferred transmission patterns are only a hypothesis.”

Source : Nextstrain / ncov

“We are not epidemiologists—we are the design, engineering, and support teams at a mapping company. Just as we don’t spend every moment thinking about how to track and slow the spread of infectious disease, most public health teams we work with are not experts in spatial data visualization. In the environment of a growing epidemic, maps have a way of spreading fast, too — making it imperative that they are accurate, informative, and thoughtfully designed. To answer common questions and help our partners make thoughtful design decisions while mapping this and other health crises, we have put together a number of best practices and common pitfalls to avoid.”

Source : 7 best practices for mapping a pandemic – Points of interest

“Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection.”

Source : Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cartographie numérique: Visualiser les densités de population en 3D et à l’échelle mondiale

Matthew Daniels, qui a élaboré cette application, a représenté l’ensemble de la population mondiale sous forme de blocs 3D avec un dégradé de couleurs du vert clair au bleu foncé. Ce type de cartographie est surtout efficace pour représenter les « pics » de population dans les zones urbanisées.

Source : Cartographie numérique: Visualiser les densités de population en 3D et à l’échelle mondiale

GELT 2015-2018

“The map  takes the more than 6.6 billion location mentions across the 850 million worldwide news articles monitored by GDELT 2015-2018, snaps them to a 0.001 degree grid and then visualizes the final dataset. Locations are not sized by the number of mentions they receive, meaning a major metropolis mentioned tens of millions of times will still only appear as a single small dot in the image below. Due to artifacts of the rasterization pipeline you will see a few areas of the map below with rectangular artifacting – those are technical issues, rather than meaningful geographic patterns”

Source : Mapping The Geography Of GDELT: 2015-2018 – The GDELT Project

Barabazi Art Critics
— Thomas Griessen (@ThomasGriessen) 12 novembre 2018

« Older posts

© 2020 no-Flux

Theme by Anders NorenUp ↑