Étiquette : mapping (Page 1 of 3)

“Last week Buzzfeed News and the Los Angeles Times featured street-level visualizations of how COVID-19 is affecting traffic patterns in major cities around the world. The visualizations were generated from Mapbox Traffic data. For this post, we dug further into our telemetry data to show how much and where movement and local travel patterns have changed around the globe during the COVID-19 pandemic.”

Source : Where and when local travel decreased from COVID-19 around the world

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“We use telemetry from all Mapbox SDKs to improve our map, directions, travel times, and search. We collect anonymous data about how users interact with the map to help developers build better location based applications. Location telemetry is critical to improving the map. We use the data to discover missing roads, determine turn restrictions, build speed profiles, and improve OpenStreetMap.”

Source : Telemetry | Mapbox

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

“Social Mapper is a Open Source Intelligence Tool that uses facial recognition to correlate social media profiles across different sites on a large scale. It takes an automated approach to searching popular social media sites for targets names and pictures to accurately detect and group a person’s presence, outputting the results into report that a human operator can quickly review”.

Source : GitHub – SpiderLabs/social_mapper: A Social Media Enumeration & Correlation Tool by Jacob Wilkin(Greenwolf)

«L’analyse des attributs géographiques des cellules a permis de pointer 21 facteurs de risque, allant de la présence d’arrêts de bus, de magasins de restauration rapide, de café et de bar, de pharmacie, de guichet de banque, de magasin d’alimentation… mais aussi bien sûr des lieux où sévissent trafic de drogue et prostitution. Et ces différents facteurs s’agencent dans un autre ordre de jour et de nuit. Peut-on pour autant prédire ou prévoir ?»

Source : Police prédictive (1/2) : dépasser la prédiction des banalités ? | InternetActu

« Here, we are interested in the 2006-2015 period, ten years during which 25.000 projects involving 45.000 people produce a 2-mode graph of more than 63.000 edges. To focus on projects and disciplines, the network is projected into a 1-mode graph of projects only. Thus, the graph displayed below contains over 15.000 projects that were funded between 2006 and 2015 » – Martin Grandjean.

Source : Martin Grandjean » Digital humanities, Data visualization, Network analysis » Complex network visualisation for the history of interdisciplinarity: Mapping research funding in Switzerland

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