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This tutorial is meant for D3 v5-v7.

This tutorial is a quick intro to D3.js, a Javascript library for creating interactive data visualizations in the browser. D3 is built on top of common web standards like HTML, CSS, and SVG.

This is not designed to be a deep dive — this tutorial will teach you how to learn D3 and give you a high-level understanding of this powerful tool.

Broadly speaking there are 4 steps to setting up a force simulation:

  • create an array of objects
  • call forceSimulation, passing in the array of objects
  • add one or more force functions (e.g. forceManyBody, forceCenter, forceCollide) to the system
  • set up a callback function to update the element positions after each tick

cola.js (A.K.A. «WebCoLa») is an open-source JavaScript library for arranging your HTML5 documents and diagrams using constraint-based optimization techniques.

It works well with libraries like D3.js, svg.js, and Cytoscape.js. The core layout is based on a complete rewrite in Javascript of the C++ libcola library.

It also has an adaptor for d3.js that allows you to use cola as a drop-in replacement for the D3 force layout. The layout converges to a local optimum unlike the D3 force layout, which forces convergence through a simple annealing strategy. Thus, compared to D3 force layout:

Poundbury Walkability index

The Walkability Index is a tool that allows existing places to be benchmarked and new proposals to be objectively tested in terms of whether they deliver car-dependence, with its associated problems – or walkability, with the social, economic and environmental benefits found in walkable places.

The location of everyday land uses – shops, offices, schools and healthcare facilities – has important effects on our movement choices: whether we reach them by walking or cycling, catching a bus or going by private car.

Sometimes there is no choice: low density, monofunctional housing estates create car dependence. This is not only harmful for the environment but damaging to our mental and physical health. Car-dependency influences obesity and loneliness. In contrast, walkable places are healthy and sociable places.

La pandemia de la COVID-19 ha puesto de manifiesto lo importantes que son los datos en general, y sus representaciones gráficas en particular, para la toma de decisiones, de lo personal a lo político, de lo económico a lo social. Hablaremos de las experiencias de visualizar datos del COVID-19 y de la relavancia de los datos en nuestras vidas.

Alberto Cairo escribe y diseña visualizaciones de datos. En esta entrevista nos cuenta sobre sus libros y sus descubrimientos. El nos dice que “Una visualización es un argumento visual. Necesita tiempo y atención para poder leerlo con cuidado.” Y que no tenemos que usar los gráficos para confirmar lo que creemos. También nos cuenta como son las clases para infografistas y como reflexionan a través de los gráficos. Hablamos de un futuro con más alfabetización visual, donde la gente está más entrenada a leer gráficos y las interpretaciones que proponen. El sostiene que para enseñar infografía es importante enseñar a interpretar los números y que la visualización de datos nos permite entender los números de una manera más crítica.

Captura de pantalla de onesharedhouse2030.com

welcome to the year 2030.
there are 1.2 billion more people on the planet.
70% of us are living in cities now.
in order to house 1.2 billion more people,
all of us are sharing more household goods and services than ever before.
we refer to this sharing as co-living.
and many more of us are living this way now.

but it’s not a new thing.
communal living has always been a solution to common problems.
like rapid urbanisation, loneliness, and high living costs.
but what does co-living look like in the year 2030?
who is it for?
how has it changed our society?
what are we sharing?

with many more of us now co-living,
there is no one configuration.
discover what type of co-living would be uniquely suited for you.
reserve your spot for ONE SHARED HOUSE 2030.

Portada 24 de mayo 2020 del New York Times

The New York Times prepared a powerful front page for its May 24 print edition, marking the somber milestone of 100,000 coronavirus deaths in the United States.

The newspaper listed the names of 1,000 people who died of COVID-19 — just 1% of the total death toll.

The newspaper staff combed through obituaries and death notices for people whose cause of death was listed as COVID-19, and listed people’s names, ages, and facts about their lives.

Mortalitat de la COVID-19 per país

Lo primero que me chocó es la gama de colores del mapa. Lo normal es que se use un color y se aplique una tonalidad que se va oscureciendo conforme los valores son más altos. En este caso el valor más alto es de un color diferente de la gama, El negro no es marrón.

Además, la diferencia de color es muy grande respecto a Italia con una variación del 5%. No sé si esto es intencionado o tan solo un error de diseño. Este mapa va a la galería de ejemplos de malas visualizaciones para mis alumnos. Para colmo, los datos no eran correctos.

Como ya se dieron cuenta @resd9 y @A_Sorines el dato de Bélgica era erróneo en vez de 15,70% era 65,67%. El mapa lo publicó la Vanguardia y ha rectificado el dato aunque mantiene el color negro.

Teams are making ready-to-use COVID-19 datasets easily accessible for the wider data visualization and analysis community. Johns Hopkins posts frequently updated data on their github page, and Tableau has created a COVID-19 Resource Hub with the same data reshaped for use in Tableau.

These public assets are immensely helpful for public health professionals and authorities responding to the epidemic. They make data from multiple sources easy to use, which can enable quick development of visualizations of local case numbers and impact.

At the same time, the stakes are high around how we communicate about this epidemic to the wider public. Visualizations are powerful for communicating information, but can also mislead, misinform, and — in the worst cases — incite panic. We are in the middle of complete information overload, with hourly case updates and endless streams of information.

Captura de pantalla de la web From Data to Viz

From Data to Viz leads you to the most appropriate graph for your data. It links to the code to build it and lists common caveats you should avoid.

What kind of data do you have? Pick the main type using the buttons below. Then let the decision tree guide you toward your graphic possibilities.

Data visualizations are a powerful way to display and communicate data that otherwise would be impossible to transmit in effective and concise ways. The spread of broadband Internet, the easier access to reusable datasets, the rise in read/write digital media literacies, and the lower barrier to generate data visualizations are making mass media to intensively use of infographics. Newspaper and online news sites are taking advantage of new, affordable and easy to access data visualization tools to broadcast their messages. How can these new tools and opportunities be used effectively? What are good practices regarding data visualization for a general audience?

After an introduction to a series of key concepts about visualizing data the lecture will follow with an analysis of a series of significant data visualizations (tables, pie and bar charts, maps and other systems) from TV, daily newspapers and news websites to detect good and bad practices when visualizing statistical information. The lecturer will then analyze recent literature of visualization studies regarding persuasion, memorability and comprehension. What are more effective embellished or minimal data visualizations? Does graphical presentation of data make a message more persuasive?

Basically, women were slightly more liberal with the modifiers, but otherwise they generally agreed (and some of the differences may be sampling noise). The results were similar across the survey—men and women tended on average to call colors the same names.

Color Naming Perception Chart

Each user was shown a random color and asked to type in a name. We asked people provide the most generic name possible, dropping things like modifiers (blue instead of light blue). Instead of using the entire RGB spectrum (millions), we pruned the color set to 216 «web safe» colors. This helped ensure every color had many responses. We left it online for a week or so and collected 6,276 color responses.

Captura de pantalla de "¿De qué año es tu casa? El mapa que muestra cómo se ha construido España"

El año en el que más se construyó en España fue 1960. Por primera vez en varias décadas, el país experimentó una estabilidad económica que permitió un pequeño salto en la calidad de vida. Coincidió con el éxodo rural, para el que hicieron falta nuevas construcciones que acogiesen a los recién llegados del campo, y con el origen de un turismo que capitalizaría la economía española hasta nuestros días. La época conocida como el ‘desarrollismo’ propició dos programas de planificación económica y social en torno a zonas industrializadas y de costa que dibujaron el desarrollo urbano de los años posteriores.

Según el castastro, el 17% de construcciones de todas las capitales de provincia data de esa década. Luego llegaron los PAU, que alejaron la vida del casco histórico de las capitales, las grandes urbanizaciones, la burbuja y, también, los cadáveres inmobiliarios de la crisis.

…la fabricación en cadena del Seat 600 hizo que el automóvil pasara de ser un artículo de lujo a una utilidad de clase media, dando lugar a un nuevo enfoque urbanístico: ¿por qué vivir en el centro si ya es posible hacerlo en una casa más amplia sin tardar más de 20 minutos al trabajo?

Nació así el fenómeno del ‘urban sprawl’ o la dispersión urbana, que dio lugar a casi todos los barrios fuera de la M-30, como Aluche, Móstoles o Vallecas, donde se construyeron principalmente edificios de ladrillo; o Fuencarral y Pozuelo, lugares al norte en los que proliferaron las primeras urbanizaciones de chalés.

…este nuevo concepto de Madrid supuso la ruptura de una ciudad centrada en la proximidad, en una ciudad dispersa, basada en el coche.

This is part of a series of examples that describe the basic operation of the D3.js force layout.

…linkDistance tells the force layout the desired distance between connected nodes. It may seem strange that D3 doesn’t simply compel all links to be that distance. The force layout, however, takes other factors into account as well, which sometimes prevents it from achieving the exact link distance in all cases.

…charge, so named because it’s a property that acts like electrical charge on the nodes. With force-directed graphs in particular, charge causes nodes in the graph to repel each other. This behavior is generally desirable because it tends to prevent the nodes from overlapping each other in the visualization.