fabiochiu/medium-articles huggingface dataset:
[Event] Adding Assemble Protocol(ASM), MAP Protocol(MAP)
Tags: Event, Map, Bithumb, Adding, Asm

Fellow Bithumb Users,

This is No.1 Digital Asset Platform, Bithumb.

We are pleased to announce that Assemble Protocol(ASM), MAP Protocol(MAP) will be added on KRW market today.

The details about the event are as follows.

We greatly appreciate your support and look forward to your active participation.

1. Details of Adding Assemble Protocol(ASM), MAP Protocol(MAP) on KRW market

■ KRW market support timeline : 2021.04.26 (Mon) Within 30 minutes after the announcement

■ Available channel : PC Web, Mobile, API

2. Assemble Protocol(ASM) Event

2–1. 40 ASM Airdrop for a single transaction !

■ Event Period : 2021.04.26 (Mon) Adding on Market ~ Until the the exhaustion of the amount

■ Event details : 40 ASM will be distributed to the customers who made at least a single transaction during the event period.

** Customers can participate once a day on UID basis

** End point of this event will be noticed through additional announcement

** Assemble Protocol(ASM)’s trading on KRW market only.

2–2. Assemble Protocol(ASM)’s Airdrop Event for Trading

■ Event Period : 2021.04.26 (Mon) Adding on Market ~ 2021.04.28 (Wed) 24:00 (KST)

■ Event details : ASM will be distributed in accordance with each daily contribution rate of ASM during the event period. (for 3 days)

** Contribution Rate(%) = (Total ASM transaction amount of member A during the event period / Total ASM transaction amount on each round during the event period) %

** Assemble Protocol(ASM)’s trading on KRW market only.

3. Caution

■ This event will be limited to trading of Assemble Protocol(ASM) which have been newly listed and added on Bithumb

■ Airdrop will be made on May 11th, 2021 (Tue) and payment will be trimmed by 4 decimal places.

■ If the transaction is considered as illegal or abnormal, you will not be eligible for the event participation.

** Transactions occurring from Exchange or Foundation’s account will be considered as abnormal transactions, which will not be eligible for the event participation.

■ Prime members and customers who have participated in other events on Bithumb will be excluded from this event. ​

■ Bithumb may request an extra authentication process, conduct restriction on deposit/withdrawal and proceed account blocking for the users with unauthenticated accounts and the new users who have joined Bithumb within 7 days based on the announcement day.

Thank you.
Microsoft Bing COVID Tracker
By: Laurent Schoenaers
Tags: Co Vid 19, Microsoft, Bing, Maps, Coronavirus

Microsoft has published an interactive world map that shows in real time how many officially registered corona infections, cures and deaths are measured in the affected countries.

The Covid-19 Tracker uses Microsoft Bing Maps, the company’s own map technology. The world map highlights which areas are affected by means of red circles. The bigger the circle, the more affected the region is. When zooming in on the circle of a country, a box appears with the number of active infections, the number of deaths and the number of people declared cured.

In the column on the left, the Covid-19 Tracker ranks the countries based on the number of official infections. At the top of the list are China and Italy, followed by Iran, Spain and Germany.

When you click on a country in the list or on the map, the Covid-19 Tracker not only shows extra details, but also immediately searches the Bing-engine and shows related articles and videos.

Did you know that the John Hopkins University and Medicine also has a live tracker?

But most important of all, please follow your region’s guidelines and stay safe!

Source: http://laurentschoenaers.be/2020/03/18/microsoft-bing-covid-tracker/
States in India and Their Capital and Language in 2020 — TextBooq
Tags: Culture, Politics, Maps, India, General Knowledge

States in India and Their Capital and Language in 2020: There are total 28 states and 9 union territories in India. States in India with capital is one of the most common areas of General Knowledge. India states and their capital and language is an easier topic to memorize and also easier to score. Here we will provide a list of all states in India and their capital and their language.

Know Some Basics

Types of Union Territories

Union territory with legislature.

Union territory without legislature.

Delhi, Puducherry, and Jammu and Kashmir are the Union territories with legislature.

There are three types of capitals

Administrative: An administrative capital is the place where all executive offices of government are situated.

An administrative capital is the place where all executive offices of government are situated. Legislative: A legislative capital is the one where the state assembly convenes.

A legislative capital is the one where the state assembly convenes. Judicial: A judicial capital is the place that consists of territorial highcourts.

Article 1 of the Indian constitution defines India as a Union of States and not as a federation of states.

Indian States and Their Capitals and Languages

How Many Indian States Have More Than One Capital?

There are few states in India, where more than one capitals are administered due to various reasons.

Jammu and Kashmir: Srinagar (Summer) and Jammu (Winter)

Srinagar (Summer) and Jammu (Winter) Himachal Pradesh: Shimla (Summer) and Dharmasala (Winter)

Shimla (Summer) and Dharmasala (Winter) Maharashtra: Mumbai (Summer) and Nagpur (Winter)

Mumbai (Summer) and Nagpur (Winter) Uttar Pradesh: Lucknow (Administrative capital) and Prayagraj (Judicial capital)

Lucknow (Administrative capital) and Prayagraj (Judicial capital) Uttarakhand: Gairsain (Summer) and Dehradun (Winter)

Andhra Pradesh cabinet proposed to have three capitals for the state. Visakhapatnam, Amaravati, and Kurnool will be the executive, legislative, and judicial capitals respectively.

All the information on the States and capitals of India 2020 are updated as on the date of the publication of the article. You can download the Indian states and capitals and Language in 2020 pdf using the button at the bottom of the article.

The list of States in India and Their Capital and Language in 2020
MAP Protocol Weekly Report #39 (September 14th — September 20th)
By: Map Protocol
Tags: Mapprotocol, Blockchain, Weekly Report, Map

MAP Protocol Weekly Report #39 (September 14th — September 20th)

I. Team Update

We have entered September for almost three weeks. It might be a short period of time for a person’s lifetime but is relatively long for a startup. Since November 8th, 2019, MAP Protocol has entered the blockchain field for almost 10 months. It was a challenging journey, the COVID-19, the trade war, etc… In such conditions, the MAP Protocol has shown its patience and toughness, to solve each problem on by one. Check out the recap of MAP Protocol’s Third Quarter in 2020.

A Recap of MAP Protocol’s Third Quarter in 2020 https://medium.com/marcopolo-protocol/a-recap-of-map-protocols-third-quarter-in-2020-67b8178eb7f0

II. Community & Marketing (Sep 14th — Sep 20th)

Media Channels: 120,792

Twitter Followers: 53,262

Telegram Channel: 67,530

Communities: 79,245English Community: 69,506

Russian Community: 2,650

Indonesian Community: 1,843

Turkish Community: 1,379

Vietnamese Community: 791

Korean Community: 3,076

GitHub:

Commits: 551

Star: 11

Fork: 7

III. Technical Development
#Howto: change the value range ​​of a chart axis or map legend | EN/GER
Tags: Data Journalism, Ddj, How To, Dataviz, Maps

####Für Deutsche Version nach unten scrollen####

Visit us at: app.23degrees.io

When I create a graphic with 23°, I have 3 different options to display the “Value Range”, i.e. the displayed value range of the axes in charts or the legend on the map.

Click on the pen icon to open the editor. You will find the “Value Range” settings at the bottom of the “Visual” section.

There are three different options to display the “Value Range”

1) Complete data table

If you choose this setting the complete value range is shown on the axis of the chart or on the map legend. This could mean that those values which are not visible in the actual chart or map because they are hidden -for example through a filter- will still show up on the axis or the legend.

2) Currently visible values

The values that are actually active are taken into account here. For example, if you have created a graphic that is filtered by years, you will get a new value range with different values on the axis for each year. You can see in the GIF below how the values on the axis adjust according to the values of each selected year.

3) Custom selection

Here you can set custom values. Click on the “Value Range” tab and select “Custom selection”. Then enter the min. and max. values ​​in the field.

Write us: office@23degrees.io

twitter: @23degrees_io

fb: @23degrees.io

ig: @23degrees_io

############GERMAN VERSION###############

Wenn ich eine Grafik mit 23degrees erstelle, habe ich 3 verschiedene Möglichkeiten, die “Value Range”, also den angezeigten Wertebereich der Achsen in den Charts bzw. der Legende in der Karte anzeigen zu lassen.

Klicke im Editor, im Bereich “Visual” auf “Value Range” .

1) Complete data table

Hier wird der komplette Wertebereich in den Achsen oder der Legende dargestellt. Das heißt auch jene Werte, welche z.B. durch einen Filter in der Grafik gerade nicht sichtbar sind. Hast du beispielsweise ein Grafik mit einem Zeitreihen Filter bleibt der Min. und Max. Wert der Achsen gleich.

2) Currently visible values

Hier werden jene Werte berücksichtigt, die tatsächlich gerade aktiv sind. Hast du zum Beispiel eine Grafik erstellt, in der nach Jahren gefiltert wird, erhältst du für jedes Jahr, einen neuen Wertebereich mit anderen Werten. Siehe im GIF unten, wie sich die Werte in der Achse mit jedem ausgewählten Jahr neu anpassen.

3) Custom selection

Hier kannst du selbst einstellen, welche Werte angezeigt werden sollen. Klicke hierzu auf den Reiter beim Punkt “Value Range” und wähle “Custom selection”. Gib dann im Feld die Min. und Max. Werte ein.
Get Ready for Tableau Catalog
By: James Luby
Tags: Data Catalog, Tableau, Dataviz, Tableau Server, Metadata

photo by @erol from unsplash

If you’re a Tableau administrator or author, your life is going to get a lot easier. For Tableau Server deployments, particularly large ones, managing your data can be complicated. Under the hood, large Tableau deployments integrate data from many sources with varying attributes and quality. Have you ever wondered where your workbooks and worksheets are drawing data from? What is the quality of that data? Or what would happen if you needed to migrate data?

Version 2019.3 will include Tableau Catalog, a new capability of the Data Management Add-on to help you easily see the lineage of your data — which data sources, field attributes and much more. Data owners can automatically track information about the data (i.e., metadata), including data connections, tables and even table columns.

Tableau Catalog also helps build trust in the data across your organization. With data quality warnings, users can quickly see when there’s an issue with data being used in a dashboard — like a missing field or a maintenance interruption. Definitions and additional metadata can be added so users have a better understanding of the data itself. These data details are included alongside your dashboard, making it easy for everyone to feel confident about the data behind their analysis.

On the improved Tableau Server UI, users can see data issue related warnings directly in their dashboards and in the portal.

As part of the VizConnect video series, Tamas Foldi, Starschema CTO and Tableau Zen Master, explains the new features of Tableau Catalog and demonstrates data discovery, lineage and impact analysis, communication about data quality, and developer APIs. Even though this new capability is in beta version, Tamas provides several demos and walks you through these significant improvements to data management in Tableau. Check out the presentation video below.
C°F Experiments: Learning and Growing Through Experimentation
By: Gert Franke
Tags: Technology, Data, Dataviz, Design, Experiment

For a long time, we here at CLEVER°FRANKE have been working on a number of self-initiated projects. Eventually, we decided to collect them all on a single platform to give an overview of these experiments and to show you our explorations. In this article, I will share the value of conducting experiments for both our team and our clients, and hopefully trigger your curiosity for new topics, tools and methods.

There are often moments of downtime within project, while we wait on feedback from clients. To make good use of this time, we invest our own time in learning new tools and developments in the field of data, design and technology. This, of course, ignites questions in our minds. We start thinking about how to improve our current client projects and possibilities for starting new projects from scratch, where we can implement our learnings. More importantly, we form hypotheses that have high potential to enrich our skill set and portfolio. Once the questions in our minds turn into hypotheses, we start testing them. This is a great way to see how things actually work and you’ll be surprised by the results and how quickly you get them.

Starting the experiment

Naturally, it is not feasible to start testing every idea that comes to mind. We first organize our thoughts by coming up with concepts that we find interesting to explore. These concepts seem initially like clusters, but once we start delving into new areas, we often find that these clusters actually form a meaningful whole.

After deciding which concept to focus on, we start looking for different visualization techniques and tools that best suit turning this concept into a tangible story. Our Creative Coder Jonas gave his view on this process:
The 5 Most Important Principles of Data Visualization
By: Shachee Swadia
Tags: Data Visualization, Visual Design, Dataviz, Visualization, Data Science

Data visualization, in the simplest terms, is a graphical representation of data to understand patterns and communicate insights.

I reckon we all are now aware of the importance of dataviz in the current times. But, we still need to focus on the essential principles for creating effective and authentic visualizations. Misleading, confusing, and impractical visualizations are common and created by even the well-known dataviz designers and journalists among us.

“There are two goals when presenting data: convey your story and establish credibility.” — Edward Tufte

Follow these five principles to create compelling and competent visualizations:

Image by author

1. Tell the truth

I know this sounds pretty obvious, but unfortunately, it needs to be said. There are a plethora of graphs that misguide the reader by showcasing skewed data and projecting false narratives.

The job of the presenter is to inform the audience, not influence them.

Avoid these three misleading methods to ensure that your graphs are clear and honest.

A. Omitting the baseline

Generally, the baseline for a graph should start from zero unless specified otherwise. By starting the baseline from a different number, it can bias the perception of data. This technique is used to make the difference between data points seem to be greater than actual.

Images from Venngage and captions by the author.

B. Going against the conventions

There are certain conventions when plotting data. A larger bar indicates a greater amount, and a bigger area represents a higher number of values. By going against the convention, the viewer is unwittingly led to a wrong inference.

Images from Venngage and captions by the author.

C. Cherry-picking data

Cherry-picking is when only a few data points are plotted to show a misleading trend. This is one of the most common tactics used to mislead or deceive the audience.
Teaching Viz Digest No. 1: Exercise is Good for You
By: Jovan Lekovic
Tags: Teaching, Data Visualization, Dataviz, Colour, Design

It’s been just a little over one week since #teaching-vis has launched on the Data Visualisation Society (DVS) Slack. And it’s quite something.

We’re a mixed bunch that includes academics, consultants, researchers, designers, and dataviz enthusiasts.

We teach students, co-workers, organisations, scientists, journalists, designers, the general public and some of us even teach our friends.

And, following in the ethos of DVS, it’s inspiring to hear discussion among D3 wizards, Tableau Jedi, R gurus and pen & paper geniuses.

But more than all of that, it’s amazing to share ideas about how we teach data visualisation.

A big topic in this first week has been around exercises we use to teach our students different aspects of dataviz.

This began with a great question from Enrico Bertini on whether the group had ideas for exercises that teach students how to use colour in visualisations. This discussion quickly snowballed into a document where we’re adding some of our favourite exercises (and not just on colour).

So without further ado (and there’s been a lot ado, seeing it’s issue one of the digest), here are some the exercises that have been shared.

Cutting up the Visual Vocabulary

Having taught the Visual Vocabulary in the post-lunch graveyard slot by talking through the charts, I was delighted to see this exercise submitted by Henry Lau and originally developed by Martin Chorley.

Asking groups to re-create the FT’s brilliant Visual Vocabulary from cut-outs sounds like an ingenious way to get people to think out loud about choosing the right chart for their data.

Turning the vocabulary into a puzzle and making students piece it together sounds like a neat trick to help expand their use of common chart types.
In Focus: Digital Archives
Tags: Data Architecture, Data Visualization, Digital Archives, Dataviz, Database

The Information Operations Archive (IOA) is a database of social media posts with content like “This is big! Hillary Clinton covered up child trafficking investigation at the State Department” and “Watch: Barack Obama admits he was born in Kenya #birtherism.” Co–created by the Alliance for Securing Democracy, a bipartisan think tank initiative, and Graphika, a network analysis firm, IOA currently hosts 10 million Twitter and Reddit messages from Russian and Iranian–backed election interference missions.

Twitter’s prompt to download information contained in the Information Operation Archive.

While Twitter and Reddit “released” datasets for public use in 2018, they imposed significant hurdles to access them. Twitter asked users to enter an email address to download a 5GB file that was virtually impenetrable to anyone without a data science background. For our part, we helped elucidate the data sets by transferring them to a user–friendly environment where social posts—in addition to date range and associated hashtags—are visualized. The IOA fulfills a purpose in not only making the tweets accessible and searchable, but also in foregrounding how they can (and should) be used for critical analysis. And though frequently nauseating, the IOA exposes common, specious strategies that bad actors use to stoke division.

A search for “Kamala Harris,” for example, yields both expected results (“BUSTED! Kamala Harris Caught Using Taxpayer Dollars for Her Lavish Travels”) and ones that on a surface level seem more puzzling, such as a tweet wishing Harris a happy birthday. The message— sent by a fake account that espoused progressive views—features a photo of Harris as a child sporting an afro.

MEET Digital Culture Center
#Howto create a bar chart with 23° | EN/GER
Tags: Data Journalism, Data Visualization, Dataviz, Maps, Charts

So what’s the difference? The difference between wide and long format lays in the number of chart-types you can create once you have saved the dataset. Turning the data into long-format by using our “Autocreate Groups” function leaves you with more options because -as the name implies- it automatically creates groups, which can be helpful when applying filters or creating grouped, or stacked bar charts.

How do I create a grouped or a stacked bar chart then?

In contrast to a “regular” bar chart, a grouped or stacked bar chart represents at least 2 groups .

“simple bar chart”

Gender; Age

male; 20

Female; 30

“grouped bar chart”

Gender; City; Age

male; Vienna; 20

male; Berlin; 30

Female; Vienna; 30

Female; Berlin; 40

Let me give you an example:

For this chart we first used the wide format.

Since no groups are created this way, we can still only display a simple bar chart — or donut for that matter. You can only select one out of the two value fields (Age female or Age male).

We now use the same data set and turn it into the long format with the “Autocreate Groups” function.

The two columns “Age female” and “Age male” now become one “Group” column. Both cities Vienna & Berlin are now assigned a value for the male group and the female group.

#German#

Das Erstellen von Balken- und Säulendiagrammen mit 23 ° ist mit wenigen Klicks möglich.

Dazu musst du lediglich die Daten hochladen und deinen bevorzugten Diagrammtyp auswählen.

Wie muss die Datentabelle aussehen, die ich hochladen möchte?

1. Wide-Format

Wide-format data

2. Long-Format

Long-format data

Glücklicherweise spielt es keine Rolle, welches Format deine Daten haben.

Das liegt daran, dass wir alle Daten automatisch in das Long-Format konvertieren, um die Verarbeitung zu vereinfachen. (Außer du deaktivierst die Funktion “Autocreate Groups”)
Turn your data into beautiful dataliths
By: Luca Falasco
Tags: React, Data Visualization, Dataviz, Typescript, D3js

Let’s assume you’re a developer (or a designer) and you want to visualize some data using web technologies.

You basically have two options:

Using a library which provides high-level basic charts. Building the charts yourself (either using a low-level component library or not).

The solution you’re going to choose may depend on multiple factors, but most of the time it all comes down to one thing: time.

The first option usually works best if you have a short timeline and you need to put together something really quick. Of course this often means the end result is going to look really ordinary, and probably not so eye-catchy.

The second option is of course better if you want to build something custom/tailor-made and deadline is not an issue, and you have time to put into fine tuning etc..

Datalith tries to stand in the middle.

It is a collection of clean, lightweight and easily customizable React components for data visualization.

The purpose of the library is to provide an easy way to integrate custom data visualizations in any React project. The components were designed with simplicity in mind and a strong focus on the shapes and the visual patterns.
Always Check The Data Source
By: Nadezhda Kantur
Tags: Infodemia, Covid 19, Dataviz, Infodemic

Always Check The Data Source

I have recently read the article “18 Lessons of Quarantine Urbanism” by Benjamin H. Bratton, Professor of Visual Arts at University of California, San Diego, and Professor at European Graduate School and Visiting Professor at NYU Shanghai and the Southern California Institute of Architecture.

As long as the title of Bratton’s article is self-explanatory, I won’t go deep into the topic about the future of the post-pandemic world. Still, I’d like to share a short story about the words in this article which have particularly drawn my attention:

“Spanning the globe, the Kübler-Ross stages of grief are the new national horoscope: denial, anger, bargaining, depression, acceptance. To say that the USA is ten days behind Italy is not only an epidemiological analysis; it is a psychiatric diagnosis”.

To get an idea about the five stages of grief by Kübler-Ross you can watch this funny cartoon:

I was so amazed by an idea that the attitude of the nation to COVID-19 can be measured by the stages of grief that I immediately shared the link to this article to a WhatsApp chat with my family which is spread all around the globe.

My sister told me that the order of the stages of grief by Kübler-Ross had been messed up in this article. The sister’s guess was based on a data viz from the English version of Wikipedia:
What is Graphical Excellence and How to Create it
By: Khuyen Tran
Tags: Data Visualization, Graph, Dataviz, Visualization, Data Science

Time Series

The time-series plot is one of the most frequently used forms of data science. One dimension marches along to the rhythm of seconds, minutes, hours, days, weeks, months, years, centuries. This ordering of the time scale gives this graph the strength and efficiency of interpretation found in no other graphic arrangement.

When hearing about the time-series graph, we often think about this kind of graph:

Retrieved from Displayr

In fact, the graph above is the most commonly used kind of time-series graph. But do you know that time-series graph can also look like this:

Charles Joseph Minard, Tableaux Graphiques et Cartes Figuratives de M. Minard, 1845–1869

The explanatory power of time-series displays is enhanced when adding spatial dimensions to the design of the graphic. The graph above is the classic of Charles Joseph Minard, showing the terrible fate of Napoleon’s army in Russia. There are several things to notice in the above graph:

Beginning at the left, the thick tan flow-line shows the size of the Grand Army (422, 000) at the beginning of the invasion.

The width of this band indicates the size of the army at each place on the map. The size got smaller and smaller as the army reached Moscow.

The path of Napoleon’s retreat from Moscow is the darker, lower band, which is linked to the temperature scale and dates.

The combination of data maps and time-series efficiently portrays a sequence of devastating losses suffered in Napoleon’s Russian campaign. It was a bitterly cold winter and many froze on the march out of Russia. If we are told that the size of the army was initially 422, 000 and ended with 10, 000, we may not really perceive the huge number of the losses. But by looking at the graph, we could vividly visualize what the man came through and how severe the cold was.

So How Should we Graph?

This article does not exhaustively show every criterion of an excellent graph. But I hope this article gives you an important message: an excellent graph is not the graph that displays beautiful designs with advanced graphing technology. An excellent graph is one that consists of complex ideas communicated with clarity, precision, and efficiency. An excellent graph gives the viewers the greatest number of ideas in the shortest time in the smallest space. An excellent graph tells the truth about the data.

Thus, when graphing, first define the purpose and the ideas you want to convey through your graph, then find the way to present your ideas.

I like to write about basic data science concepts and play with different algorithms and data science tools. You could connect with me on LinkedIn and Twitter.

Star this repo if you want to check out the codes for all of the articles I have written. Follow me on Medium to stay informed with my latest data science articles like these:

Reference

Tufte, Edward. “The Visual Display of Quantitative Information.” Graphic Press LLC. August 2007
Outlier: Send Us Your Lightning Talks!
By: Mollie Pettit
Tags: Data Visualization, Outlierconference, Conference, Dataviz, Community

Submit a Lightning Talk!

In addition to the speakers and talks we’ve already curated, we will also be showcasing lightning talks. (Note: you need to be an attendee to give a lightning talk.)

We acknowledge that some folks who submitted to speak and were not selected may feel disappointed, and we totally understand that. If time and budget allowed, we would have loved to have selected many more. Lightning talks and unconference sessions(information on unconference sessions here) are an opportunity for more people to get involved and showcase their efforts, and we hope you consider being involved!

Lightning talks

You are invited to submit a lightning talk idea. The lightning talks will each run for five minutes and will be grouped into back-to-back sessions. This format provides a space for many to quickly share some of their most exciting insights and/or projects.

Submit your talk

Want to submit a lightning talk? Great! Fill out this survey to submit by the deadline — February 1st, 2021. (You need to be an attendee to submit.)

We ask that you follow a few guidelines when creating your lightning talk video:

You must be a ticket holder to submit a lightning talk.

The video must be five minutes or less in its entirety.

The talk should not contain any offensive materials and must abide by the Data Visualization Society Code of Conduct and Outlier Rules of Engagement.

Do your best to create a quality video; here are some hints and tips in how to best do this.

Lighting talks cannot be promotions or advertisements for any commercial purpose.

Note: Lightning talks can be submitted in any language.

The “selection”

We are planning to accept all submissions as long as they follow the above guidelines. We are considering highlighting some lightning talks (i.e., showing them without competing talks, along with the other curated talks), and including others during open sections of the schedule (which will consist of multiple concurrent unconference-style sessions).

How will this be determined? We’ll let you all (attendees) decide! If necessary, after February 1st, we will send out a survey with information on the submissions and request that attendees flag the talks they’re interested in seeing. We’ll use the survey results to help organize the schedule.

Join us!

You’re required to be an Outlier attendee to submit a lightning talk. If you don’t have your tickets yet, you can snag one here!
Project Showcase: Integrated Report for Museo Egizio
Tags: Data Visualization, Dataviz, Fundraising, Museums, Print

Project Showcase: Integrated Report for Museo Egizio

A world–famous museum overhauls its annual reporting and portrays the progress and setbacks of a historic year. Accurat Jul 29·3 min read

Normally, March and August are the busiest months of the year for Turin’s Egypt Museum. That wasn’t the case in 2020.

Like many organizations dependent on tourism, Museo Egizio shifted its focus to digital outreach and engagement during the pandemic, and carried on with day–to–day operations (including, impressively, the international transportation of several exhibitions) remotely.

The museum made measurable strides in 2020, and when the time came to compile facts and figures into annual reports for stakeholders, publishers were prepared; fortuitously, plans were already underway to reinvent two major publications—financial and visitation summaries—by consolidating them into one unified report that could holistically capture the “qualitative and quantitative” impact of museum activities. Organizers turned to Accurat for help in creating a new, “Integrated Report” that captured in look and feel the start of a fresh era.
Year Eight at Stamen
By: Alan Mcconchie
Tags: Cartography, Mapping, Stamen, Dataviz, Design

We also continued our Stamen dataviz and mapping workshops, some onsite for clients, and some for the general public in our San Francisco studio. These classes were increasingly a collaboration with the talented Curran Kelleher who has extensive experience developing online teaching materials for D3.js and for data visualization in general. We did our last in-person workshop in San Francisco in November 2019, and tried out our first virtual class in April 2020. We think our first foray into online teaching went well, but are still doing some serious thinking about how we really want to continue our training initiatives, whether in person, online or both. For now our workshops are on hiatus, but please keep in touch if you want to be notified of when classes start up again!

Screen grab of Curran and I kicking off our first virtual workshop in April 2020

Also in 2019 I attended the d3.unconf in Chicago, where we all got custom name badges using a data visualization we created, printed using 3D lenticular lenses so the visualization would change when you look at it from different angles. I made an “interactive” map using data from my old Pop vs Soda website. Here’s a video of the name badge in action:

Writing

Starting in late 2020, I was able to spend more time to write some more long-form blog posts for Stamen. These were a fun experience to put together, and a new way of thinking about writing for me: these posts were less about my personal views on any topics, and more of an attempt to synthesize all the fascinating but not-ready-for-prime-time conversations, thoughts, and passing observations that happen around the virtual water cooler at Stamen (basically, on Slack and in the margins of our Zoom meetings).

First I wrote a long piece about election maps that was republished by Fast Company: “We design maps for a living. Here’s who got the 2020 election right.”

I wrote a pair of posts surveying the best maps and data visualizations from the pandemic: “Visualizing the pulse of a pandemic: A year of COVID line charts” and “Corona-cartography: what we learned from a year of COVID-19 maps,” which included a bit of autobiographical-visualization of activity in our own Slack channels:

Posts about coronavirus in Stamen’s internal Slack channels. This includes our general discussion about #coronavirus and our internal #coronaproject (which became http://studio.stamen.com/hospital-map) but does not include posts about COVID-related client projects

I should also admit that some large parts of those blog posts were sourced from Twitter rants I’d already made about the topic. Here’s an experiment: these maps use simulated COVID data. In which scenarios do you think the covid rates are higher in Canada? In which scenarios are the rates higher in the US? In which maps are they the same?

Images from a twitter rant about misleading COVID maps

Spoiler, they’re all the same data! In short: the way you classify and cluster your data can have a huge effect on how readers interpret it. Thank you for coming to my TED talk.
Project Showcase: Hormone Season for Scientific American
Tags: Data Visualization, Science Journalism, Dataviz, Scientific American, Scientific Research

To communicate the immensely interesting findings to their readership and beyond, Scientific American asked us to help them visualize the clock–like rhythms of hormonal growth.

A recreation of our process, from plotting to coloring and refinement.

A team led by Accurat Co-Director of Technology Mariano Viola pored over the research paper and consulted our Data Scientist Otho Mantegezza—who has a PhD in biomolecular sciences, to boot—for extra help in cleaning and organizing a .CSV file filled with raw scientific data. In R Studio, he plotted variables in a small multiples visualization, essentially sketching an outline for each dataviz. Designers were left to simply refine graphic elements with respect to style and layout.

The collaboration is our most recent with Scientific American. Since 2016, we’ve developed a full–page spread from Julian D’Huy’s research on how the evolution of folktales can shed light on patterns of human migration, and visualized the makeup of the Svalbard Vault: a protected store of 1.05 million environmentally regenerative plant seeds.

The oldest continuously published magazine in the United States, Scientific American has made the work of researchers accessible to ordinary people since 1845. Its journalists often play double duty as translators, reworking academic texts—such as the one by scientists at Weizmann Institute of Science—into journalism comprehensible to laypeople. Data visualization is therefore an essential item in editors’ toolkits—it obviates the need for words altogether.

See the full spread below, or read a digital version on Scientific American’s website.
Introducing POLLEN!
By: Julia Krolik
Tags: How To, Data Visualization, Dataviz, Navigation, Information Design

With such an engaged Slack community, it’s difficult for new members to get a sense of our channel landscape and amazing discussions. I kept thinking that it should be easier to see, discover, and join Slack channels, ideally all in one place.

HOW IT STARTED

To help with this, my partner Owen Fernley and I decided to create something interactive that could visualize all our workspace channels. This seemed like a fun little project for our consulting company.

It was about a year ago and the conversation went something like this:

The inspiration to visualize the DVS Slack channels came from discussions on Slack about the current channel naming structure. The DVS channels are organized into different categories and are separated by their channel names with a hyphen. For example, #location-nyc would be the “nyc” channel under the “location” category. That meant we were working with two hierarchies: channels and the various categories they belong to. As a first step, I made a quick visual to see what the channels would look like all grouped together into their categories.
Trends in Local Elections in 2019
By: Daniel Mcglone
Tags: Government, Dataviz, Elections

Since March 1, 58 cities that Cicero monitors in the U.S. have had elections. On the Cicero team, we track local elections in nearly 400+ cities worldwide — and cities do not often have election cycles in sync with other state or national elections. In total, over 500 elected officials were up for election in major cities like Chicago, Dallas, and Denver. Texas, by far, had the most elections in cities Cicero monitors, with 24 cities having an election.

Elected Official Turnover

We measured the percentage of new officials in each state after the election. Of states with three or more elections, Oklahoma had the most turnover at 27%, followed closely by Colorado at 26%. Florida, Illinois, and Texas all had turnover around or just under 20%, while Nevada and Missouri saw very little turnover — 10% or less. The table below contains a summary by state of the elections so far this year.

Member change in local elections by state, Spring 2019

Partisanship Change and Turnover by City

Many municipalities elect officials as nonpartisan. However, many mayors and councilmembers are affiliated with a political party. We track partisanship of officeholders when it is available. The elections this Spring had one major turnover for political parties. The Mayor of Phoenix switched from Republican control to Democratic following the election of Kate Gallego. The election was decided in a runoff in March after the general election last November.

Of the 58 cities with elections, most saw at least some turnover. Jacksonville, West Palm Beach, and Tampa had nearly half their city council turnover. However, each city had many officials not running for re-election. Several cities in Texas saw every officeholder re-elected, most notably — Fort Worth’s Mayor and Council.

Member change in location elections by city, Spring 2019

Interested in election data? Follow our Twitter feed of elections to track upcoming elections around the world. Play with the data yourself by signing up for a free trial and getting access to our API for 90 days. Or read our blog on How to Make an Election Calendar with D3.js and Observable Notebooks.
Data Visualization For The Kings
By: The Visual Agency Editorial
Tags: Data Visualization, Information Design, Dataviz, Infographics, Charts

Some historical examples of data visualization still amaze us today and can teach us a lot.

Let’s look at an old “dashboard” of over 200 years ago by William Playfair, an English statistician. He is credited to be the father of data visualization having used bar charts, line charts and pie charts for the first time to show numerical entities. The work of Playfair is therefore a fundamental piece in the history of charting and graphics. Playfair drew this chart to visualize the economic entities on the different European nations and it was published in his “Statistical Breviary” in 1801.

He had no technological tool at his disposal, but only a ruler and a compass. With these tools alone, he created a table that never ceases to amaze for the clarity and richness of information on a single sheet of paper. Incidentally, this is also the first case in which a pie chart is used to represent numerical information.

Now let’s try to break it down into its essential components. The circles represent the different European nations, and the area of each circle represents the area in square miles of the nation. The biggest on the left is Russia; the green circle represents the extension in Asia and the red circle is the extension in Europe. Russia is followed by Turkish Empire and then all other countries back them. The size of the Empire is splitted among Europe, Africa and Asia, making it the first pie chart to appear in history.

Then there are two vertical axis, one on the left and one on the right. The bar on the left of each bubble represents the population in the country; the bar on the right instead represents the “revenue”, it is not clear if revenue is comparable to taxes collected in each country or what we call GDP today: the segment that unites the two vertical bars gives us therefore the ratio of revenue per capita.

Recently it happened to us to do a course of data visualization to a team of data scientists of a multinational company, and we gave them as an exercise the underlying dataset to the visualization of Playfair (without showing them the Playfair Chart). The result was depressing and interesting at the same time: some students took the data set and placed it in Spotfire, their company dataviz tool and they could not find a synthetic way to visualize the different variables. Unexpectedly one person, who did not have access to the tool, used his imagination and drawing with a pen on a piece of paper managed to devise a really effective way of presenting data!

This should make us meditate: we often do not think about visualizing the data with our brain, but we are guided by the tool we have on our desktop; instead we should refine our analysis and information design skills without delegating too much to automatic tools — which are only a means, not the end.
An Analysis of Tactical Voting Sites for UK General Election 2019
By: Ahmad Barclay
Tags: Labour Party, Liberal Democrats, Politics, Dataviz, General Election 2019

I’ve been following the debate about tactical voting in the 2019 UK General Election, and particularly the controversy about Best for Britain’s site getvoting.org, which is accused of giving “bogus” advice based on a fairly opaque methodology (see this Guardian article).

So… I decided to get the constituency-by-constituency data from this site and a couple of others (tactical.vote and tacticalvote.co.uk) to see how they compare, and whether they pass a basic “plausibility” test based on % voting swing needed from 2017 results.

I narrowed down my analysis to the 317 seats won by the Conservatives at the 2017 General Election, since these are the core focus for tactical voting, whether to Stop Brexit or just get the Tories out. This chart shows which party came second in each of these constituencies in 2017.

I narrowed down again to look at % swing needed for the Liberal Democrats or Labour to win in each constituency. The baseline tactical strategy would be to vote for whichever party came closest to beating the Conservatives in 2017, and this is almost exactly what tactical.vote advises.

The same plot for getvoting.org shows something very different. Even on the assumption of a huge swing from Labour to the Lib Dems on 12 December, there’s no plausible “tactical” case for voting for Lib Dems in clear Tory/Labour marginals like Watford or Hendon.

Meanwhile, tacticalvote.co.uk takes a more sensible/cautious approach by reserving judgement on many constituencies until later in the election campaign. As the 2017 General Election and 2019 EU Election showed, there can be a huge shift in public opinion during this short period.

Almost by definition, “tactical voting” is worthless if voters who want the same thing follow contradictory advice. Whatever the “science” behind getvoting.org, its advice contradicts the others and doesn’t pass a basic plausibility test, so I would suggest to view it with extreme caution.

You can see all the data that I compiled for this analysis in this Google Spreadsheet. The constituency-by-constituency data is based on this spreadsheet compiled by Britain Elects.
UK Tactical Voting Analysis, Part 2
By: Ahmad Barclay
Tags: Labour Party, Liberal Democrats, Politics, Dataviz, General Election 2019

Last week I shared a comparison of the tactical voting advice of three different sites for the 2019 UK General Election. Since then tacticalvote.co.uk has updated its advice, and Gina Miller has just launched remainunited.org. So here’s an update…

Like getvoting.org, the advice of remainunited.org is based on a “Stop Brexit” logic rather than a “Tories Out” one. More importantly, it’s also similarly based on a methodology similar to MRP (multilevel regression and post-stratification), derived from polling data. However the results prove that not all MRP-style models are equal…

Here’s the comparison vs the other tactical voting sites. Interestingly, the four Conservative vs Labour marginals that I highlighted as “implausible” calls for the Lib Dems by getvoting.org are all slated as tactical votes for Labour by remainunited.org.

Helpfully, remainunited.org shared their data, and since it includes predictions for all UK constituencies we can plot the election outcome their model predicts with/without tactical voting. If it’s right, the difference could be a Conservative majority vs a majority for remain (or at least for a second EU referendum).

A lot can change between now and election day. If polls are trusted Labour are seeing modest gains vs the Lib Dems, while the Conservative vote seems to be holding up vs the Brexit Party. Tactical voting could be decisive at this election, but all this conflicting advice to voters doesn’t really help matters.

I have made available all of the data that I compiled from the 4 tactical voting sites, along with 2017 election results. I won’t promise that it’s error-free, but you’re all welcome to make use of it!
📈 I’ve open-sourced a simple Coronavirus (COVID-19) dashboard (React + Chart.js + BootstrapTable)
By: Oleksii Trekhleb
Tags: Data Visualization, Web Development, JavaScript, Data, Dataviz

I’ve recently open-sourced a new 📈 Coronavirus (COVID-19) Dashboard which shows the dynamics (the curvature of the graph) of Сoronavirus distribution per country.

Reasoning

The reason for creating a new dashboard was to complement the well-known JHU Dashboard (which is made by Johns Hopkins CSSE) with the feature of seeing the charts with the number of COVID-19 confirmed / recovered / deaths use-cases per country.

Basically I personally had a question like: “What about the Netherlands/Ukraine?”, “Is the virus spread (growth factor) slowing down?”, “How I can compare the recovered/deaths dynamics per-country?”, “Which countries are doing the proper things to slow down the growth-factor”.

Here is how the main functionality looks like:

Data source and tech-stack

The dashboard is using COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE as a data source.

Front-end wise I’ve tried to make it as simple as possible, therefore the dashboard is using a pure React.js (without JSX transpiler or CreateReactApp starter). To display the data I've used Charts.js to draw the chart and Bootstrap Table to display a sortable, searchable and clickable data table.

Main Functionality

The dashboard is still raw but it provides a basic functionality of displaying the global and per-country data charts.

For example here is how Global dynamics of confirmed/recovered/deaths use-cases looks like as for March 23rd:

Here we may see a positive dynamics for China (Hubei):

We may also compare Italy to Spain:

The regions are displayed in sortable, searchable and clickable data-table:

Known issues

The following functionality is not implemented yet but it would improve a usability of the dashboard: