chekos-visuals-blog
visuals
i'm interested in exploring graphic design and informative art. this is a collection of visualizations i've created along the way.
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#100Viz 08 Testing Gifs
This entry is more practice on small technical things. I wrote a small medium post on a simple ipywidgets and Altair application and I wanted to expand on that. What if instead of a drop down menu I would play a set of values essentially creating an animation of sorts. I know there's gganimate in R but I wanted to see if I could do it in #python. -
I bet this could be more engaging of the data had more dramatic twists or if I had more series but I wanted to concentrate on developing the skill to animate. the analysis part will be for another day. -
today i didn't feel like working on this challenge much but I know that what makes it effective is putting in the time and so I chose a smaller and more concrete skill to practice (animating) rather than a whole analysis + viz. We'll see how it goes tomorrow.
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This 7th installment is a small remix of 02. I got some feedback on r/DataIsBeautiful about the order of the bars in the bottom half.
If the legend is next to the top chart the reader needs to go up and down to understand what the bottom charts are showing.
The bottom charts are to serve as thumbnails in a sense. They are not supposed to give you detailed information of each country but more overarching themes or patterns. For example, you can see Mexico's "Less than high school"[red] numbers drop drastically and "Some college"[blue] numbers rise in all 5 countries. You can also see India's numbers remain stable through the years.
This was good practice, revisiting a visualization feels more ok now. This was also great altair practice. I used the alt.Order() encoding for the first time. These types of data transformation in the visualization seem to be super powerful if you can use them wisely.
I really like this visualization, I wonder how it would look as a page spread on a magazine or a newspaper.
This is the end of week 0 of #100Viz. I already feel like I learned a lot, can't wait to see what else I can come up with in the following weeks.
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At first I was hesitant of remaking charts for my #100Viz challenge. If I only have 100 “entries” I want them all to be unique charts. But today I listened to @dataviztoday and I was re-inspired to remake yesterday’s attempt to make it more inviting like she said. Yesterday’s visualization is really cool, the data is cool, isotypes are cool, etc. but it’s literally *too* long to even read it. -
Today’s #100Viz visualization: 06 Visualizing Strata (Remix)
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Mexico’s National Institute of Staistics, Geography, and Informatics (INEGI) in 2016 introduced the Module on Intergenerational Social Mobility (MMSI). “Its objective was to generate statistical information on intergenerational social mobility of the population aged 25 to 64 years in Mexico, noting the influence of characteristics such as educational level, socioeconomic level and occupation of economic ascendants, from a certain condition in the current socioeconomic position of their descendants.”
Question 3 of Part 10 asks the respondent to identify, in a color wheel presented, what they think the color of the skin of their face is.
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Mexico’s history of racism has been long and complex. Since the years of La Raza Cosmica to older matriarchs asking young women to marry white to better the race, colorism is omnipresent. As modern Mexico, starts to tackle these issues at a societal scale, understanding “how much” it affects Mexicans will become more and more important.
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The objective of this chart wasn’t to dig deep into the numbers but paint a high level picture of the trends.
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In most states in Mexico, most of those who identify their skin color as “lighter” tend to also be in the "high" socio-economic stratum. Those who identify with darker skin tones are more likely to populate the lower socio-economic strata.
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inspirado por una visualización que me encontré en Twitter de Luis Monroy-Gomez, la quinta instalación de #100Viz hoy es una reimaginación de la suya.
Mucho más simple, el análisis pudiera ser más complejo si fueran días y no una hora el límite que me impuse 😂 first isotype in Altair! yay
México has some really interesting datasets open to the public. This one (MMSI or Módulo de Movilidad Social Intergeneracional) had the objective of finding out the influence of things like educational attainment or economic status of ancestors have on the questionnaire taker’s current status.
Incredibly interesting!
They actually ask the survey taker to identify in a color scale what they perceive to be their face’s skin color!
I wonder what the US’ equivalent of this graph would look like.
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#100Viz 04 Afro-Mexicans (Remix) Ft Datawrapper.
What took me forever yesterday took me < 30 minutes using Datawrapper.de + mapshaper.org
and now it's interactive! AMAZING. the link is in the bio if ur interested.
I was hesitant at first about repeating a box in a row but I really couldn't let go lol. the scale is a little better but the variation is so large that it's hard to visualize it.
This is a lot smaller than my old "interactive" version too. I don't know what I did but I ended up producing a 134MB HTML file for the map only 😂.
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#100Viz 03: Reckoning Roots. My self-imposed time limit got the best of me on this one 😭 I spent most of the time figuring out how to get a shp file to geojson (geopandas is amazing but so fragile 🙁) I think this could be an interesting viz, may revisit later in the challenge.
In 2015, for the first time ever, Mexico asked its citizens if they self-adscribed as black or “of afro-descendence” finally recognizing thousands of Mexicans’ identities. Mexico has always had a sizeable black population but it’s barely being recognized today.
Some things I’d like to work on:
0) working on the legend. the high percentages in a handful municipalities totally mess up the scale.
1) mapping geographical data: it’s so dope.
2) annotations on maps: maps are so rich and informative because they have so many dimensions of data all at once but, for example, it would be helpful to have some annotations like the state name in this map.
3) developing a workflow for geospatial analysis
4) figuring out how to use GeoPandas effectively.
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#tbt one of my favorite visualizations I've ever worked on.
Kendrick Lamar's DAMN.
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second #dataviz installment of my #100Viz challenge: Arriving Education.
This is a reimagination of a chart I worked on for work. Most immigrants coming to California are arriving from countries in Asia, not Latin America as the public discourse may suggest. Secondly, most people arriving to California are highly educated. Lastly, while not in the chart, 2016 was the first year Mexico 🇲🇽 is not the top country of origin of recently arrived immigrants: it’s China 🇨🇳.
Pro-tip for those using ACS/ipums data: use the detailed-level code for “bpl” (birthplace) because some countries are clumped together sometimes. For example, the numbers for general-code “India” are a lot higher than the detailed-level code because they group Bhutan, Pakistan, Sri Lanka, and others under the label “India”.
more at the original post: bit.ly/PPIC-recent-immigrants
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first installment of #100Viz: Fighting Fire in California. Inspired by the @latimes story by Ruben Vives on Mexican immigrant firefighters in Redding helping contain the Carr fire.
Here are the numbers for firefighters in California according to the American Community Survey and the largest fires in CA highlighted.
I would have thought there would be a positive relation, that more fires would mean more people are signing up to fight them. However there seems to be no correlation. What is surprising too is that there are only about 35,000 firefighters in California. I don’t know why but I expected the number to be a lot higher.
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I’ve been listening to The Honest Designers Show for a week non-stop now and I’m starting to think that graphic design might be something I want to explore more seriously. I’ve been interested since a teenager but never enough to dive deep. I was inspired by their episode on their daily drawing challenge and decided I could try my own version of it. I’ve chosen to mix it with another passion I have: coding. I’ve been constantly inspired to practice every day with the help of the PyBites guys and this is what I came up with: a #100Viz challenge.
I will create 100 data-driven visualizations. I don’t have a timeframe in mind but I will work on them daily for 30 to 90 minutes. I will be using publicly available data and publishing the code + data on GitHub. The goal is to experiment and grow as a visual artist and as a data analyst. I already spent a few hours this week putting together a color scheme and it’s been great fun not only making sure the colors work but also coding the chart config to create a theme (I’m using `altair`). We’ll see how this goes!
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