prettymaps VS owid-grapher

These maps look great! Reminds me of a project I saw a long time ago [1]. Glad you made this downloadable for everyone who cannot write code.

P.S.: Now, I also have some (birthday) presents ;-)

[1] https://github.com/marceloprates/prettymaps

You can generate them yourself from Open Street Maps. Use this Google Colab. Source - prettymaps.

Check out this repo

Check out Pretty Maps, or the online version, and /r/prettymaps_

Great work! I love osmnx, it's such a nice library. I remember using it at school to work out distance from fire stations across my city. There's this nice library that uses it called prettymaps, I've been meaning to take some time with it to do some sweet posters.

prettymaps – A small set of Python functions to draw pretty maps from OpenStreetMap data

Globally, since the advent of widespread computer use, human life has improved by orders of magnitude along every metric we have: poverty, hunger, disease, literacy, child mortality, life expectancy, homicide rates, etc., etc., etc.: https://ourworldindata.org

Respectfully, you are the one making an extraordinary claim in need of evidentiary support.

That's actually the part about people constantly negging on social sciences [1] that I often find confusing.

There's huge amounts of data available (geography, lots and lots of maps; history, huge amount of historical documentation; economics, vast amounts of public datasets produced every month by most governments; political science, censuses, voting records, driver registrations, political contest results all over the Earth - often for decades if not centuries).

Most is relatively well verified, and often tells you how it was verified [2]. Often it's obtainable in publicly available datasets that numerous other researchers can verify was obtained from a legitimate source. [3]

There's lots of data available. Much is also verifiable in a very personal way simply by walking somewhere and looking. In many ways, social sciences should be one of the most rigorous disciplines in most of academia.

[1] Using Wikipedia's grouping on "social sciences" (anthropology, archaeology, economics, geography, history, linguistics, management, communication studies, psychology, culturology and political science): https://en.wikipedia.org/wiki/Social_science

[2] Census 2020, Data Quality: https://www.census.gov/programs-surveys/decennial-census/dec...

[3] Economic Indicators by Country: https://tradingeconomics.com/indicators

[4] Our World in Data (with Demographics, Health, Poverty, Education, Innovation, Community Wellbeing, Democracy): https://ourworldindata.org/

Population data by country over time was scraped from ourworldindata.org

> filled our heads with candied dreams of endlessly-spanning information super-highways

* https://www.wikipedia.org

* https://www.openstreetmap.org

* https://github.com

* https://data.gov

* https://fred.stlouisfed.org

* https://ourworldindata.org

* ...

Plus the countless documentation websites that we technical writers lovingly toil away at day-in and day-out.

Neat!

This is one of my favorite spaces, so I'll add some generic advice which may or may not be helpful.

I once had the privilege of working for Max Roser and Hannah Ritchie at Our World in Data, as one of the engineers on their Grapher library (https://github.com/owid/owid-grapher), and learned a ton from them (and others on the team) about making great charts.

My one piece of advice from looking at your examples would be: don't neglect title, subtitle, and caption! They would be so easy to do well because you've already created your "simple headers thingies". A few words go along way. Check out "Storytelling with Data" by Cole Nussbaumer Knaflic for a great read on the subject. Owid's Grapher does those the best, IMO (followed closely by DataWrapper.de -- but that's not open source).

At some point, if you keep up with this, you'll also want to add a dataflow library and DSL. Hadley Wickham's dplyr in R was the GOAT, and I copied that in my Ohayo tool and in OWID Grapher's CoreTable library (https://github.com/owid/owid-grapher/tree/master/packages/%4...). Jeffrey Heer's newish Arquero (https://idl.uw.edu/arquero/) library is also along those lines.

Lately I've delving into Mike Bostock's new thing Plot (https://observablehq.com/plot/). So far, excited by it, but only spent a day or two with it at this point.

I don't use emacs anymore, but hopefully something helpful in the comments above.

https://ourworldindata.org/

I reach for it several times per week. Never struggle finding what I want, nor getting it into the shape I want it.

Let’s begin with a data visualization-friendly resource.

Here's a dashboard: https://ourworldindata.org/

Pick almost anything to see a positive trend.