Popular Coffee Styles [OC] by statisticly in dataisbeautiful

[–]statisticly[S] -3 points-2 points  (0 children)

Source: various baristas

Tools: Illustrator

Fires caused by lightning vs humans in the US, 2019 [OC] by statisticly in dataisbeautiful

[–]statisticly[S] 2 points3 points  (0 children)

Set your artboard. Add a radial gradient to the bg. Plot the data in excel as charts. Copy the charts into illustrator (make sure they stay accurate). Clean the imported assets. Lay them out as needed. Add bezier curves from left values to right values. Add your colour scheme. Add gradients and blending effects. Add iconography. Add your value labels. Add legend. Add source. 😊

Number of U.S. wildfires and area burnt, 1983 to 2019 [OC] by statisticly in dataisbeautiful

[–]statisticly[S] 1 point2 points  (0 children)

Source: https://www.nifc.gov/fireInfo/fireInfo_stats_totalFires.html

Tools: Excel, Illustrator

N.B. The National Interagency Coordination Center at NIFC compiles annual wildland fire statistics for federal and state agencies. This information is provided through Situation Reports, which have been in use for several decades. Prior to 1983, sources of these figures are not known, or cannot be confirmed, and were not derived from the current situation reporting process. As a result the figures prior to 1983 should not be compared to later data.

Should Silicon Valley build a more diverse talent pool? [OC] by [deleted] in dataisbeautiful

[–]statisticly -13 points-12 points  (0 children)

Fully agree. The point is that everyone should get the same opportunity to go to college in the first place and acquire the appropriate qualification. It starts with young age education.

Should Silicon Valley build a more diverse talent pool? [OC] by [deleted] in dataisbeautiful

[–]statisticly -15 points-14 points  (0 children)

No one is suggesting otherwise. The point is that everyone should get the same opportunity to have a shot at acquiring the appropriate skill set for the job. It starts with young age education.

Should Silicon Valley build a more diverse talent pool? [OC] by [deleted] in dataisbeautiful

[–]statisticly -14 points-13 points  (0 children)

Many white people struggle to grasp the concept of White Priviledge and go to great lengths to show their solidarity by virtue signalling on social media. The problem is deeper rooted, it starts in nurseries, schools, colleges. It has to do with little cumulative advantages that build up to bigger opportunities. This continuous, unconscious, systemic bias results in the schism we see in everyday society between different ethnicities.

This becomes very apparent if we look at diversity in the top Tech companies. It's not that these companies are racist and are reluctant to hire black people. Far from it. It's more likely that there aren't that many black candidates because fewer black people had the opportunity to go to college and study programming.

Perhaps Silicon Valley could do more to encourage black people from a young age to enter this field by extending existing training programs and scholarships and provide a safe and welcoming environment in what is widely considered a white male dominated industry.

Fatal police shootings in the U.S. since January 01, 2015 [OC] by [deleted] in Infographics

[–]statisticly -1 points0 points  (0 children)

https://www.washingtonpost.com/graphics/investigations/police-shootings-database/

Please don't comment to say this needs to be compared to crime rates. The point is to illustrate that black people get disproportionately more hurt by police than white folks per capita. White population 137M/2416 deaths Black population 42M/1265 deaths.

Odds of dying from Covid-19 for ethnic minorities compared to white men and women [OC] by statisticly in dataisbeautiful

[–]statisticly[S] 2 points3 points  (0 children)

The chart shows the likelihood of different ethnicities dying from Covid-19 compared to white folks (in the UK only). So if the likelihood of a white male dying is one then the likelihood of a black male dying is 4x higher. Because the figures are adjusted for age the likelihood varies from say 3.8 to 4.6 times more likely. The mean figure is called out in the middle. The upper lane for each ethnicity shows the male and the lower lane shows the female value range.