All posts by Minda Smiley

Pitch: College Majors, Men & Women

Partnering with Rikki Reyna

Headline: Women now complete more college degrees than men – but what are they majoring in?

Slug – College Majors

Key Elements: Women are now completing more college degrees than men. What’s less widely know is what women are majoring in. Although women reached (and surpassed) equality in terms of college attendance and graduation rates in comparison to men, there still remains a large gender gap in terms of college major choice.

News Hook: There is a gender gap in the workforce, with men still making more money than women. This data shows another gender gap that exists – before men and women start their post-graduate careers.

Link to data: http://onlinelibrary.wiley.com/doi/10.1111/iere.12040/full

Festival of Data – Smiley

http://www.theguardian.com/news/datablog/interactive/2014/jan/29/state-of-the-union-address-obama-twitter-reaction

This is an interactive visualization on The Guardian where the Twitter Data team and Datablog editor Simon Rogers show the response Obama’s SOTU speech by US geography and subject. For the subject graph, they have a timeline starting at 9:15 and ending at 10:20 pm. documenting the use of the 9 most popular hashtags used in one minute intervals. They also have the entire speech transcribed, and when you scroll down it will highlight a segment of the speech and show the what hashtags were being used during that time in the map above.

It also breaks down the speech by geography. So, when you’re hovered over a certain part of the speech, a map on the right side will show you how engaged each state was with each hashtag at that particular moment.

I think this is interesting because it maps out social media data in a way that shows what Americans (who use Twitter) found most important about the SOTU and in what states. I think the biggest thing missing in this interactive is the fact that there are no hard numbers.

Hartman/Smiley Storyboard

Madison Hartman & Minda Smiley

Title: It’s All Fun and Games Until Someone Gets Hurt: Hundreds of Olympians are Injured During Winter Olympic Games

Slug: Olympic Injuries

Story: http://www.scrollkit.com/s/JcoKDoJ 

As the winter Olympics heat up in Sochi, each news cycle alerts us of yet another athlete’s injury. Before the games even began, a young American free skier broke her leg and had to be wheeled through the opening ceremony.

Data from the 2010 Winter Games in Vancouver breaks down all the injuries from those games. The International Olympic Committee (IOC) commissioned a team of doctors and researchers to record injuries that took place during the games in order to find out more about injury rates during the games to try to combat them in the future.

We’ve talked to Doctor Lars Engebretsen, Head of Scientific Activities for the IOC, to help put some of the information in context. We also spoke with the Minnesota Gophers Athletic Trainer for Men’s Hockey so he could help explain why so many hockey players get injured. Currently, we are trying to get in touch with someone at the Sport Injury Prevention Center to see if they have been influenced by the study. We would also like to know what work they have done to try to prevent Olympic injuries.

Our graphics break down the injuries primarily by gender, sport, and injury location. Our first graph features a skeleton (front and back) that shows total injuries by location and number.

Our second graph is a bar graph that shows injuries by event type using percentages. For example, the first and highest bar shows that ice hockey contributed to 18% of the injuries. When you hover over the bar, you can see just how many injuries there were (in this case, 82).

Our third graph is also a bar graph that shows the most common injuries by place of injury and sport.

We also include some key takeaway facts, including most dangerous sport for female athletes compared to male athletes as well as which gender received more injuries per 1,000 athletes.

Overall, our work distills this information into graphics that can help readers get a more comprehensive understanding of just how Olympic injuries break down. Through adding expert opinion, we are able to see how this data compares to the injuries in the recent Sochi games as well as find out what steps are being taken to prevent injuries. They can also explain why some sports are more dangerous than others and what can be done to help athletes compete as safely as possible.

Smiley Data Sets

 

1) http://www.rtknet.org/db/erns/substance

 

 

 

This data was compiled by ERNS, the Emergency Response Notification System. It provides information for toxic chemical spills and other accidents for 2012, including substance, number of incidents, deaths, hospitalizations, injuries, evacuations, and property damage. It’s interesting because these incidents have been in the news recently- the chemical spill in West Virginia, the toxic ash spill into a North Carolina river, etc. Incidents like this affect everyone because many times they affect drinking water. I think graphing this data could help give more insight into these incidents and possibly lead to a deeper story.

 

 

 

2) http://www.health.ny.gov/statistics/vital_statistics/2011/table04.htm

 

 

 

This data was compiled by the Department of Health and includes birth summaries in New York State for 2011 broken down by race and ethnicity. A recent government study found that in 28 states (including NYC), first-time C-sections declined to 21.5% in 2012, from 22.1% in 2009. Since this data includes the method of delivery, it would be interesting to map this out and find out if there is any correlation between method of delivery and race/ethnicity in New York State.

 

 

 

3) https://data.cityofnewyork.us/Social-Services/Dirty-Water/k2um-vsan

 

 

 

I found this data from NYC Open Data. It’s based on 311 Service Requests from 2010 until the present, so it’s changing every day. It includes exact date & time of complaint, complaint type (water quality or water system, drinking water) and even sometimes includes a description of what is wrong with the water (tastes bitter/metallic, looks cloudy, etc.) I think this data would be interesting into mostly because I think it might show patterns (certain boroughs, neighborhoods, streets having more problems than others, etc.) Analyzing this data could also help when it comes to looking into other data about water in NYC. For example, if complaints from a particular area in Queens keep resurfacing over time, it may be worth looking data about that area’s water system/quality.