Ivanova/Johnson – Hospital Charity Care Storyboards

2014-02-28 16.00.16

The bar chart shows the change from 2008 to 2010 in hospitals’ financial aid distributions.

With a scatter plot, we try to show that there’s no correlation between how much funding a hospital gets and how much financial aid it gives out.

2014-02-28 16.00.03

 

REVISED PITCH

A proposed title or headline

Not so charitable

A story slug — up to three words that capture the essence of your story

Charity care

A list of the story’s key elements

1. Hospitals are required by law to accept patients regardless of the patient’s ability to pay. In New York State, a fund called the Indigent Care Pool exists to cover the costs of treating patients who can’t pay for their care.

2. Despite receiving funds to care for indigent patients, at least 11 hospitals, including five in and around New York City, did not approve any applications for charity care.

3. These hospitals are in some of the wealthiest parts of the city.

 A news hook, or explanation of why this story matters now

With Obamacare and Medicare cuts in the news, health care costs–not just how we pay for care–are once again in the public discussion. New York State, despite its reputation for pricey health care, has a program in place called the Indigent Care Fund, which gives money to hospital throughout the state that have a lot of poor or uninsured (read:unprofitable) patients. But many hospitals don’t treat indigent patients, though they receive government funds for their care. Data collected by the Community Service Society shows that 11 hospitals in the state that get funding for charity care offer none, or little. Conversely, public hospitals that treat many indigent patients often get far less funding than private hospitals who approve fewer charity-care applications. The Brooklyn hospitals that have closed in the last five years were all significant providers of charity care. They haven’t been replaced by comparable medical providers.

A description of and link to the data

  • A 2012 report from the nonprofit Community Service Society describes the 201 hospitals in New York that provide charity care and compares it to the number of applications they approved (report, and report summary.)

  • A list of hospitals open, closed, and close to closing in New York City as of 2014 (compiled by Irina Ivanova and Victoria Johnson from a mayoral speech on 2/20

https://docs.google.com/a/journalism.cuny.edu/spreadsheet/ccc?key=0AvuYANAX-U9QdDFrSFZXRWN6X2xBdlNyWk9OdzMtZ0E&usp=sharing#gid=0

  • A look into the average and median costs for over 1,400 different kinds of procedures in New York State (including NYC) hospitals from 2009-2011.

https://health.data.ny.gov/Health/Hospital-Inpatient-Cost-Transparency-Beginning-200/7dtz-qxmr

 Sources

  • Elisabeth Benjamin, Vice President, Health Initiatives/Community Service Society of NY. (212) 614-5461; ebenjamin@cssny.org. We’ve spoken with EB about the 2008 data, which concluded that there was no oversight of hospitals distributing financial aid. She pointed us to another researcher at CSS who did the bulk of the report and we’re getting in touch with her for help reading the 2010 data.

  • Robin Gelburd, president, FAIR Health. 212-370-0704

  • Keith Smith, surgeon who runs a cash-based, transparently priced practice in Tulsa, OK.  KSmith@surgerycenterok.com; 405-627-0274.

  • (This one’s on hold for now since we’re unsure if, or whether, they’ll figure in the story. CityMD, spokesperson: Tanyelle Broschart 917 622 3226; tbroschart@citymd.net)

Sharif/MacVey: Starbucks

 

Page Layout:

PageLayoutStarbucks

 

MapZIPSwithFirstStarbucks

MapStarbucksBoroughCount

IllustrationStreetviewStarbucks

BarChart_BeforeAndAfterStarbucks

SubwayStarbucksChart

SubwayRentChart

+ a proposed title or headline: Starbucks and Sky-High Retail Rates

+ a story slug — up to three words that capture the essence of your story: Starbucks; Neighborhood; Rent

+ a list of the story’s key elements:
  •  Does the opening of a Starbucks mean that all of the local retailers in your neighborhood will close and apartment rents will skyrocket?
  • How does Starbucks choose it’s new locations in New York City?
  • What do the de Blasio administration and the New York Economic Development Corporation have planned to support small businesses in the outer boroughs?

+ a news hook, or explanation of why this story matters now:

        With Bill de Blasio taking office, there is increasing attention on changes to the outer borough neighborhoods and what types of demographics those changes are being catered to. Starbucks is planning to open 3,000 new stores in North America by 2017 (we will find information about what new locations are currently slated in New York City).
+ a description of and link to the data (which means you have to find your data!):
Cleaned up Starbucks data:
Retail Rent Data:
+ one source you have already spoken with or at least three potential expert sources and your plans for reaching them:
  • NYU Furman Center for Real Estate and Urban Policy.
    Shannon Moriarty
    Communications Director
    Email: shannon.moriarty@nyu.edu
    Phone: 212-998-6492
  • Real Estate Board of New York.
    Jamie McShane,
    SVP Communications
    jmcshane@rebny.com
    212-532-3100
  • Paul Milstein Center for Real Estate Research at ColumbiaMedia InquiriesKeshia Mark
    klm74@columbia.edu
    Evan Nowell 
    egn2109@columbia.edu
  • Baruch Real Estate
    George Donohue
  • Todd Trewhella. Director of Development at Starbucks Coffee Company
  • Someone at RKF. A large New York retail real estate broker
  • Farron Roboff, Senior Vice President at Royal Properties. Has leased to Starbucks locations.
    press contact is Jeff Kintzer jeff@royalpropertiesinc.com

Gomez/Eugenios, Permit Fees storyboard

Two charts for the wages of DOB-related jobs
Two charts for the wages of DOB-related jobs

Byline: Camilo Gomez and Jillian Eugenios
Hede: How much does a DOB license fee really cost?
Slug: Permit Fees

Key elements:
Permit fees per job
Median annual wage per job
Jobs listed with DOB
Information on where permit fees go

News hook:
Construction jobs are on the rise, in an industry that has continued to show healthy growth post-recession. However, many of those jobs require permits that must be renewed every one to three years, bringing into question whether or not some pay too much based on how much they earn.

Description of data and link:
Department of Buildings information on NYC Open Source Data
https://data.cityofnewyork.us/Business/DOB-License-Fees/vi6e-zw9u

Findings:
Since the information in NYC Open Data dated from 2009 we visited the DOB site to get updated information: http://www.nyc.gov/html/dob/html/development/licensing_main.shtml.

We clicked through each job permit below the heading “Select a category” and built our own updated excel spreadsheet.

We then picked applicable information on wages from the New York state’s Department of Labor information about New York City wages: http://www.labor.ny.gov/stats/lswage2.asp#47-0000.

Nevertheless, we realized that wages for construction jobs are broken down into categories that do not match exactly the categories of license permits from the DOB­—for instance, the DOB has separate permits for “Master riggers” and “Special riggers” with different permit fees for each, while the wage information for the job of “rigger” in the Department of Labor is under one single category. Furthermore, many of the jobs for which the DOB issues license fees are lumped together in the Department of Labor’s wage data under one same category. We contacted both the Department of Labor and the DOB to get further information and were told that the data we have is the best data we can get. For this reason we believe that in order to draw a sensible comparison between license fees and expected wages we will have to take the fees of those jobs that are lumped together at the Department of Labor and draw an average of how much these workers have to pay for their licenses. As to the question of where the money that the DOB charges for the license fees goes, Everton Harris from the DOB told us on the phone that it “goes to New York City”. We sent him an email asking him to transmit our question to somebody capable of answering it in more detail.

As our sketch shows, we are planning to have two graphs: a scatterplot with “license fees” on the y-axis and “average yearly wage” on the x-axis. We expect that jobs that pay more will have proportionately higher license fees. This graph will allow us to find any outliers that pay disproportionate amounts.

The other image is a line chart of money spent in license fees and renewals over the years for each job. If the amount paid for renewals in each job rises proportionately over time, the gradient of each line should be the same. Nonetheless, it isn’t.

Sources:

Elena Volovelsky
Labor Market Analyst
NYS Department of Labor
212 775 3332

James Brown
Labor Market Analyst
NYS Department of Labor
212 775 3330

Everton Harris
NYC Department of Buildings
212 393 2126
evharris@buildings.nyc.gov

Tweets:
DOB construction job fees. Why are some more expensive than others?

New York’s most important behind-the-scenes jobs and what they have to pay for their permits.

Keith-Willens Oscar Storyboard

Oscar_Storyboard

 

We plan on creating a column chart with drill-down. The first layer will be organized along the X axis by year and the average number of days it takes for an oscar nominated film to leak along the Y axis. Clicking on a column will zoom in to the specifics information from each year. The X axis will change to the names of nominated films. Depending on the difficulty working with the code we could create separate columns by leak method. The Y would change into a measure of days until leak although it would still essentially remain a day counter. This chart could also potentially work as a filterable scatter/bubble chart. The pie chart would show how often each leak method is first. Also depending on fair use, maybe a little oscar guy on the side.

Story Update: We found that more than 60% of the time, the oscar screener is the first leak for nominated films. Research has shown that the average number of days until leak has fluctuated so widely due to the varying levels of enforcement from the MPAA. For several years Oscar screeners were sent with special DVD players that could only be watched in certain places, a certain number of times or not sent at all. However the process seems changes to change randomly.

We contacted Andy Baio, the owner of the data-set who explained that he began the database after reading several press releases by the MPAA discussing the leak of oscar screeners. Baio was surprised by the attention these leaks were receiving because they happen all the time. So he found the data to demonstrate how often leaks happen.

Over the past decade, the Academy of Motion Picture Arts and Sciences – the organization that presides over the Oscars – has been waging war against online piracy.

It is a war that they have been losing.

Since 2003, over 60 percent of the films  sent to Academy members for consideration have appeared on sites like The Pirate Bay weeks, sometimes months, before they become available for sale or  legal stream.

The Motion Picture Association of America has stated that the film industry employs over two million people  and provides $104 billion dollars in wages in the United States.  It also estimates that online piracy costs the film industry more than $20 billion per year.

That number has been called into question by some experts, but there is no denying that the movie industry’s biggest hits have a way of winding up on file-sharing networks.

Hollywood depends on the overwhelming success of its blockbusters to buoy the rest of the industry. But since most of these films end up nominated for Oscars they are usually pirated before official release dates.

We propose creating a visualization that displays how quickly Oscar nominated films have been pirated over the past eleven years.

During our initial examination of the data it appears that the average number of days until the screener leaked has gone through two distinct slides. The averages in 2008 and 2014 were about three weeks until leak compared too two months in 2005 and 2011.

We have also obtained data on how frequently the most pirated films have been available on legal streaming sites like Netflix, Hulu and Amazon Video. The vast majority of frequently pirated films are not available legally leading to the conclusion that piracy may stem from availability rather then cost or malice. While we are not certain if we will include this dataset in our final visualization it does seem like an appropriate conclusion to our data narrative.

Our deadline is set for after the Oscar award ceremony and consequently the end of awards season. This will allow us to gather a complete dataset on film leaks for 2014 as well allow us to piggy back on Oscar related buzz.

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.

Harris/Reyna ARRA Fund Piece

photo copy

 

We envision the layout for our piece to look something like this. One the top left is a chart that shows how much money each NYC Department/agency received in stimulus funding, and how much they have spent. The pink (or whatever color) shows how much of the stimulus funding that department has spent, the black shows how much in stimulus they received in total.

On the bottom left, we have a pie chart that shows a breakdown of the 2014 NYC budget, so that you can compare how well each agency is doing in terms of using their funding vs. how much they’re about to receive from the budget.

On the right side, we want to create two infographics, one that lists the top 5 worst agencies in terms of using their stimulus funding, and one that shows the top 5 best. We would outline which program their funding was meant for and what that program would do.

Our piece would also include a text component as well. This is obviously a very preliminary, rough sketch but we thinks it’s a good start to organizing what the best way is to present our data.

Update on our story:

We have contacted Doug Turetsky and someone over at DYCD who were both confused because our data shows that DYCD hasn’t spent any of its stimulus funding, however both of them told us that DYCD has spent all of that funding. Doug is checking with one of his analysts and getting back to us to figure out what the discrepancy is, as well as the DYCD employee.

We’ve called the other agencies as well and couldn’t get in touch with anyone so we’ll be pursuing that as well as talking to James Parrot.

Festival of Data: Olympic Medal History

http://sochi2014.nytimes.com/results/historic

This interactive, created by the New York Times, gives a detailed overview of the medals won at each Winter Olympics in history. Readers can select a year, country or type of medal to sort through. When you select a certain year and country, you can then click for more information and a pop-out shows the names of the Olympians who won for that country and which medals they won in what events. Each year is displayed as a bubble and the size of the bubble depends on the number of medals earned.  If you click on the year, the countries resort in order of who won the most overall medals. One possible addition that I would like to see is the total number of medals won by each country over the entire history of the winter games, which would be especially interesting for the smaller countries.

Inequality and New York’s subway

This is the link to the data visualization that I commented in class last week: http://www.newyorker.com/sandbox/business/subway.html

It shows graphs for each New York subway line and each graph shows the median household incomes of the populations living near subway stations on that line.

In our class discussion we came to the conclusion that people from New York who visit the URL will most likely click on the subway line that they normally take. We therefore wondered whether other ways of presenting the data would have been more comprehensive for a user interested in inequality in New York City.