Info

JOUR 72312

Posts by Jack D'Isidoro

As spring blossoms in the city, New Yorkers take to their bicycles again to get about the city. With increased ridership, the city’s existing bicycle infrastructure is put under closer scrutiny. Since 2000, bicycle commuting has quadrupled, which has been tracked since 1985. Mayor Bill de Blasio’s Vision Zero campaign has promised the constructions of 50 miles of bike lanes every year.

The Belmont neighborhood in the Bronx has been underserved by this initiative. Local business owners are spearheading a plan to create more bike infrastructure, but have met resistance from unprogressive local community leaders. According to the DOT, bike parking increases business.

Here is what it looks like.

I want to scrape some data to determine the miles of bike lanes per residents in each borough to calculate a metric of accessibility. I also want to give the story context through the Belmont neighborhood.

Contacts:

Councilman Ritchie Torres

Frank Franz, Belmont BID President

Ivine Galarza,  Community Board 6 Manager

Brian Zumhagen, Transportation Alternatives

Gloria Chin, DOT

The New York Times’ The Upshot published an interactive graph representing a model which predicts an American’s lifetime voting habits based on their birth year. The study found that a person’s most formative years were between the ages of 14 and 24, and that political events and presidential approval ratings during this timeframe were strongly influential on a person’s voting behavior.

The study excluded African Americans because they have historically voted predominantly Democratic. It also excluded Hispanics, particularly recent immigrants, because their population numbers have increased over the years.

The model also noted that once we reach age 40, we are three times less as likely to change our party allegiance given current events than at age 18.

One of the visualization’s strengths is that  it is able to show how different age demographics voted in the 2012–it was mostly people in their twenties and early sixties, which demonstrates Obama’s ability to galvanize youth voters. So, given the model’s prediction, Obama voters in their twenties are more likely to remain Democrat than, say, their parents.

Another of its strengths is that it demonstrates that certain generations essentially “made up their minds.” For example, the WWII generation, influenced by Eisenhower in the 1950’s, remained primarily Republican for the rest of their lives. Conversely, baby-boomers, who grew up in the 1960’s, remained Democrats.

In this same vein, I think the visualization falls short because it is unable to exhibit or quantify historical moments that may have had a great influence on a specific generation. At a minimum, it could have displayed, in an additional line, the president listed below’s approval rating and/or party affiliation.

Historical context makes the visualization compelling, but it’s difficult to import that information in an objective way.

On an interactivity level, the visualization is incredibly easy and appealing. I’m sure most people, like myself, scrolled the bar to their own date of birth to compare themselves with their generational compatriots.

I would assume that one data set the study may have used was party records, since registering for a party is public record.