Unarguably the most difficult part of this process was the in class instillation of the Landsat software. That is to say that using this landsat …software I guess you would call it? …is incredibly easy and dare I say fun to use.

For the assignment, I just wanted to make sure I could use Landsat efficiently and effectively while understanding what was actually happening. My first attempt at using it, I decided to pull in my exact location in Brooklyn. I started by going to www.latlong.net and finding my exact location:

I took the screenshot because I was curious to see just how precise the landsat imagery was going to be. I obviously wasn’t expecting the specificity of a street level view but I was anticipating I could make out some geographical features that would lead me to believe: “Oh, this Brooklyn.”

I took the first 6 digit of the lat and long, and input that into my terminal. After the JSON information loaded, I chose the scene ID that seemed to have the most minimal amount of cloud coverage, used that to download and process some bands and got this image:

This does…not look like Brooklyn to me. Doesn’t look like any area that I know in NY. I decided I’d try it again with another familiar image: my home in MD. This time however, rather than taking just 6 digits from lat and long, I took the entire lat long as found by the same aforementioned website.

Here is my before image:

 

And here is the processed image when the exact lat/long was placed in the landsat software:

This, to me, is a much more recognizable image.

The purpose of this to me was to used locations in which I am specific to see how granular the satellite imagery will get. Now, I have some context as to how most effectively I can use Landsat.

EDIT: So after the first class, I knew essentially what I was interested in doing for a final project. A little context: My dream is to work for Major League Baseball. I’m currently a freelancer/independent contractor for them in their QA department – I place baseball video games all day – and hope to continue climbing the ladders they provide for me. As a result, a fair amount of the work I produce at ITP is baseball related. Baseball provides me with a context that makes learning new tech just a little bit easier.

With that said, for this assignment, I want to see if there is any correlation between placement of a Major League ballpark and surrounding poverty levels. How does a stadium change the community in which it’s placed? I know this is a bigger project than a final but I figure for the final I can bring in poverty levels of all the areas in which a stadium is located and compare them to poverty levels PRE the building of that stadium. I can also do something where ticket sales are assessed for example: is there a correlation between tickets sales, poverty levels and placement of a stadium.

Below is just information I’ve begun to gather about the poverty rate in each county where the ballpark is located.

Is it better to look at avg attendance per game or total?

Source for all information: US Census Bureau – http://www.census.gov/quickfacts/table/PST045216/00

Angel Stadium of Anaheim in Anaheim, California – Poverty Rate – 16.5%. Total 3,016,142 AVG 37,236 (7th)

AT&T Park in San Francisco – Poverty Rate – 13.2% Total – 3,365,256. AVG – 41,546 (4th)

Busch Stadum – St.Louis, Missouri – 27.1% Total –  3,444,490. AVG – 42,524 (2nd)

Chase Field – Phoenix, Arizona – 23.1% Total 2,036,216 AVG 25,138 (21st)

Citi Field – Queens, NY – 13.9% Total 2,789,602 AVG 34,870 (9th)

Citizens Bank – Philadelphia, PA – 26.4% Total 1,915,144 AVG 23,643 (24th)

Comerica Park – Detroit, MI – 40.3% Total 2,493,859 AVG 31,173 (13th)

Coors Field – Denver, Colorado – 17.3% Total 2,602,524. AVG 32,129 (11th)

Dodger Stadium – Los Angeles, CA – 22.1%. Total – 3,703,312. AVG – 45,719 (1st)

Fenway Park – Boston, Massachusetts – 21.5% Total – 2,955,434. AVG – 36,486 (8th)

Globe Life Park in Arlington – Arlington, TX – 17.5% Total – 2,710,402 AVG 33,461(10th)

Great American Ballpark – Cincinnati, OH – 30.5% Total – 1,894,085 AVG 23,383 (25th)

Guaranteed Rate Field – Chicago, Illinois – 22.3% Total 1,746,293 AVG 21,828 (26th)

Kaufmann Stadium – Kansas City, MI – 19.0% Total 2,557,712 AVG 31,576 (12th)

Marlins Park – Miami, FL – 28.3%Total 1,712,417 AVG 21,405 (27th)

Miller Park – Milwaukee, WI – 28.7% Total 2,314,614 AVG 28,575 (16th)

Minute Maid Park – Houston, TX – 22.5% Total 2,306,623 AVG 28,476 (17th)

Nationals Park – Washington D.C – 18.0% Total 2,481,938 AVG 30,641 (14th)

Oakland Coliseum – Oakland, CA – 20.4% Total 1,521,506 AVG 18,784 (29th)

Oriole Park at Camden Yards – 23.7% Total 2,172,344 AVG 26,819 (20th)

Petco Park – San Diego, CA – 15.4% Total 2,351,426 AVG 30,641 (15th)

PNC Park, Pittsburgh, PA – 22.9% Total 2,249, 021 AVG 28,112 (18th)

Progressive Field – Cleveland, OH – 36.2% Total – 1,591,667 AVG 19,650 (28th)

Rogers Centre – Toronto, ON – 13.1 % Total – 3,392,299. AVG – 41,880 (3rd)

SunTrust Park – Cumberland, GA – 11.4%** new stadium, only county data available NA

Safeco Field – Seattle, WA – 13.5% Total 2,267,928 AVG 27,999 (19th)

Target Field – Minneapolis, MN  -21.9% Total 1,963,912 AVG 24,245 (23rd)

Tropicana Field – St. Petersburg, FL – 17.2% Total 1,286,163 AVG 15,878 (30th)

Wrigley Field – Chicago, Illinois – 22.3% Total 3,232,420 AVG – 39,906 (5th)

Yankee Stadium, Bronx, NY – 30.3% Total 3,063,405 AVG – 37,819 (6th)