Where it’s at April 2019
One of the things that my bots are tracking, in my data collection / data analysis efforts, are the places where news is happening.
I am tracking mainstream and alternative news sources as well as keeping a good mix of left and right news sources. I’m calling the results: Where It’s At.
I haven’t documented this little side hustle since February, however I’ve sent my bots out on the reg. I am starting to get enough data to chart on maps.
I am also starting to get a good feel of where the News Entertainment Industrial Complex thinks is newsworthy.
While Venezuela continues to dominate mainstream news headlines, the majority of U.S. news is domestic and tends to be reported about:
- New York
- Washington
- Chicago
- Florida
- California
- Los Angeles
- Texas
- Miami
- Hollywood
- Arizona
Aside from the proof that “Florida Man“ is a thing, I am sure that you can see one of the problems that I am having. California, Los Angles, Hollywood – these are obviously different “places” but the latter two are also part of the former as well.
I’ve settled on tracking “place names” so, country, city, continent are all fair game.
This introduces a number of problems ( such as charting on a map… ) and may not be the right “data science-y” way to do things. It is however how I have chosen to proceed for now.
There is another issue that I’ve encountered that I did not anticipate. The North – South problem. As you’ll see below Carolina, Korea, and even Dakota make the list. I’m working on this one…
Where It’s At April 2019
| venezuela | 135 |
| new york | 111.69 |
| china | 110.69 |
| washington | 110.44 |
| chicago | 101.05 |
| florida | 96.66 |
| california | 94.12 |
| new england | 81.45 |
| america | 79.55 |
| los angeles | 78.1 |
| vatican | 67.82 |
| mexico | 46.55 |
| canada | 46.45 |
| texas | 43 |
| north korea | 42.35 |
| miami | 38 |
| vietnam | 31.45 |
| europe | 24.33 |
| korea | 24.33 |
| united kingdom | 23.67 |
| nigeria | 22.58 |
| russia | 19.67 |
| india | 18 |
| france | 17.67 |
| saudi arabia | 17.33 |
| syria | 17.33 |
| pakistan | 15 |
| london | 12 |
| eu | 11.67 |
| iran | 11.33 |
| greece | 10.33 |
| germany | 10 |
| colombia | 9 |
| afghanistan | 8.33 |
| carolina | 7.67 |
| hollywood | 7.33 |
| kashmir | 7 |
| paris | 7 |
| ukraine | 6.67 |
| africa | 6 |
| italy | 6 |
| spain | 6 |
| arizona | 5.67 |
| cuba | 5.33 |
| sweden | 5.33 |
| toronto | 5.33 |
| georgia | 5 |
| michigan | 5 |
| new zealand | 5 |
| rwanda | 5 |
| el salvador | 4.67 |
| japan | 4.67 |
| puerto rico | 4.67 |
| australia | 4.33 |
| las vegas | 4.33 |
| iowa | 4 |
| beijing | 3.67 |
| maryland | 3.67 |
| bangladesh | 3.33 |
| gaza | 3.33 |
| mozambique | 3.33 |
| tennessee | 3.33 |
| minnesota | 3 |
| virginia | 3 |
| hungary | 2.67 |
| kentucky | 2.67 |
| yellowstone | 2.67 |
| atlanta | 2.33 |
| nova scotia | 2.33 |
| philippines | 2.33 |
| turkey | 2.33 |
| utah | 2.33 |
| palm springs | 2.32 |
| beverly hills | 2.07 |
| el paso | 2 |
| philadelphia | 2 |
| singapore | 2 |
| vancouver | 2 |
| guatemala | 1.67 |
| laredo | 1.67 |
| new jersey | 1.67 |
| nyc | 1.67 |
| pittsburgh | 1.67 |
| tehran | 1.67 |
| baltimore | 1.33 |
| boston | 1.33 |
| caribbean | 1.33 |
| dakota | 1.33 |
| hong kong | 1.33 |
| jerusalem | 1.33 |
| pennsylvania | 1.33 |
| tel aviv | 1.33 |
| tokyo | 1.33 |
| charlottesville | 1 |
| cleveland | 1 |
| colorado | 1 |
| dallas | 1 |
| detroit | 1 |
| egypt | 1 |
| ethiopia | 1 |
Ok, so I know how I want to handle the “what is a location” issue. If the result gives us a wildly inaccurate result on the map ( after being geocoded ) then it’s out.
For instance New England is gone as it maps to somewhere in the UK but Africa plots the arrow somewhere on the continent so its cool.
I know this is messy, but I have an idea about how to work it.
This also solves another issue. If 2 places share an “address” then they are the same ( NYC and New York City ) and they are not the same if they don’t ( EU and Europe )