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.

Where its at - the news that is...

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:

  1. New York
  2. Washington
  3. Chicago
  4. Florida
  5. California
  6. Los Angeles
  7. Texas
  8. Miami
  9. Hollywood
  10. Arizona

where its at in the usa

Aside from the proof thatFlorida 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



  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.

    1. 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 )

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