Geographical data in Twitter JSON

Analyzing Social Media Data in Python

Alex Hanna

Computational Social Scientist

Locations in Twitter text

Tweet with location text

User-defined location

print(tweet['user']['location'])

Bay Area

place JSON

Bounding box illustration

print(tweet['place'])

{'attributes': {},
 'bounding_box': {'coordinates': 
  [[[-80.47611, 37.185195],
    [-80.47611, 37.273387],
    [-80.381618, 37.273387],
    [-80.381618, 37.185195]]],
  'type': 'Polygon'},
 'country': 'United States',
 'country_code': 'US',
 'full_name': 'Blacksburg, VA',
 'name': 'Blacksburg',
 'place_type': 'city',
 ...}

Calculating the centroid

Bounding box illustration

coordinates = [
    [-80.47611, 37.185195],
    [-80.47611, 37.273387],
    [-80.381618, 37.273387],
    [-80.381618, 37.185195]]

longs = np.unique( [x[0] for x 
    in coordinates] )
lats  = np.unique( [x[1] for x 
    in coordinates] )

central_long = np.sum(longs) / 2
central_lat  = np.sum(lats) / 2

coordinates JSON

Single coordinate place

print(tweet['coordinates'])

{'type': 'Point', 
'coordinates': [-72.2833, 
                 21.7833]}

Let's practice!

Analyzing Social Media Data in Python