www.cs.odu.edu/~rlewis/urban_characteristics
Urban Characteristics
As discovered from the Twitter
open API
Open Data Gathering
• Ability to request posts from a region
▫ Provide center point and radius (≤ 1km)
▫ Returns ≤ 1,500 posts up to one week old
▫ Contains
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User ID
Time
Location
Posted message
Socio-Psychological Discoveries
• Focus is on the content of the message
• Places emphasis on link structure of followers
(subscribers) of the post
• Gather trends within posted messages to cull
tastes/preferences
Socio-Migration Discoveries
• Focus is on the time and location of the tweet
• Cull regional characteristic patterns from
movement histories
1. Monitor updates from micro-blogging site in
relation to geography
2. Clustering posts as a step to measure movement
patterns
3. Extract characteristic patterns
Circumventing Twitter Open API Limitation
• The naïve way
▫ Method
 Uniformly split a region into 1km grid cells
 Call API for each cell
▫ Application
 Circular area of 100km radius (124 miles in diameter)
 Need ┌1002π┐ cells
 Results in 31,416 separate calls to the API (current limitation
imposed is 150 calls per hour)
Circumventing Twitter Open API Limitation
• Quad-tree splitting
▫ Method
 Recursively split the region into four rectangular cells of equal
size
 Stop splitting when
 Cell is larger than the minimum radius permitted by the API (1km)
 Number of posts in a cell is < 1,500
Circumventing Twitter Open API Limitation
• Quad-tree splitting (continued)
▫ Application
Quad-Tree Splitting
Aggregation Model
Dispersion Model