Urban CharacteristicsAs discovered from the Twitter open API
www.cs.odu.edu/~rlewis/urban_characteristics
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
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 histories1. Monitor updates from micro-blogging site
in relation to geography2. Clustering posts as a step to measure
movement patterns3. 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
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