1. Urban Characteristics As discovered from the Twitter open API www.cs.odu.edu/~rlewis/urban_characteristics

2. 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

3. 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

4. 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

5. 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 ┌ 100 2 π ┐ cells  Results in 31,416 separate calls to the API (current limitation imposed is 150 calls per hour)

6. 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

7. Circumventing Twitter Open API Limitation Quad-tree splitting (continued) ▫Application

8. Quad-Tree Splitting

9. Aggregation Model

10. Dispersion Model