Urban Characteristics

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

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 • Monitor updates from micro-blogging site in relation to geography • Clustering posts as a step to measure movement patterns • 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 ┌1002π┐ 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