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