1. Spatial Variation in Search Engine Queries Lars Backstrom, Jon Kleinberg, Ravi Kumar and Jasmine Novak

2. Introduction Information is becoming increasingly geographic as it becomes easier to geotag all forms of data. What sorts of questions can we answer with this geographic data? –Query logs as case study here Data is noisy. Is there enough signal? How can we extract it. Simple methods aren’t quite good enough, we need a model of the data.

3. Introduction Many topics have geographic focus –Sports, airlines, utility companies, attractions Our goal is to identify and characterize these topics –Find the center of geographic focus for a topic –Determine if a topic is tightly concentrated or spread diffusely geographically Use Yahoo! query logs to do this –Geolocation of queries based on IP address

4. Red Sox

5. Bell South

6. Comcast.com

7. Grand Canyon National Park

8. Outline Probabilistic, generative model of queries Results and evaluation Adding temporal information to the model Modeling more complex geographic query patterns Extracting the most distinctive queries from a location

9. Probabilistic Model Consider some query term t –e.g. ‘red sox’ For each location x, a query coming from x has probability p x of containing t Our basic model focuses on term with a center “hot-spot” cell z. –Probability highest at z –p x is a decreasing function of ||x-z|| We pick a simple family of functions: –A query coming from x at a distance d from the term’s center has probability p x = C d -α –Ranges from non-local (α = 0) to extremely local (large α)

10. Algorithm Maximum likelihood approach allows us to evaluate a choice of center, C and α Simple algorithm finds parameters which maximize likelihood –For a given center, likelihood is unimodal and simple search algorithms find optimal C and α –Consider all centers on a course mesh, optimize C and α for each center –Find best center, consider finer mesh

11. α = 1.257

12. α = 0.931

13. α = 0.690

14. Comcast.com α = 0.24

15. More Results (newspapers) Newspaperα The Wall Street Journal0.11327 USA Today0.263173 The New York Times0.304889 New York Post0.459145 The Daily News0.601810 Washington Post0.719161 … Chicago Sun Times1.165482 The Boston Globe1.171179 The Arizona Republic1.284957 Dallas Morning News1.286526 Houston Chronicle1.289576 Star Tribune (Minneapolis)1.337356 Term centers land correctly Small α indicates nationwide appeal Large α indicates local paper

16. More Results Schoolα Harvard0.386832 Caltech0.423631 Columbia0.441880 MIT0.457628 Princeton0.497590 Yale0.514267 Cornell0.558996 Stanford0.627069 U. Penn0.729556 Duke0.741114 U. Chicago1.097012 Cityα New York0.396527 Chicago0.528589 Phoenix0.551841 Dallas0.588299 Houston0.608562 Los Angeles0.615746 San Antonio0.763223 Philadelphia0.783850 Detroit0.786158 San Jose0.850962

17. Evaluation Consider terms with natural ‘correct’ centers –Baseball teams –Large US Cities We compare with three other ways to find center –Center of gravity –Median –Most likely grid cell Compute baseline rate for all queries Compute likelihood of observations at each 0.1x0.1 grid cell Pick cell with lowest likelihood of being from baseline model

18. Baseball Teams and Cities Our algorithm outperforms mean and median Simpler likelihood method does better on baseball teams –Our model must fit all nationwide data –Makes it less exact for short distances

19. Temporal Extension We observe that the locality of some queries changes over time –Query centers may move –Query dispersion may change (usually becoming less local) We examine a sequence of 24 hour time slices, offset at one hour from each other –24 hours gives us enough data –Mitigates diurnal variation, as each slice contains all 24 hours

20. Hurricane Dean Biggest hurricane of 2007 Computed optimal parameters for each time slice Added smoothing term –Cost of moving from A to B in consecutive time slices γ|A-B| 2 Center tracks hurricane, alpha decreases as storm hits nationwide news

21. Multiple Centers We extend our algorithm to locate multiple centers, each with its own C and α –Locations use the highest probability from any center –To optimize: Start with K random centers, optimize with 1-center algorithm Assign each point to the center giving it highest probability Re-optimize each center for only the points assigned to it Not all queries fit the one- center model –‘Washington’ may mean the city of the state –‘Cardinals’ might mean the football team, the baseball team, or the bird –Airlines have multiple hubs

22. United Airlines

23. Spheres of influence

24. Spheres of Influence Each baseball team assigned a color A team with N queries in a cell gets N C votes for its color Map generated be taking weighted average of colors

25. Distinctive Queries For each term and location –Find baseline rate p of term over entire map –Location has t total queries, s of them with term –Probability given baseline rate is: p s (1-p) t-s For each location, we find the highest deviation from the baseline rate, as measured by the baseline probability

27. Conclusions and Future Work Large-scale query log data, combined with IP location contains a wealth of geo- information Combining geographic with temporal –Spread of ideas –Long-term trends Using spatial data to learn more about regions –i.e. urban vs. rural