A predictive model for frequently viewed tiles in a Web map

1. A predictive model for frequently viewed tiles in a Web map Sterling QuinnMGIS CandidateESRI ArcGIS Server Product Engineer Mark GaheganFaculty Advisor

2. Introduction This project presents a model for predicting high-traffic areas of a Web map Model output indicates where server-side cache of map tiles should be created

3. Project objectives Describe server-side caching of map tiles Describe the need for selective caching Present a predictive model for popular areas of the map Describe ways the model could be used and evaluated

4. Web map optimization and the advent of server-side caching

5. Organizing large maps in manageable “tiles” is not new Large paper map series are indexed in organized grids CGIS, a pioneering GIS, used “frames” to organize data (right)

6. Other techniques for organizing maps in tiles or grid systems Pyramid technique successively generalizes rasters in groups of four cells (right) Quadtree structures index datasets in a hierarchy of quadrants

7. The modern map tile JPG or PNG image Standard square dimensions (256 x 256 or 512 x 512) Stored in large “caches” on the server at multiple scales

8. Server-side caching of map tiles is new Traditional map servers (ArcIMS, WMS) draw the image on the fly Can take a while if the map is complex Cached map tiles give extremely fast performance Tiled maps allow users to retrieve just the needed pieces of the map

9. Advent of tiled maps and server-side caching Microsoft Terra Server an early deployment of massive amounts of cached imagery tiles Google Maps serves cached map tiles with AJAX techniques to create a “seamless” Web mapping experience

12. Caching options

13. Current caching options Current GIS software allows analysts to create tile caches for their own maps ESRI’s ArcGIS Server Mapnik Microsoft MapCruncher

14. Caching can require enormous resources on the server Caches covering big areas at large scales can include millions of tiles Many gigabytes, or even terabytes of storage Days, weeks, or sometimes months to generate Many GIS shops lack resources to maintain large caches

15. Selective caching as a strategy for saving resources Administrator can cache only the areas anticipated to be most visited Remaining areas can be: Added to the cache “on-demand” when first user navigates there Filled with a “Data not available” tile

16. Benefits of selective caching Wise because some tiles (ocean, desert) will rarely, if never, be accessed Saves time Saves disk space

17. Implications of selective caching Requires an admission that some areas are more important than others Poses challenge of predicting popular areas before the map is released

18. The need for a predictive model

19. Project presents a predictive model for where to pre-cache tiles “Which places are most interesting?” Inputs are datasets readily available to GIS analyst Output vector features a template for where to pre-cache tiles

20. Purpose of the model Help majority of users see a fast Web map while minimizing cache creation time and storage space

21. Not a descriptive model Descriptive model shows where users have already viewed Microsoft Hotmap good example of a descriptive tool (right) Descriptive models useful for deriving and validating predictive models

22. Advantages of a predictive model Doesn’t require the map to be deployed already Can include fixed and varying geographic phenomena Has applications far beyond map caching

23. Proposed methods

24. Study area and conditions Model predicts frequently viewed places for a general base map May create models for thematic maps if time allows Study area of California

25. Input datasets Populated / developed areas Road networks Coastlines Points of interest

26. Populated / developed areas Human Influence Index grid by the Socioeconomic Data and Applications Center (SEDAC) at Columbia University Model selects all grid cells over a certain value

27. Road networks Major roads buffered by a given distance All roads within national parks, monuments, historical sites, and recreation areas, buffered by a given distance

28. Coastlines All coastlines buffered by a given distance (wider buffer on inland side)

29. Points of interest Set of 60 interesting points chosen by model author Mountain peaks Theme parks Sports arenas Etc. Represents a flexible layer that could be tailored to local needs

30. Deriving the output Merge all layers together Clip to California outline (with small buffer) Remove small holes and polygons Dissolve into one multipart feature Simplify to remove unneeded vertices

31. Using the model output Output a vector dataset that can be used as a template for creating cached tiles Compare model output area with total area to understand percent coverage Compare model output with actual usage over time Refine if necessary

32. Limitations Models of world scope should account for Internet connectivity Input datasets have varying collection dates Input datasets vary in resolution and precision Maps with many scales might require multiple iterations and variations of the model

33. Questions?

34. References De Cola, L. & Montagne, N. (1993). The PYRAMID system for multiscale raster analysis. Computers & Geosciences, 19(10), 1393 – 1404. Tomlinson, R. L., Calkins, H. W., & Marble, D. F. (1976). Computer Handling of Geographical Data. Paris: Unesco.