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A predictive model for frequently viewed tiles in a Web map Sterling Quinn MGIS Candidate ESRI ArcGIS Server Product Engineer Mark Gahegan Faculty Advisor.

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Presentation on theme: "A predictive model for frequently viewed tiles in a Web map Sterling Quinn MGIS Candidate ESRI ArcGIS Server Product Engineer Mark Gahegan Faculty Advisor."— Presentation transcript:

1 A predictive model for frequently viewed tiles in a Web map Sterling Quinn MGIS Candidate ESRI ArcGIS Server Product Engineer Mark Gahegan Faculty Advisor

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

3 Project objectives Describe server-side caching of map tiles Describe server-side caching of map tiles Describe the need for selective caching Describe the need for selective caching Present a predictive model for popular areas of the map Present a predictive model for popular areas of the map Describe ways the model could be used and evaluated 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 Large paper map series are indexed in organized grids CGIS, a pioneering GIS, used frames to organize data (right) CGIS, a pioneering GIS, used frames to organize data (right) From Tomlinson, Calkins, & Marble, 1976, p. 56.

6 Other techniques for organizing maps in tiles or grid systems Pyramid technique successively generalizes rasters in groups of four cells (right) Pyramid technique successively generalizes rasters in groups of four cells (right) Quadtree structures index datasets in a hierarchy of quadrants Quadtree structures index datasets in a hierarchy of quadrants From De Cola & Montagne, 1993, p

7 The modern map tile JPG or PNG image JPG or PNG image Standard square dimensions (256 x 256 or 512 x 512) Standard square dimensions (256 x 256 or 512 x 512) Stored in large caches on the server at multiple scales 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 Traditional map servers (ArcIMS, WMS) draw the image on the fly Can take a while if the map is complex Can take a while if the map is complex Cached map tiles give extremely fast performance Cached map tiles give extremely fast performance Tiled maps allow users to retrieve just the needed pieces of the map 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 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 Google Maps serves cached map tiles with AJAX techniques to create a seamless Web mapping experience

10 Tiles in Google Maps quickly retrieved as you navigate Tiles in Google Maps quickly retrieved as you navigate From Google Maps: 1 2

11 Many sites have followed Googles pattern Many sites have followed Googles pattern MapQuest: Yahoo Maps: Microsoft Virtual Earth:

12 Caching options

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

14 Caching can require enormous resources on the server Caches covering big areas at large scales can include millions of tiles Caches covering big areas at large scales can include millions of tiles Many gigabytes, or even terabytes of storage Many gigabytes, or even terabytes of storage Days, weeks, or sometimes months to generate Days, weeks, or sometimes months to generate Many GIS shops lack resources to maintain large caches 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 Administrator can cache only the areas anticipated to be most visited Remaining areas can be: Remaining areas can be: Added to the cache on-demand when first user navigates there Added to the cache on-demand when first user navigates there Filled with a Data not available tile Filled with a Data not available tile

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

17 Implications of selective caching Requires an admission that some areas are more important than others Requires an admission that some areas are more important than others Poses challenge of predicting popular areas before the map is released 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? Which places are most interesting? Inputs are datasets readily available to GIS analyst Inputs are datasets readily available to GIS analyst Output vector features a template for where to pre-cache tiles 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 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 Descriptive model shows where users have already viewed Microsoft Hotmap good example of a descriptive tool (right) Microsoft Hotmap good example of a descriptive tool (right) Microsoft Hotmap Microsoft Hotmap Descriptive models useful for deriving and validating predictive models Descriptive models useful for deriving and validating predictive models From Microsoft Hotmap

22 Advantages of a predictive model Doesnt require the map to be deployed already Doesnt require the map to be deployed already Can include fixed and varying geographic phenomena Can include fixed and varying geographic phenomena Has applications far beyond map caching Has applications far beyond map caching

23 Proposed methods

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

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

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

27 Road networks Major roads buffered by a given distance Major roads buffered by a given distance All roads within national parks, monuments, historical sites, and recreation areas, 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) 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 Set of 60 interesting points chosen by model author Mountain peaks Mountain peaks Theme parks Theme parks Sports arenas Sports arenas Etc. Etc. Represents a flexible layer that could be tailored to local needs Represents a flexible layer that could be tailored to local needs

30 Deriving the output Merge all layers together Merge all layers together Clip to California outline (with small buffer) Clip to California outline (with small buffer) Remove small holes and polygons Remove small holes and polygons Dissolve into one multipart feature Dissolve into one multipart feature Simplify to remove unneeded vertices 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 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 area with total area to understand percent coverage Compare model output with actual usage over time Compare model output with actual usage over time Refine if necessary Refine if necessary

32 Limitations Models of world scope should account for Internet connectivity Models of world scope should account for Internet connectivity Input datasets have varying collection dates Input datasets have varying collection dates Input datasets vary in resolution and precision Input datasets vary in resolution and precision Maps with many scales might require multiple iterations and variations of the model 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 – De Cola, L. & Montagne, N. (1993). The PYRAMID system for multiscale raster analysis. Computers & Geosciences, 19(10), 1393 – Tomlinson, R. L., Calkins, H. W., & Marble, D. F. (1976). Computer Handling of Geographical Data. Paris: Unesco. Tomlinson, R. L., Calkins, H. W., & Marble, D. F. (1976). Computer Handling of Geographical Data. Paris: Unesco.


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