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Dynamic Tiled Map Services: Supporting Query-Based
Visualization of Large-Scale Raster Geospatial Data
Jianting Zhang12, Simin You2
City College1 & Graduate Center2 of
The City University of New York
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Outline•Motivation and Introduction•Background and Related Work •The Proposed Solution
•System Architecture
•Query Processing Server
•Tile Image Generation and Caching
•Visualization Client
•Experiments and Evaluation•Conclusion and Future Work
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Introduction/Motivation
3
If you load your own data in Google Earth,
Wouldn’t it be nicer if you can query your data and highlight the query results?
In addition to simple display, zoom in/out, pan
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Global 30s Precipitation Data from WorldClim (Interpolated 1950-2000)
Coloring Schema:Green: 0 mmRed: 100 mmLinear Interpolation
Undergraduate Project: Generate Dynamic KML Files for Interactive Visualization in Google Earth (C. Dasrat/CCNY)
Jan July
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Introduction/Motivation• Task: Show the regions where precipitation amount in January is
between [p1,p2). • Intuitive Solution 1: pre-generate all query results for all possible
p1 and p2 combinations and then publish them as Tiled WMS services – virtually impossible
• Intuitive Solution 2: output the query results and then publish them as Tiled WMS services – better, but still suffer from slow start– Reading/examining/outputting a raster of 43200*21600 cells takes minutes– Generating image tiles take tens of minutes or even longer (TileCache)– This is for each of every query
• Our Solution: Index raster data, perform the query in main memory, dynamically generate tiled images on-demand based on user’s current view and cache the tiled images as necessary
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Background & Related Work
• Spectral, spatial and temporal resolutions of geospatial raster data are getting increasingly finer larger data volumes– The next generation GOES-R satellite will provide
global coverage at the 0.5-2 km resolution every 5 minutes (16 bands)
– Large-scale model simulation (e.g. WRF)
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Background & Related Work
• Manually examine all the data through visual display is not possible anymore– Human eyes can only effectively distinguish a
limited number of colors at a time – Studies show that screen resolution beyond 4000 by
4000 pixels is not effective
• Query data and highlight results (Region of Interests) for further analysis become more preferable
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Background & Related Work
• Tiled map service techniques: – Google Map/Earth, Microsoft Bing Map
– ArcGIS, MapServer/TileCache
– They are used for static images, not dynamic query results
• Query Driven Visual Exploration for scientific data • Spatial Databases
– Vector data (R-Tree/Quadtree indexing)
– Raster data
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System Architecture
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Indexing for Query Processing
BMMQ-Tree (SSDBM’10)
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Tile Based Query
• Step 1: locate the quadtree node representing the tile being requested (x,y,L) that satisfy query criteria [V1,V2). – Divide x and y by 2 L times– Use the reverse order of the reminders to travel the tree – Stop the process if any node along the travel path does not
satisfy the query criteria
• Step2: starting from the located quadtree node– Traversal k levels of the sub-tree at the node (tile size s=2k )– Discard tree nodes that do not satisfy the query criteria– Return all the qualified nodes with top-left coordinates and level
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Querying with value range [1,3] under tile (0,1,1)
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Tile Image Generation and Caching
• Assuming the tile being requested is (x,y,L), the tile size is s=2k and the quadtree nodes are returned in the form of (r,c,l) triples, we can simply draw a square starting at (r,c) with length 2k-(l-L)
• Since l<=k+L, the length is guaranteed to be equal or larger than 1 pixel.
• When k+L is larger than the maximum level of the quadtree, i.e., k + L >max_quad_lev, digital zoom-in will be applied (does not convey additional information)
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Tile Image Generation and Caching
• Java ImageIO package– Easier to use than GD package that MapServer relies
on
• Part of TDS source tree is extracted and assembled for image caching– TDS: THREDDS Data Server (UCAR Unidata)– Designed for OPeNDAP and OGC WCS– We reuse its caching sub-system through code
reorganization– Significant development saving on coding and testing
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Visualization Client
• ArcGIS Flex API– Rich Internet Application (RIA) framework– Built-in APIs to visualize statically cached titled map – Allows to control rendering the canvas in a Web
browser at the pixels level– Poor rendering performance when the number of
squares goes beyond thousands
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Visualization Client
private var _baseURL:String = "http://134.74.112.202:8080/pngsvr/PNGServlet?";
override protected function getTileURL(level:Number, row:Number, col:Number): URLRequest{
var url:String=_baseURL+"minB="+_minB+"&maxB="+_maxB+"&level="+level+"&row="+row+"&col="+col;return new URLRequest(url);
}
• Extending ArcGIS Flex API TiledMapServiceLayer class to visualize dynamically tiled images in ArcGIS Flex applications
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Experiments and Evaluation
• Data: WorldClim January Precipitation Data at 30s resolution (43200*21600)
• Value range [0,1003]• Number of bins=32 • Quadtree level=16• Tile image size =256*256 (k=8)• Query processing server: Dell T5400• Tile image/Web server: Lenovo T400s
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Experiments and Evaluation
Estimating End-to-End time
• Assume available network bandwidth=300k Bps TT=10ms
• Assume client display area 1024*102416 tiles
• Assume no server/client side caching (cold start)
• Estimated time: (QT+GT+TT)*16 = (50+10+10)*16=1120 ms
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Summary
•Online demo: http://134.74.112.202/comgeo/testoverlay.html
•We have designed algorithms to efficiently perform tile-based queries on quadtrees and to convert quadrant-based query results into tiled images.
•An end-to-end prototype has been developed to demonstrate the feasibility of the proposed dynamic tiled map services approach
•Experiments results have showed that the prototype system achieves an end-to-end performance in the order of sub-second for 1024*1024 pixels display area using 16 tiles.
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Additional Information
• GPU-based indexing– Nvidia Quadro FX3700 GPU card with
112 cores and 512M device memory
– Raster size is limited to 4096*4096 due to device memory constraints 11*5 blocks
– 20X speedup (8.7s vs. 0.4s)
– We expect to index the same global data on SGI Octane III 2-node mini-cluster with 4 GPU cards in about 1-5 seconds after fine-tuning our current codebase real time indexing