This document proposes using SQL Server to improve vector display through point clustering. It discusses current limitations of displaying large numbers of vectors in web mapping environments. The proposed solution involves carving the display area into a grid, drawing single points and clustering other points into centroids. Thresholds determine when to draw all, cluster some or take no action. Functions calculate grid cell sizes and return polygons for clustering. This clusters points to overcome JavaScript limitations while still allowing attribute queries.

1. Leveraging SQL Server
to Improve Vector
Display through Point
Clustering

2. The Problem…
 Due to Java Script limitation,
Large Numbers of Vectors don’t
draw in Web-Mapping
Environments.
 Cutoff is ~ 300-500 Features

3. Current ways to solve…
1. Use Silverlight or Flash
 Increases capacity roughly an order of magnitude, but similar
limitation exists.
2. Show Pictures, Query Features
 Has the capability to show and query ALL features for a
pleasant user experience, but takes time to build tile cash (the
pretty pictures).
 However, Tile Caches are Large and are, basically, redundant
storage.
3. Use a WMS to feed ‘pictures’ dynamically and then use
a separate, spatial query for feature attributes.
 Extra ‘moving’ parts in the solution.
 Possibly extra cost in licenses.

4. Why Invent a ‘Better’
Solution???
 Original Design…
 Texas Hydrologic Information System (TexasHIS)
 www.waterdatafortexas.org
 Client desires immediate access to vector data
and attributes
 Data Characteristics
 Lots of tightly packed ‘Sites’ ~ 300K+
 data with gobs of related attribute data ~
5,000,000+ related records!

5. Factors Affecting a Good ‘Clustering’
 The Data
Solution…
 Density
 Distribution
 The Scale
 Large – Basically Draw everything
 ‘Medium’ – Draw some, Cluster some – Lots of Code and
Logic!
 Small – Basically Cluster everything
 Logic
 Thresholds/Limits of when to Cluster vs. When to Draw
 How to Carve the display to deal with Data Distribution
 Complex Algorithms to determine the optimal number of
features to draw.

6.  Create a ‘smart’ solution that…
 Isn’t affected by the java script limit
 Only queries the database one time
(other solutions can query the
database for image creation and
attribute retrieval.
 Is still capable of retrieving vector
attributes for single features

7. Solution Basics…
1. Drawing Thresholds
 When to Draw everything
 When to Cluster
 When to take no action
2. Carving The Display
 At first glance, a 16 x 16 ‘Grid’ would be
best. It would yield 256 Cluster ‘Features’
3. Clustering Logic
 Draw the Singles
 Cluster Everything Else

8. Solution Basics: Thresholds…
1. Drawing Thresholds
 Draw Everything – if the total number
of Points to ‘cluster’ is below 500
 Cluster – If there are 500 (Lower Limit)
to 5000 (Upper Limit)
 Above the Upper Limit – Take no
action. The Clustering Query becomes
too expensive. So much so that user
experience is drastically diminished.

9. Solution Basics: Carving up the Display
and Applying the Logic Rules…
 Spatial Indexes are Similar in concept
 16 x 16 creates 256 Cells – Near the upper limit of
our display threshold
 Polygons with more than 1 Site are ‘Cluster
Candidates’. The Centroid of the polygon will
represent the cluster spatially.
 Threshold values are variables so that they are
easily ‘tuned’ in a series of runs.

10. Solution Basics: How it works…
 The Envelope of the Display is passed as a parameter
 First Function, a Scalar-Valued Function, determines
how ‘big’ the cells will be in the X and Y direction –
Returns a comma separated number pair –
“0.3456778,0.3456777”
 Second Function, a Table-Valued Function, returns a
table of polygon cells that are ‘built’ from the output
of the SVF and the starting point of the Display
Envelope. A ‘cutter’ variable is used to build-out
polygons with @var_Y rows and @var_X columns.
 The results of these functions are then used to
0perate on the ‘Sites’ point dataset.

11. X
Y

13. At Long Last…
DEMO!!!

14. Further Improvements…
 Add logic to always draw ‘up-to’
the lower limit.
 Possibly add some ‘weight’ to the
clustered point so that it doesn’t
show in such a ‘regular’ way.
 Others…

15. TNRIS Information Services…
 Richard Wade – Team Lead
 Ryan Mitchell – Web Systems and Data Czar
 Yvette Giraud – Web Development
 Ragunath Jayabalakrishnan – Developer (Contract)
 Chris Williams – Database Administrator

16. www.foursquare.com