Leveraging SQL Server to Improve Vector

Display through Point Clustering

Due to Java Script limitation, Large Numbers of Vectors don’t draw in Web-Mapping Environments.

Cutoff is ~ 300-500 Features

The Problem…

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.

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!

Why Invent a ‘Better’ Solution???

Factors Affecting a Good ‘Clustering’ Solution…

The Data 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.

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

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

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.

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.

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.

X

Y

At Long Last…

DEMO!!!

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…

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

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