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<html><head>
<title>Open Visualization Data Explorer: Auto Insurance Claims</title>
</head>
<body bgcolor="white">

<h2>Auto Insurance Claims</h2>
<table border = 0>
<tr bgcolor="white">
<td>
<applet
	code="imageWindow.class" width = 820 height = 253
	codebase=../
	archive=htmlpages/dx.jar,htmlpages/samples.jar
	MAYSCRIPT=true
>
	<param name=INITIAL_IMAGE value="htmlpages/AutoInsurance1.0.0.gif">
	<param name=IMAGE_NODE value="Image_1">
</applet>
</td>
</tr>
<tr>
<td>
<applet
	code="CaptionLabels.class" width = 820 height = 25
	codebase=../
	archive=htmlpages/dx.jar,htmlpages/samples.jar
	MAYSCRIPT=true
>
	<param name=DXLOutput0 value="DXLOutput_1">
	<param name=BACKGROUND value="[1.0, 1.0, 1.0]">
	<param name=FOREGROUND value="[0.0, 0.0, 0.0]">
	<param name=FONT value="TimesRoman-italic-24">
</applet>
</td>
</tr>
</table>

<br>
<table border=0>
<tr>
<td>
<applet
	code="AutoInsurance.class" width = 380 height = 300
	codebase=../
	archive=htmlpages/dx.jar,htmlpages/samples.jar
	MAYSCRIPT=true
>
	<param name="name" value="AutoInsurance">
	<param name=NETNAME value="AutoInsurance.net">
	<param name=DXUIVERS value="4.1.1">
	<param name=BACKGROUND value="[1.0, 1.0, 1.0]">
</applet>
</td>
<td>
<applet
	code="imageWindow.class" width = 432 height = 141
	codebase=../
	archive=htmlpages/dx.jar,htmlpages/samples.jar
	MAYSCRIPT=true
>
	<param name=INITIAL_IMAGE value="htmlpages/AutoInsurance2.0.0.gif">
	<param name=IMAGE_NODE value="Image_2">
</applet>


<h3>...about the visualization</h3>
This visual program shows car insurance claim information 
projected onto a map using zip codes.  Height and color of 
each glyph corresponds to statistics on three available claim 
items: VehicleAge, Claim, and AnnualMileage.  The available 
statistics are mean, standard deviation, max, and count 
(the number of claims in the region).

</td>
</tr>
</table>

<h3>...about the visual program</h3>
Also shown is a bounding box of all the zip codes, 
which indicates claims in NY and CA - making it clear 
that not all the claims come from addresses within Texas 
(as might be assumed when mining the data).  This use of 
data visualization to provide an interactive spatial view 
of raw data with local aggregation is valuable both for 
understanding the raw data prior to mining and for discovering 
trends within the data itself.

<h3>...about clustering</h3>
The data may be shown by individual zip code or based on 
the local aggregation of values.  This helps, for example, 
in downtown regions where many zip codes are clustered 
together.  AggregateSize specifies the bin size (in degrees) 
of an imaginary grid overlaying the map, and data for all 
zip codes in each bin will be gathered together before 
calculating statistics.  If AggregateSize is 0, individual 
zip codes will be used.
<p>
Another method for clustering is available using the K Means 
algorithm, which uses spatial information to recognize a user 
specified number of clusters.  This algorithm takes longer to 
execute since it requires looping numerous times, but once the 
clusters have been created the algorithm need not be re-run as 
the user changes displayed statistics.  Note that the entire 
algorithm is implemented as a macro in the visual program and 
no C code was required.


<h3>...about the web page</h3>
In the <i>Execution</i> control panel, select <i>Pick</i> mode.
Picking on a glyph will produce a caption showing its numeric 
values, including the zip code, which may be the average zip 
code in the bin if it encompasses more than one.

<p>

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</body>
</html>