Auto Insurance Claims

...about the visualization

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).

...about the visual program

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.

...about clustering

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.

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.

...about the web page

In the Execution control panel, select Pick 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.

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