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the locations of road accidents taking into account the various insurance
categories (full coverage, partial coverage, and so on) and the client data. This
schema includes a spatial measure representing the locations of accidents. We
can use, for example, the default aggregate function (the spatial union) to roll
up to the InsuranceCategory level in order to display the accident locations
corresponding to each category aggregated and represented as a set of points.
Other aggregation functions can also be used for this, such as the center of
n points.
Insurance
Category
CategoryName
...
AgeGroup
Year
GroupName
MinValue
MaxValue
...
Year
Quarter
InsuranceType
Quarter
Insurance
Age
Client
ClientId
FirstName
LastName
BirthDate
Profession
SalaryRange
Address
...
InsuranceNo
ValidityPeriod
...
Month
Month
Calendar
Accidents
Time
Date
...
AmountPaid
Address
City
State
LocationId
StreetName
Number
...
CityName
CityPopulation
CityArea
...
StateName
StatePopulation
StateArea
...
Fig. 11.13 A variant of the schema in Fig. 11.12
An alternative schema for the analysis of road accidents is shown in
Fig. 11.13 . In this schema, there is no spatial measure; the focus of analysis has
been changed to the amount of insurance payments according to the various
geographic locations. This is reflected by the spatial hierarchy Address
City
State .
Although the schemas in Figs. 11.12 and 11.13 are similar, different
analyses can be performed when a location is represented as a spatial measure
or as a spatial hierarchy. For example, in Fig. 11.12 , the locations of accidents
can be aggregated (by using spatial union) when a roll-up operation over the
Time or Insurance hierarchies is executed. However, this aggregation cannot
 
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