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IPS129 Claudia V. et al.
women (more than 80% are less than 45 years old), less unsafe and worried
about crimes than the previous cluster, living in better areas in term of social
decay, in municipalities other than metropolitan areas, mainly in the South
(Basilicata, Campania, Puglia, Sicilia) at a lesser extent in Liguria and Marche:
their situation has improved over time.
As regards the economic loss of bag-snatching victims, the significant
effects increasing the victim’s economic loss are living in the center of a
metropolitan area with respect to other municipalities’ type, if the victim was
within his/her car and at a lesser extent if he/she was within a shopping center,
while the loss was lower than the mean loss if the victim was in a park when it
happened, if money or jewelries have been stolen, and if it happened between
6 and 9 a.m.
As regard robbery the economic loss increases if the stolen objects, among
other, are jewelry, luggage, watch, fur coats, silver ware, HiFi, Tv, furniture.
Objects that suggest that the crime happen at home, the intangible lost is very
high and there is a home violation.

4. Discussion and Conclusion
For the FDA models adopted (Lavit et al 1994) or Tucker models
(Kroonenberg 1992) seems more suitable for cubic matrices of data as the
third dimension is time, predicted by the peculiarity to be ordered: in FDA it is
explicitly treated as an element of a different nature compared to the other
two dimensions, unit and variable. The regression model gives an interesting
result trying to highlight the effective cost of considered crimes.
The paper aims to validate a model of analysis of the economic impact of
victimization on an individual and family level by comparing the subjective
dimension with the objective dimension of the loss.

References
1. Aromaa K. and M.Heiskanen (eds.), (2008), “Victimisation Surveys in
comparative Perspective”, HEUNI -European Institute for Crime
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2. Ceccato V., (2011), The Urban Fabric of Crime and Fear, Springer
Dordrecht Heidelberg New York London.
3. Coppi R., Zannella F.(1979), L’analisi fattoriale di una serie temporale
multipla relativa allo stesso insieme di unità statistiche, Atti della XXIX
riunione della SIS.
4. Corazziari I., Dynamic Factor Analysis, in Vichi M., Opitz O., Classification
and Data Analysis, Theory and Application, Springer, 1999, p.171-179.
5. Cordner G (2010). ‘Reducing Fear of Crime: Strategies for Police’. Office
of Community Oriented Policing Services, U.S. Department of Justice,
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