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Geographical distribution of crime in Italian provinces: a spatial econometric analysis

Content Provider	Springer Nature Link
Author	Cracolici, Maria Francesca Uberti, Teodora Erika
Copyright Year	2009
Abstract	In the last years, the increasing level of criminality that has characterized the modern economies has drawn the attention of sociologists and economists in order to identify the causes leading to commit criminal offences. The aim of the paper is to investigate the causes of crime activity in 103 Italian provinces (NUTS3 regions) for the years 1999 and 2003. The Italian crime phenomenon is characterized by some stylized facts: high spatial and time variability of crime activities, and presence of ‘organized crime’ (e.g. Mafia and Camorra) localized in some local territorial areas. Using exploratory spatial data analysis (ESDA), the paper firstly explores the spatial structure and distribution of four different typologies of crimes: murders, thefts, frauds and squeezes. ESDA allows us to detect some important geographical dimensions and to distinguish crucial micro- and macro- territorial aspects of offences. Further, on the basis of Becker-Ehrlich model, a spatial cross-sectional model – including deterrence, economic and socio-demographic variables – has been performed to investigate the determinants of Italian crime in 1999 and 2003 and its ‘neighbouring’ effects, measured in terms of ‘geographical’ and ‘relational’ proximities. The empirical results obtained by using different spatial weights matrices highlighted that socio-economic variables have a relevant impact on crime activities, but their role changes enormously respect to crimes against person (murders) or against property (thefts, frauds and squeezes) and over time. In den letzten Jahren hat die zunehmende Kriminalitätsrate der modernen Volkswirtschaften die Aufmerksamkeit der Soziologen und Ökonomen auf sich gezogen um die Ursachen zu ermitteln, die dazu führen diese Straftaten zu begehen. Das Ziel dieser Studie ist die Ursachen der Kriminellen Aktivitäten in 103 italienischen Provinzen für die Jahre 1999 und 2003 zu ermitteln. Dieses Phänomen zeichnet sich durch einige stilisierte Fakten: hohe räumliche und zeitliche Veränderlichkeit von kriminellen Aktivitäten und die Präsenz von “organisiertem Verbrechen” (z. B. Mafia und Camorra) örtlich in regionalen Gebieten eingegrenzt. Mittels der explanatory spatial data analysis (ESDA) untersucht die Studie zunächst die räumliche Struktur und Verteilung von vier verschiedenen Arten von Verbrechen: Mord, Diebstahl, Betrug und Erpressung. ESDA ermöglicht es uns wichtige geographische Dimensionen zu ermitteln und bedeutende Mikro- und Makro-regionale Aspekte von Straftaten zu unterscheiden. Weiterhin, auf der Grundlage des Becker-Ehrlich Modells, wurde ein räumlicher Querschnitt-Modell erstellt, welches Abschreckungsvariabeln, wirtschaftliche und sozio-demographische Variablen miteinbezieht, um die Ursachen zu ermitteln, gemessen bezüglich der geographischen und relationalen Nähe, welche die italienische Kriminalität für die Jahre 1999 und 2003 und ihre “benachbarten” Auswirkungen prägen. Mittels der unterschiedlichen räumlich gewichteten Matrizen zeigen die Ergebnisse, dass die sozio-ökonomischen Variablen eine relevante Auswirkung auf die Kriminellen Aktivitäten haben, aber ihre Rolle sich erheblich ändert mit Bezug auf Verbrechen gegen die Person (Mord) oder gegen Eigentum (Diebstahl, Betrug und Erpressung).
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	01737600
Journal	Jahrbuch für Regionalwissenschaft
Volume Number	29
Issue Number	1
e-ISSN	16139836
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-01-08
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Crime Spatial analysis Distance weights matrix Relational weights matrix Landscape/Regional and Urban Planning Geography (general) Population Economics Environmental Economics Regional/Spatial Science
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Economics and Econometrics

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