The Statistical Analysis of Crime Data at Street Level: Models Comparison
AbstractThe main techniques used for the spatial analysis of Urban Crime can generally be traced to crime mapping techniques, which are mere representations of crime dispersion over a specific urban area without any statistical modeling of its correlation with the urban structure of the city or any group of socio-demographic and economic variables. In this work, as a proposal to overcome the aforesaid limitation, we analyze the crime occurrences, recorded at street level, in a highly populated district of the City of Genoa, and we use different statistical models to study crime events in relationship with the context in which they happened, interpreting the urban layout of the roads network as a lattice
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Bibliographic InfoPaper provided by University of Genoa, Research Doctorate in Public Economics in its series DEP - series of economic working papers with number 4/2012.
Date of creation: May 2012
Date of revision:
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More information through EDIRC
Urban Crime Analysis; Lattice Models; Space Syntax; Spatial Models;
Find related papers by JEL classification:
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law
- O18 - Economic Development, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
- P25 - Economic Systems - - Socialist Systems and Transition Economies - - - Urban, Rural, and Regional Economics
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- Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 307-33, March.
- Joseph Hilbe, 1994. "Negative binomial regression," Stata Technical Bulletin, StataCorp LP, StataCorp LP, vol. 3(18).
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