cRIME PREVENTION MODEL

CRIME ANALYSIS

The qualitative and quantitative study of crime and law enforcement information in combination with socio-demographic and spatial factors to apprehend criminals, prevent crime, reduce disorder, and evaluate organizational procedures.

TYPES : Intelligence Analysis, Investigative Analysis, Tactical Analysis,  Administrative Analysis

CRIME ANALYSIS

Strategic Analysis

The study of crime and law enforcement information integrated with socio-demographic and spatial factors to determine long term “patterns” of activity, to assist in problem solving, as well as to research and evaluate responses and procedures.

1) Assist in the identification and analysis of long-term problems such as drug activity or auto theft

2) Conduct studies to investigate or evaluate relevant  responses and procedures

SARA problem solving approach

CRIME ANALYSIS

GEOGRAPHIC Space & CrIME

Ecologist theories : 19th century French sociologists and 20th century Chicago School

Placed-based theories :  Cohen and Felson 1979,  Cornish and Clarke (1986)

Behavioral : Becker (1968), Ehrlich (1973), Osorio (2013)

Urbanization theories: Glaezer and Sacerdote (1996)

Atheoretical models Marselli and Vannini (1997), Ehtorf and Spengler (2000), Luiz (2001)

Social Bio-Physics  Bastin, Rollason, Hilton et. al (2007),  Berestycki, Rodriguez, Ryzhik (2013)

sPATIAL eCONOMETRICS

Spatial effects

spatial heterogeneity 

aspects of socioeconomic structure over space

and spatial dependence

socioeconomic interaction among agents

Classical Linear Regression Heteroskedasticity ass'n is violated

ESDA

Exploratory Spatial Data Analysis

• existence of spatial regimes
• preliminary spatial autocorrelation
  
• potential regressors
• spatial trends
• the influence of local outliers
  
• spatial clusters (“hot spots” and “cool spots”)

Precedes a good spatial econometric model

It is intended to discover spatial patterns in data and suggest hypotheses

ESDA

Distribution Mapping  (choropleth map)

Summary Statistics (min, max, mean,  sd, skew,  kurt, q's)

Global spatial autocorrelation Tests --- Weight Matrix

Statistics: Z-statistic, Moran  (1948)  I and Geary (1954)  C

Moran Scatterplot, each location's : value vs spatial lag

ESDA

Spatial Clustering Analysis : Local Indicator of Spatial Association

Further evidence of spatial regimes: Spatial ANOVA Regression, Include Spatial Lag

Outliers Analysis, Spatial Trends (Trend Surface Model)

LISA & ANOVA

Relevant identification of significant spatial clusters via pattern decomposition

Diagnostic of local instability (spatial outliers) in measures of global spatial association.

 Via statistical tests

Multicollinearity k,

Normality J-B,

Heteroskedasticity K-B,

Spatial Dependence  I, LM

 "econo" BAckGRound

many many variables....

real consumption per
capita, unemployment rate, the share of employed in the service sector, social security
benefits, average monthly salary, the share of young male on the total population, the share of students that achieved the secondary and high school degree, the share of foreign residents, share of location firms, government expenditure, more proxies, etc 

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Causality Test : Granger and Newbold (1977)

Game Theory,  Political, Development & Dynamic Economics

Crime Prediction & TIME DYNAMICS

UniMethods: Leading Indicators, Point Process Model, Polygon Grid/Raster GIS Methods,

Point Pattern Analysis: quadrat count and kernel estimation and Bayesian Networks

Areal Analysis

CROSS SECTION ----> PANEL ANALYSIS

Multivariate Methods --> VAR Models

overcome collinearity ---> BVAR Litterman (1980, 1986)

MAth MEat

Adaptative Machine Learning,  Link Analysis, Genetic Programming

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 Biology: Epidemiological models

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Physics: Reaction-Diffusion models 

Biblio

Crime Mapping Laboratory, Police Foundation

Nucleo de Economia Reg. y Urb U. Sao Paulo (NEREUS)

División de Desarrollo Social, CEPAL

Centre d’analyse et de mathématique sociales (CAMS)

FONDAZIONE ENI ENRICO MATTEI

& MANY MORE

EXTREME PROGRAMMING