Policy Paradox: Mapping Residential Restrictions

Do residential restrictions placed on convicted sex offenders serve to protect the public?  Duke Economics Ph.D. candidate Songman Kang, has been using the analytical capabilities of geographic information software to help determine the extent to which the restrictions affect residential locations of sex offenders: computing the area covered by a restriction and determining which offenders had to relocate due to a restriction.

According to Kang, the residential restrictions are designed to reduce recidivism among sex offenders and prevent their presence near places where children regularly congregate.  Neither of these claims has been found consistent with empirical evidence though, and it is unclear whether the restrictions have been successful in reducing the rates of repeat sex offenses.  On the other hand, the restrictions severely limit residential location choices, and may force offenders to relocate away from employment opportunities and supportive networks of family and friends.  As a result of the deteriorated economic conditions, the offenders who had to relocate may become more likely to commit non-sex offenses.

The following maps illustrate some of the restricted zones in Miami and in the Triangle area of North Carolina studied by Mr. Kang.

Figure 1: Residential Restricted Zones in Miami

Figure 2: Triangle Restricted Residences

Rolling with R in 2011

Interest in the open source statistical package R has grown over the last few years as researchers discover its powerful graphic capabilities, a suite of packages that extend its functionality, and its data import capabilities.  While several courses use R to teach introductory statistics, most researchers arrive at R with some statistical experience.  The following selected resources represent a growing number of books and websites designed to help orient users to the capabilities of R.

Quick-R Homepagequick_r
This website tries to provide a quick overview of basic data management and statistical capabilities of R for current SAS, SPSS, Stata, and Systat users.  The stress is on providing a brief overview of R commands for common data analysis needs.

R for Stata UsersR for Stata
A comprehensive guide for getting started in R using Stata as point of reference.

R for SAS and SPSS Users (not pictured)
Similar concept as R for Stata users.

Using R for Introductory Statistics
John Verzani’s R for Introductory Statistics is one of several introductions to using R for basic statistics. Examples are available as an R package.

Do you have other sources that you like for R?  Let us know in the comments.