futurity.org: Using Mathematical Model to Formulate Sex Offender Laws.
A new mathematical model could help communities that are in the midst of passing or reforming sex offender laws quantify risk and address issues of special concern. The model is designed to help the policymakers of concerned communities focus on the spatial management of sex offenders and not mere punitive measures.
The model incorporates many of the pertinent variables addressed in popular sex offender laws, including housing restrictions, sensitive facilities, and individuals who might be considered the prey of sexual predators. By adjusting parameters and variables, model users can see how adjustments in a law would influence the position and density of sex offenders in a community.
There are three commonly used geographic strategies for managing sex offenders, all of which entail some type of housing restrictions. In general, residence restrictions prevent sex offenders from establishing a permanent residence within a specified distance (e.g. 1,000 ft.) from a sensitive facility, such as a school. Dispersion ordinances seek to reduce neighborhood exposure to sex offenders by minimum distance at which the sex offenders may live or work relative to other sex offenders. The rationale behind saturation laws is similar to that of dispersion laws, except saturation laws focus on limiting the number of sex offenders who may live in a single residence, or within a pre-defined complex of residences or development.
While most U.S. states have residence restrictions in place, supplemental or increasingly punitive laws are often passed at the local level in the wake of tragedies. As a result, many laws tend to be focused on the isolation of offenders, to the exclusion of practical matters, like ensuring access to rehabilitation services or monitoring the unfair exposure of rural or exurban areas to higher concentrations of sex offender parolees.
“A lot of local policies are knee-jerk reactions,” Grubesic says. “As a result, communities may actually expose themselves to a net-greater risk than in the absence of a law, and that’s because there is very little empirical investigation into how these laws might impact communities before they are passed.”
A commonly reported story last year was the clustering of convicted sex offenders under the Julia Tuttle Causeway in Dade County, Fla. Laws that restrict the zones where sex offenders can live in the county (which includes Miami) were so vast that there were few, if any, places left for sex offenders to live.
Some might be tempted to disregard the sex offenders’ plight as fitting, if only because sex offenders are among the most reviled criminals in our society. But what of the law-abiding citizens who live near sex offender clusters? Are such residence restrictions fair to them? And aren’t sex offender parolees harder to track if they aren’t associated with a specific residence.
The model allows communities to see how different kinds of approaches to managing sex offenders work and to see how these approaches interact with each other in new and unexpected ways. It also allows governments to demonstrate an intention of good faith—that they acted dispassionately to protect society-at-large, rather than pile on double-jeopardy-type punishments to sex offenders who have completed the terms of their sentences. Civil rights organizations, such as the ACLU, occasionally take up the causes of sex offenders in those situations.
“Our model allows communities to more definitively state that the laws were passed earnestly and in a transparent fashion—taking into account the various costs and benefits associated with different distributions of sex offenders,” Grubesic says.
Grubesic and Murray tested their model in Hamilton County, Ohio, chosen for its ongoing efforts to manage sex offenders and for its demographic diversity. The geographers demonstrated vastly different outcomes associated with a variety of hypothetical sex offender ordinances and their permutations.
By way of example, the researchers have shown that lawmakers could ostensibly look at the geographic results of each use of the model, and decide which risk management strategy best suits local values and needs.
Grubesic and Murray’s work is funded with grants from the National Science Foundation.
