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Sex Offender Residency and Spatial Equity

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Abstract

Recent legislation at the local, state and federal levels has mandated spatial restriction zones around sensitive facilities, such as schools, daycares and public parks, to minimize the exposure children have to convicted sex offenders. A potential byproduct of this legislation is the clustering of offenders in certain areas outside restriction zones. Accordingly, efforts are now being directed toward ensuring an equitable spatial distribution of exposure to convicted sex offenders. In particular, laws are being enacted or resurrected to thwart such clusters. The question addressed in this paper is what approaches can be used to assist planners and public officials in the further development of policy for mitigating community impacts associated with the spatial distribution of sex offender residences. We review and develop methodologies for use in assessing and managing residential impacts. Application results suggest that these modeling approaches can provide insight for policy development useful to local and regional governments.

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Notes

  1. To be clear, we are not recommending a policy of offender dispersion. Instead, we are recommending that law enforcement agencies and legislators fully explore the contingencies of dispersion legislation before ratification.

  2. For a more thorough review of dispersion modeling and its applications, see Erkut and Neuman (1990) or Curtin and Church (2006).

  3. Sex offender data was acquired from Simon L. Leis and the Hamilton County Sheriff’s Office (HCSO 2005). This database consists of 1,095 offenders. Through subsequent address matching, 974 offenders were assigned directly to their residential parcel while the remaining 121 offenders were assigned latitude and longitude coordinates in a geocoding process. For more details regarding this database, see Grubesic et al. (2007).

  4. The chi-square goodness-of-fit test is formulated as follows:

    $$\chi ^2 = \sum\limits_{i = 1}^k {\frac{{\left( {O_i - E_i } \right)^2 }}{{E_i }}} $$

    where:

    O i :

    observed or actual frequency count of a good, service or hazard in spatial unit i

    E i :

    expected frequency count in the spatial unit i

    k :

    number of spatial units in the study area

  5. The location quotient is formulated as follows:

    $$LQ_i = \left( {\frac{{{{s_i } \mathord{\left/ {\vphantom {{s_i } {p_i }}} \right. \kern-\nulldelimiterspace} {p_i }}}}{{{{\sum\limits_i^n {s_i } } \mathord{\left/ {\vphantom {{\sum\limits_i^n {s_i } } {\sum\limits_i^n {p_i } }}} \right. \kern-\nulldelimiterspace} {\sum\limits_i^n {p_i } }}}}} \right) \times 100$$

    where:

    s i :

    number of goods, services or hazards in spatial unit i

    p i :

    population in spatial unit i

  6. For more details on “share-based” approaches such as the location quotient, and their spatial implications, see Mack et al. (2007).

  7. The municipalities were selected to highlight the utility of the specified location model, not as a representative sample of convicted offenders for statistical analysis.

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Authors and Affiliations

  1. Department of Geography, Indiana University, Bloomington, IN, 47405, USA

    Tony H. Grubesic

  2. School of Geographical Sciences, Arizona State University, Tempe, AZ, 85287, USA

    Alan T. Murray

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  1. Tony H. Grubesic
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  2. Alan T. Murray
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Correspondence to Tony H. Grubesic.

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Grubesic, T.H., Murray, A.T. Sex Offender Residency and Spatial Equity. Appl. Spatial Analysis 1, 175–192 (2008). https://doi.org/10.1007/s12061-008-9013-5

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