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The Emergence and Evolution of Problematic Properties: Onset, Persistence, Aggravation, and Desistance

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Abstract

Objectives

Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each.

Methods

We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year.

Results

Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations.

Conclusions

The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.

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Notes

  1. Parcels contain one or more properties (e.g., condo buildings are parcels with a separate property for every unit; mean = 1.82 properties, sd = 7.88, 88% had 1 property, but 171 (0.2% of parcels) had more than 50). However, in official records of events the most granular piece of information is the street address, which does not distinguish between properties within a parcel. For this reason, it is necessary to treat parcels as the most fundamental unit available to analysis.

  2. K-means is a non-deterministic technique that is dependent on the starting point of the process. As a result it can generate various solutions, some of which are “local optima” that fail to fully capture the contours of the data. To address this, we ran the cluster analysis 10 separate times to get a sense of the range of optima identified. Qualitatively, these were nearly identical. There was always a predominant category of parcel-years with no major issues; a smallest category with a mixture of serious issues, or violent hubs; and four other categories that featured specialization. This latter group featured slight distinctions across solutions (e.g., the combination of both forms of physical disorder in a single cluster in one solution), but the most consistent composite result was the identification of four clusters specializing in private conflict, public denigration, private neglect, and gun-related events. A second consideration was how liberally “no major issues” was defined by the cluster analysis when a parcel-year had a small number of issues. Across solutions this varied by ~ 30,000 parcel-years (i.e., between 595,000 cases and 625,000 cases). Based on these observations, we prioritized a solution that had cleaner representations of specialization as it made the description of year-to-year transitions more accessible, interpretable, and actionable; and solutions that classified fewer cases as having no major issues in order to minimize the number of potentially meaningful cases that were excluded from the Markov chain analysis. That is what we report here.

  3. This was accomplished in three steps. We first calculated the t value for the difference between the observed and expected likelihood of a given cell in the probability transition matrix separately for neighborhoods with above- and below-average crime. We then translated these t values to Cohen’s d, a standardized measure of magnitude, using the equation \(2*\frac{t}{\sqrt{n-2}}\) where n was the number of parcels who could have experienced that transition (i.e., for aij, n = # of parcels with initial state of i). We also calculated the sampling variance (v) for each d value as \(v= \frac{1}{{n}_{1}}+\frac{1}{{n}_{2}}+\frac{{d}^{2}}{2*\left({n}_{1}+{n}_{2}\right)}\). These values then permitted a traditional z-score calculation of \(z=\frac{{d}_{1}-{d}_{2}}{\sqrt{{v}_{1}+{v}_{2}}}\) that evaluated whether the magnitude of difference from expectations was different between the two contexts.,

  4. The reader may note that the exclusion of parcels that never experienced major issues across the study period will remove more parcels from neighborhoods with below-average crime and disorder than from those with above-average crime and disorder. Because the transition calculations in the Markov chain are contingent on the starting transition, this is only consequential for transitions that begin at a point of no major issues. In the analysis here, this is limited to examinations of onset as the other three phenomena operate from the starting point of experiencing major issues. In light of this, comparing with local expectations clarifies the interpretations by accounting for the proportion of parcel-years in the “no major issues” category in the remaining sample.

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Funding

Funding was provided by Division of Social and Economic Sciences (Grant No. 1921281).

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Authors and Affiliations

  1. School of Public Policy and Urban Affairs, Northeastern University, 310 Renaissance Park, 1135 Tremont St., Boston, MA, 02120, USA

    Daniel T. O’Brien, Alina Ristea & Forrest Hangen

  2. School of Criminology and Criminal Justice, Northeastern University, Boston, USA

    Daniel T. O’Brien & Riley Tucker

  3. Boston Area Research Initiative, Northeastern and Harvard Universities, Boston, USA

    Daniel T. O’Brien, Alina Ristea, Riley Tucker & Forrest Hangen

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  1. Daniel T. O’Brien
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Correspondence to Daniel T. O’Brien.

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O’Brien, D.T., Ristea, A., Tucker, R. et al. The Emergence and Evolution of Problematic Properties: Onset, Persistence, Aggravation, and Desistance. J Quant Criminol 39, 625–653 (2023). https://doi.org/10.1007/s10940-022-09542-z

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