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Assessing the Spatial–Temporal Relationship Between Disorder and Violence

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Abstract

The relationship between disorder and violence has generated much debate in the field of criminology. While advocates of the broken windows thesis believe disorder is the root cause of crime, other researchers view both disorder and crime as analogous behaviors resulting from the breakdown of collective efficacy. Scholars from both sides of this debate, however, assume a long-term correlation between disorder and crime at places. This assumption has not been tested with a longitudinal dataset at a relatively small geographic unit of analysis. The current study used data collected in Seattle, Washington and utilized Group-based Trajectory Analysis and Joint Trajectory Analysis to explore the longitudinal relationship between disorder and violence. The results showed that disorder, just like crime, concentrates in a few “hot spots.” Additionally, the results showed that while the lack of disorder problems guarantees places to be violence free, having high levels of disorder predicts having violence problems only about 30% of time. As such, these findings point out the need for future theorization efforts on the disorder-violence nexus to include contextual factors which could explain this imperfect association between the two.

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Notes

  1. People who are interested in the debate can refer to Sousa and Kelling (2006). This debate, however, is not the central focus of this paper and will not be discussed here due to space limitations.

  2. Corman and Mocan (2000) also examine the association between misdemeanor arrests and crime rate in NYC over time. However, they did not measure disorder directly; rather, they used misdemeanor arrests as a proxy of disorder. Geller (2007) examines housing conditions and their relationship to crime rates in NYC. But only physical disorder was used in the study.

  3. A block group is smaller than a census tract and larger than a census block. Generally, a census tract can be divided into about 4 block groups (at most 9 block groups) depending on the size of population within each block group (Census Bureau 1994).

  4. For more information about Seattle’s park history, see http://www.seattle.gov/parks/parkspaces/olmsted.htm (Retrieved on 26 Oct 2009).

  5. The Seattle Police Department has kept detailed crime data since the 1980s. Although due to data format issues, only data after 1989 are available in computerized format (see Weisburd et al. 2004 for a detailed discussion of this issue).

  6. See Lum (2003) for an in-depth discussion of data sources available from Seattle.

  7. The type of dumping and litter items recorded in the database consists of items such as tires, appliances, yard waste, mattresses, and freezers to list just a few.

  8. In the database, several agencies have been identified to be main sources of information including Department of Corrections, Citizens Service Bureau, Police Department, Parking Enforcement, city council, and Health Department.

  9. The problem of using macro-level geographic units is that the findings might not be generalizable to smaller geographic units. Additionally, Groff et al. (2008) have found that there is a significant amount of variability in crime at the micro level that is often not observed at the macro geographic level. Thus, macro-level analysis may mask the dynamic relationships between disorder and crime (Weisburd et al. 1992; Groff et al. 2008).

  10. Other researchers have argued the importance to explore crime at even smaller geographic units (e.g., Sherman et al. (1989) used addresses, Taylor (1999) used block faces, and Eck and Weisburd (1995) studied places with a place manager). Understanding the disorder-crime relationship at smaller geographic units is important and should be explored in the future.

  11. The data obtained from the Seattle Police Department did not included any crime related information in these block groups. Specifically, any block groups associated with tract 263 and most block groups in tract 264 and 265 had no incidents reported over the study period. Consulting with the police department, it was found that the data from these sites were not included due to the cited reasons.

  12. Some researchers argue the quality of data and the availability of time-span of data will seriously impact the conclusion (see Eggleston et al. 2003). Nagin (2004) believes that all statistical tools will experience the same problems if sufficient information is not provided. Furthermore, Bushway et al. (2009) found that both GBTA and growth curve modeling identify very similar average developmental patterns of individual criminality.

  13. At a smaller geographic unit of analysis, Weisburd et al. (2004a) found similar declining crime trajectories in Seattle.

  14. Two ordinal variables are concordant if one observation is ranked high on both variables or ranked low on both variables (Weisburd and Britt, 2007).

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Acknowledgments

This research was supported by grant 2005-IJ-CX-0006 from the National Institute of Justice (US Department of Justice). The points of view in this paper are those of the author and do not necessarily represent those of the US Department of Justice. Special thanks to David Weisburd for his thoughtful suggestions throughout the writing of this paper. I would like to thank Anthony Braga, Daniel Nagin and the anonymous reviewers whose comments were invaluable in strengthening this paper. I also want to express my gratitude to Josh Hinkle and Kristen Miggans for their numerous editing assistance and thoughtful suggestions on earlier drafts of this manuscript.

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  1. Department of Criminal Justice, Georgia State University, P.O. Box 4018, Atlanta, GA, 30302-4018, USA

    Sue-Ming Yang

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Table 4 Trajectory model diagnostics
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Yang, SM. Assessing the Spatial–Temporal Relationship Between Disorder and Violence. J Quant Criminol 26, 139–163 (2010). https://doi.org/10.1007/s10940-009-9085-7

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