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As Violence Unfolds: A Space–Time Study of Situational Triggers of Violent Victimization Among Urban Youth

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Abstract

Objectives

This study clarifies three important issues regarding situational or opportunity theories of victimization: (1) whether engaging in risk activities triggers violent assault during specific, often fleeting moments, (2) how environmental settings along individuals’ daily paths affect their risk of violent assault, and (3) whether situational triggers have differential effects on violent assault during the day versus night.

Methods

Using an innovative GIS-assisted interview technique, 298 young male violent assault victims in Philadelphia, PA described their activity paths over the course of the day of being assaulted. Case-crossover analyses compared each subject’s exposure status at the time of assault with his own statuses earlier in the day (stratified by daytime and nighttime).

Results

Being at an outdoor/public space, conducting unstructured activities, and absence of guardians increase the likelihood of violent victimization at a fine spatial–temporal scale at both daytime and nighttime. Yet, the presence of friends and environmental characteristics have differential effects on violent victimization at daytime versus nighttime. Moreover, individual risk activities appeared to exhibit better predictive performance than did environmental characteristics in our space–time situational analyses.

Conclusion

This study demonstrates the value of documenting how individuals navigate their daily activity space, and ultimately advances our understanding of youth violence from a real-time, real-life standpoint.

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Notes

  1. Behavioral setting is defined as “the part of the environment which an individual can, at a particular moment in time, access with his or her sense” (Wikström et al. 2010, p. 61).

  2. Out of the 298 study subjects, 123 (41.3%) suffered gunshot wound assault and 175 (58.7%) suffered non-gunshot wound assault. A small number of female subjects (N = 31) were also recruited in the STARS. However, due to the small sample size, we dropped them from the analysis.

  3. Academic associates are individuals who were trained in recruiting patents for clinical studies.

  4. First (12- to 13-year-olds) and fourth (15- to 16-year-olds) graders were included in Averdijk and Bernasco (2015). The first grade in the Netherlands is similar to the seventh grade in the United States; the fourth grade is similar to the tenth grade.

  5. This is particularly meaningful when prior research provides little insight on what constitutes an appropriate control window. This is true of crime and violence; hardly any research has investigated the induction or hazard period associated with situational correlates or triggers of crime and violence and provided useful information on “wash-out periods”. Arbitrary selection of “control” periods can produce substantial bias to parameter estimates (Mittleman and Mostofsky 2014).

  6. We chose our method of plotting a new point on the map only when a subject reported a change in status in terms of location or any of the activities and behaviors mentioned in the next paragraph because it is an efficient way to obtain and document a considerable volume of detailed information from each subject. This significantly shortened the time needed for the mapping exercise and reduced the burden for the participants of recalling irrelevant details (e.g. if a subject was walking alone for a prolonged period of time, only two points need to be recorded).

  7. Our novel approach is different from the space–time budget method, which collects information at fixed intervals about the main activity, the function of the place where the activity was performed, and any persons present in the setting for each hour of the day (Averdijk and Bernasco 2015; Wikström et al. 2012). We move beyond prior research by not saying “please tell me what you did in the first 10 min, and then the second 10 min (or the first hour and second hour) and so on”.

  8. Also, to each path point we attached data about characteristics of the built and social environment that was present at the location. Because those data are comprised of smoothed surface layers, when attached to the minute-specific point data there is considerable autocorrelation, with values of adjacent points being more similar than values of points that are further separated in time (and space). Ten-minute segments adequately address this issue.

  9. We did not check the reverse pattern because it is highly possible that any individual who ever carried a weapon or used substances decided not to conduct those behaviors on that particular day.

  10. The survey asks people in Southeastern Pennsylvania about their health, their medical care, and what it is like to live in their neighborhoods. Interviews were conducted by telephone (landline and cell phone) using a random-digit dial methodology; twenty percent of interviews are conducted with cell phone respondents. For additional details about the survey methodology, please see: http://www.chdbdata.org/household-health-survey.

  11. The spatially smoothing process estimates the value of a variable at any specific point on a surface layer by calculating a weighted average of the values at nearby observed locations or spatially contiguous entities. Smoothing methods are frequently used to improve measurement accuracy and create more robust estimates (Waller and Gotway 2004).

  12. We accessed sunrise/sunset times from the National Weather Service.

  13. As a robustness check, we also created a measure covering both adult family members and other adults known to the subject. Substantively similar findings were obtained. Given that other adults known to the subject have varying levels of responsibility and/or attachment to the subject, we reported results considering adult family members only in this paper.

  14. SEPTA is an acronym for Southeastern Pennsylvania Transportation Authority.

  15. The Huber/White/sandwich estimator of variance adjusted for clustering or intra-subject correlation when multiple data points were included for the same participant.

  16. Missing values were assigned to path points with ambiguous answers to the activity field in our GIS-assisted interview for the unstructured activities variable because it was unclear if activities in those settings were structured or not. The most frequent reasons for missing were “none” or an unqualified single-word phrase such as “sitting, standing, walking, running or driving”.

  17. The rates of weapon carrying and substance use in our sample were relatively low partially because some people under police guard were excluded from the study.

  18. Due to the very low rate of weapon carrying at the victimization point during daytime, the regression coefficient could not be estimated.

  19. McFadden’s pseudo R2 values tend to be considerably lower than the R2 values commonly obtained in ordinary least squares regression; values of 0.2–0.4 are indicative of excellent model fits (Domencich and McFadden 1975; McFadden 1979).

  20. For example, using a case–control study design, we can examine why some individuals are more likely to be violently assaulted during routine activities and in risky behavioral-settings than others.

  21. Small risks applied to large populations often have greater population level impacts than large risks applied to small populations. In some ways, environments are the consummate small risk.

  22. Zimring (1968), for instance, reported that “the attack data do not reveal substantial differences between fatal attacks using particular weapon forms and serious area, non-fatal attacks involving the same weapon” (p. 736).

  23. It is worth noting that the activities of the day of the assault are not the only activities that matter to one's risk for victimization. Yet, what the participants did yesterday and before was all fixed within subjects and consistent within subjects over the 24-h period when we monitored them.

  24. Given the strong situational relation between victimization and offending reported by prior research (e.g., Averdijk and Bernasco 2015), the E-values do not guarantee that victims’ own role in prior conflict did not play a significant role in leading up to their victimization.

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Authors and Affiliations

  1. Department of Criminology, Law and Society, George Mason University, 354 Enterprise Hall, 4400 University Drive, MS 4F4, Fairfax, VA, 22030, USA

    Beidi Dong

  2. Department of Epidemiology, Columbia University, New York, NY, 10032, USA

    Christopher N. Morrison & Charles C. Branas

  3. Department of Biobehavioral Health Sciences, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Therese S. Richmond

  4. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, 19104, USA

    Douglas J. Wiebe

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  1. Beidi Dong
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  2. Christopher N. Morrison
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  3. Charles C. Branas
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  4. Therese S. Richmond
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  5. Douglas J. Wiebe
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Correspondence to Beidi Dong.

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Appendix

Appendix

See Figs. 1, 2, 3, 4 and Tables 5, 6, 7, 8.

Fig. 1
figure 1

A screenshot of the data collection application as it appeared on the screen of a laptop computer. Note Data are hypothetical since individuals’ location-specific data are never shown for confidentiality reasons

Full size image
Fig. 2
figure 2

An illustration of how activity path data were appended to geographic data layers based on latitude and longitude coordinates of subjects’ activities. Raster surface layer of the level of a risk factor in the urban landscape as demonstrated using off-premise alcohol outlets (top). Raster surface layer of the urban landscape overlaid with path points marking locations of the daily activities of 298 study subjects (bottom)

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Fig. 3
figure 3

Differences, in standard deviation units, between the maximum and minimum level of exposure to features of the environment experienced by subjects during daily activities. The number in each figure is the proportion of subjects who experienced a highest level of exposure to a variable that was at least one standard deviation greater than the lowest level of exposure to the variable

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Fig. 4
figure 4figure 4

Mean levels of exposure to 11 situational variables experienced by study subjects during 10-min window over the 8 h preceding and including the time of assault (left-most point in each graph). We applied a Theil–Sen estimator to the series of points for each variable, which tested the null hypothesis that the average slope over the 8-h period was zero. The result is reported in the upper left corner of each plot: *p < 0.05; ** p < 0.01; ***p < 0.001; n/s non-significant

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Table 5 Characteristics of violent assault victims
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Table 6 Source, coding, and format of data used to create surface layers representing environmental exposures in Philadelphia, PA
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Table 7 Within-subject correlation between levels of exposure to environmental characteristics that were present in the locations where subjects lived and (a) mean levels of exposure experienced during daily activities and (b) at the victimization points
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Table 8 Sensitivity analysis (E-values) for statistically significant odds ratios
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Dong, B., Morrison, C.N., Branas, C.C. et al. As Violence Unfolds: A Space–Time Study of Situational Triggers of Violent Victimization Among Urban Youth. J Quant Criminol 36, 119–152 (2020). https://doi.org/10.1007/s10940-019-09419-8

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  • DOI: https://doi.org/10.1007/s10940-019-09419-8

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