Skip to main content

Advertisement

Log in

Seasonal Variation in Violent Victimization: Opportunity and the Annual Rhythm of the School Calendar

Journal of Quantitative Criminology Aims and scope Submit manuscript

Abstract

Objectives

This study draws on an underused source of data on seasonality—victim surveys—to assess whether violent crime occurs with greater frequency during summer months or whether it simply becomes known to police more often, and to examine the extent to which seasonal patterns in violent crime are differentiated based on victim characteristics and location of crime.

Methods

Data used come from the 1993–2008 National Crime Victimization Survey. Time series regression models are estimated to describe seasonal differences in violent crime victimization and reporting rates.

Results

Seasonal trends in youth violence stand in contrast to the trends for young and older adults, primarily due to their high risk of victimization at and near school. No evidence of seasonality is found in the extent to which serious violence becomes known to the police. However, simple assault is significantly more likely to come to the attention of the police during the summer months, primarily due to increases in the reporting of youth violence.

Conclusions

Our findings confirm some of the previous work on seasonal patterns in violent crime, but also show that these patterns vary across age groups, locations, and type of violence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Property crime may also vary according to seasons. However, there appear to be contradictory arguments and findings as to when property crime rates are highest. Scholars such as Quetelet (1831/1984), Lombroso (1911) and Falk (1952) initially suggested that property crime should increase in winter, due to a scarcity of goods. More recent work by Farrell and Pease (1994) finds support for this. On the other hand, others suggest that property crime should peak in summer months when residences are more likely to be left unoccupied and personal belongings are more often left outside and unattended (see Cohn 1990; Yan 2004).

  2. Hipp et al. (2004) recognize this problem and drop from their analyses data from police departments that appear to report crime inconsistently (e.g., only in December). In their analyses, McDowall et al. (2012) rely on a dataset assembled by Maltz (n.d.) which flagged months with missing, aggregated or questionable entries.

  3. Personal email communication from the Criminal Justice Information Services at the FBI, August 9, 2010 stated that “The Uniform Crime Reporting Program no longer creates tables for monthly variations.” Monthly tables are available by special request, however in such tables “agencies that did not provide monthly totals were removed from the calculations.”

  4. In his analyses, Dodge (1988) classifies crimes as highly seasonal (i.e., a deviation of at least 20 % above or below the overall average), moderately seasonal (a deviation averaging between 15 and 20 % from the mean), and demonstrating little or no seasonality (a deviation of less than 15 % from the average).

  5. While the overall shape of the relation remains in question (i.e., whether it is linear, J-shaped, U-shaped, or some other possibility), the theory specifically posits a direct relationship. In other words, it suggests that temperature acts at the level of the individual (Anderson 1989, p. 75).

  6. We use NCVS data made available through the National Archive of Criminal Justice Data at ICPSR (datasets 4699, 24644, 25141, 26382, and 28543): http://www.icpsr.umich.edu/icpsrweb/NACJD/NCVS/.

  7. Because we define incidents that occur in December, January, and February as “winter” incidents, the data include incidents from December, 1992 through November, 2008.

  8. Incidents reported to have occurred outside of the US are excluded from these analyses. “Series” incidents (victimizations that occur more than six times and for which the respondent is unable to provide details about each incident) are included but counted as one incident, and the month of the most recent incident is used to date the event. This decision was made because the data lack information about the month of occurrence of other events classified as part of the series victimization.

  9. For example, one-sixth of the sample is contacted in early January to report about incidents for the previous July–December period, and these households are subsequently interviewed in early July to report on incidents for the January–June period. Another one-sixth of the sample is contacted in early February to report about their experiences for the previous August to January period and they are subsequently re-interviewed in early August; and so on.

  10. Details about the exact day of the month on which the incident occurred are not obtained during the interview.

  11. We examine tri-monthly periods for a number of reasons. First, because of the relative rarity of violent victimization, the estimates become increasingly unstable when using smaller temporal windows (i.e., examining monthly or even bi-monthly rates), particularly when further disaggregated by age of victim or location of the incident. Second, this definition of seasons most closely matches the social definition of seasons in the United States, allowing us to empirically examine the presumed summer peaks and winter troughs in violence. Finally, defining seasons in this manner corresponds with the annual school calendar; the June–August season in the United States is generally the period of time in which students are not attending school.

  12. The rates are derived from an average of 43,439 interviews per season. The average number of interviews with persons 12–17, 18–24, and 25 above is 4,541, 4,423, and 34,475, respectively. It should also be noted that because of temporary changes in the administration of the survey, NCVS data for the year 2006 have been referred to as a “break-in-series” and thus not directly comparable with data from 2005 or 2007 (Rand 2008). We re-estimated all of our models with and without data from the year 2006 and the results were essentially the same, suggesting that even though data for 2006 produced some unusual annual rates, the seasonality in those rates maintained patterns that were consistent with other years. Visual inspection of the seasonal rate estimates confirms this as well.

  13. For example, if the change in the population between 2 years was +6 %, then the interpolated population values would increase .5 % each month.

  14. These percentages are obtained by comparing the coefficient for each season against the estimated summer rate (i.e., the constant). For example, −.33/5.56 × 100 = −6 % describes the average percent difference between winter and summer rates of serious violence.

  15. In the first-difference model for simple assault, the spring coefficient is positive and significant, and approximately half the size of the fall coefficient.

  16. The apparently greater difference between the observed and predicted rates in 2006 is related to the ‘break-in-series’ issue, discussed in note 12.

  17. When Newey-West standard errors are used to assess significance, the fall coefficient is significant at p = .07 (two-tail test). For serious violence among youth, the series is stationary only in first differences, suggesting that the winter rates are probably significantly higher than the summer rates.

  18. Each of the series reported in Table 3 is stationary in both levels and first-differences. The results based on Newey-West standard errors are similar, though the spring-summer difference in percent total violence reported is significant at p = .06 (two-tail test).

  19. Length of residence has been shown to be negatively related to neighborhood violence risk: Net of other individual- and neighborhood-level factors, youth who have lived in their neighborhoods for shorter periods of time are more likely to be victims of violent crime (Lauritsen 2003).

References

  • Amir M (1971) Patterns in forcible rape. University of Chicago Press, Chicago

    Google Scholar 

  • Anderson CA (1987) Temperature and aggression: effects on quarterly, yearly, and city rates of violent and nonviolent crime. J Pers Soc Psychol 52:1161–1173

    Article  Google Scholar 

  • Anderson CA (1989) Temperature and aggression: ubiquitous effects of heat on occurrence of human violence. Psychol Bull 106:74–96

    Article  Google Scholar 

  • Anderson CA, Bushman BJ, Groom RW (1997) Hot years and serious and deadly assault: empirical tests of the heat hypothesis. J Pers Soc Psychol 73:1213–1223

    Article  Google Scholar 

  • Baumer E, Wright R (1996) Crime seasonality and serious scholarship: a comment on Farrell and Pease. Br J Criminol 36:579–581

    Article  Google Scholar 

  • Binkley C (2010) Hot days, high crime? It’s matter of degrees. The Columbus (Ohio) Dispatch, 8 July. http://www.dispatch.com/live/content/local_news/stories/2010/07/08/hot-days-high-crime-its-matter-of-degrees.html?sid=101

  • Block CR (1983) How to handle seasonality: introduction to the detection and analysis of seasonal fluctuation in criminal justice time series. Statistical Analysis Center. NCJ 092928

  • Block CR (1984) Is crime seasonal?. Illinois Justice Information Authority, Chicago

    Google Scholar 

  • Bureau of Justice Statistics (n.d.) Survey methodology for criminal victimization in the United States. Available at: http://bjs.ojp.usdoj.gov/content/pub/pdf/ncvs_methodology.pdf, last revised in 2011. Accessed 30 May 2011

  • Carbone-Lopez K, Esbensen F, Brick BT (2010) Correlates and consequences of peer victimization: gender differences in direct and indirect forms of bullying. Youth Violence Juv Justice 8:332–350

    Article  Google Scholar 

  • Cheatwood D (1988) Is there a season for homicide? Criminology 26:287–306

    Article  Google Scholar 

  • Cohen J (1941) The geography of crime. Ann Am Acad Political Soc Sci 217:29–37

    Article  Google Scholar 

  • Cohen L, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44:588–608

    Article  Google Scholar 

  • Cohen J, Gorr W, Durso C (2003) Estimation of crime seasonality: a cross-sectional extension to time-series classical decomposition. Working paper, Heinz School of Public Policy and Management, Carnegie Mellon University. http://www.heinz.cmu.edu/research/132full.pdf

  • Cohn EG (1990) Weather and violent crime: a reply to Perry and Simpson, 1987. Environ Behav 22:280–294

    Article  Google Scholar 

  • Cohn EG, Rotton J (1997) Assault as a function of time and temperature: a moderator-variable time-series analysis. J Pers Soc Psychol 72:1322–1334

    Article  Google Scholar 

  • Criminal Justice Information Services, Federal Bureau of Information (2010) Personal email communication

  • Dodge RW (1988) The seasonality of crime victimization. US Department of Justice. NCJ-111033

  • Dodge RW, Lentzer HR (1980) Crime and seasonality. National crime survey report SD-NCS-N-15, NCJ-648 18

  • Falk GJ (1952) The influence of the seasons on the crime rate. J Crim Law Criminol Police Sci 43:199–213

    Article  Google Scholar 

  • Farrell G, Pease K (1994) Crime seasonality: domestic disputes and residential burglary in Merseyside, 1988–90. Br J Criminol 34:487–498

    Google Scholar 

  • Field S (1992) The effect of temperature on crime. Br J Criminol 32:340–351

    Google Scholar 

  • Finkelhor D, Ormrod RK (2001) Factors in the underreporting of crimes against juveniles. Child Maltreatment 6:219–229

    Article  Google Scholar 

  • Gorr W, Olligschlaeger A, Thompson Y (2000) Assessment of crime forecasting accuracy for deployment of police. Working paper, Heinz School of Public Policy and Management, Carnegie Mellon University. http://repository.cmu.edu/heinzworks/236/

  • Granger CWJ (1979) Seasonality: causation, interpretation, and implications. NBER chapters in: seasonal analysis of economic time series, pp 33–56. National Bureau of Economic Research, Inc. http://www.census.gov/ts/papers/Conference1978/Granger1978.pdf/

  • Greenberg DF (1977) Delinquency and the age structure of society. Contemporary Crises 1:189–223

    Article  Google Scholar 

  • Heller NB, Markland RE (1970) A climatological model for forecasting the demand for police service. J Res Crime Delinq 7:167–176

    Article  Google Scholar 

  • Hipp JR, Bauer DJ, Curran PJ, Bollen KA (2004) Crimes of opportunity or crimes of emotion? Testing two explanations of seasonal change in crime. Soc Forces 82:1333–1372

    Article  Google Scholar 

  • Jacob BA, Lefgren L (2003) Are idle hands the devil’s workshop? Incapacitation, concentration, and juvenile crime. Am Econ Rev 93:1560–1577

    Article  Google Scholar 

  • Keen J (2010) Cities brace for summer crime. USA Today, 30 June. http://www.usatoday.com/news/nation/2010-06-30-summer-violence_N.htm

  • Landau SF, Fridman D (1993) The seasonality of violent crime: the case of robbery and homicide in Israel. J Res Crime Delinq 30:163–191

    Article  Google Scholar 

  • Lauritsen JL (2003) How families and communities influence youth victimization. US Department of Justice, Office of Justice Programs, Office of Juvenile Justice and Delinquency Prevention, Washington, DC. NCJ 201629

  • Lombroso C (1911) Crime: its causes and remedies. Little, Brown and Company, Boston

    Google Scholar 

  • Maes M, Cosyns P, Meltzer H, De Meyer F, Peeters D (1993) Seasonality in violent suicide but not in nonviolent suicide or homicide. Am J Psychiatry 150:1380–1385

    Google Scholar 

  • Maltz MD (n.d.) The revised UCR data: Monthly UCR data from 1960 to 2004 [Data file]. http://cjrc.osu.edu/researchprojects/hvd/usa/ucrfbi/, last revised in 2010.

  • Maltz MD (1999) Bridging gaps in police crime data. US Department of Justice, Bureau of Justice Statistics, Washington, DC. NCJ 176365

  • Maltz MD, Targonski J (2002) A note on the use of county-level UCR data. J Quant Criminol 18:297–318

    Article  Google Scholar 

  • McCabe S (2011) D.C.’s murder rate plummets as temperatures soar. The Washington examiner, 26 July. http://washingtonexaminer.com/local/2011/07/dcs-murder-rate-plummets-temperatures-soar

  • McDowall D, Loftin C, Pate M (2012) Seasonal cycles in crime, and their variability. J Quant Criminol 28:389–410

    Article  Google Scholar 

  • McPheters LR, Stronge WB (1973) Testing for seasonality in reported crime data. J Crim Justice 1:125–134

    Article  Google Scholar 

  • Michael RP, Zumpe D (1983) Sexual violence in the United States and the role of season. Am J Psychiatry 140:883–886

    Google Scholar 

  • Perry JD, Simpson ME (1987) Violent crimes in a city: environmental determinants. Environ Behav 19:77–90

    Article  Google Scholar 

  • Posegate A (2010) Is summer weather prime for crime? The Washington Post, 25 June. http://voices.washingtonpost.com/capitalweathergang/2010/06/is_summer_weather_prime_for_cr.html

  • Quetelet A (1831/1984) Research on the propensity for crime at different ages. Anderson Publishing, Cincinnati

  • Radzinowicz L (1957) Sexual offences: a report to the Cambridge Department of Criminal Science. Macmillan, London

    Google Scholar 

  • Rand MR (2008) Criminal victimization, 2007: technical notes. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics, Washington, DC. NCJ 224390

  • Rock D, Greenberg DM, Hallmayer J (2003) Cyclical changes of homicide rates: a reanalysis of Brearley’s 1932 data. J Interpersonal Violence 18:942–955

    Article  Google Scholar 

  • Rotton J, Frey J (1985) Air pollution, weather and violent crimes: concomitant time-series analysis of archival data. J Pers Soc Psychol 49:1207–1220

    Article  Google Scholar 

  • Sampson RJ, Lauritsen JL (1994) Violent victimization and offending: individual-, situational-, and community-level risk factors. In: Reiss AJ Jr, Roth J (eds) Understanding and preventing violence: social influences, vol 3 (National Research Council.). National Academy Press, Washington, DC

  • Schwartz DC (1968) On the ecology of political violence: the long hot summer as a hypothesis. Am Behav Scientist 11:24–28

    Article  Google Scholar 

  • Snyder TD, Dillow SA (2010) Digest of education statistics 2009 (NCES 2010-013). National Center for Education Statistics, Institute of Education Sciences, US Department of Education. Washington, DC

  • Snyder HN, Sickmund M (1999) Juvenile offenders and victims: 1999 National report. Office of Juvenile Justice and Delinquency Prevention, Washington

    Google Scholar 

  • Stack S (1995) Temporal disappointment, homicide and suicide: an analysis of nonwhites and whites. Sociol Focus 28:313–328

    Article  Google Scholar 

  • Tennenbaum AN, Fink EL (1994) Temporal regularities in homicide: cycles, seasons and autoregression. J Quant Criminol 10:317–342

    Article  Google Scholar 

  • Truman JL, Rand MR (2010) Criminal victimization, 2009. US Department of Justice. NCJ 231327

  • Yan Y (2004) Seasonality of property crime in Hong Kong. Br J Criminol 44:276–283

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the editors and reviewers and Richard Rosenfeld for their useful comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kristin Carbone-Lopez.

Additional information

Kristin Carbone-Lopez and Janet Lauritsen contributed equally to the manuscript.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Carbone-Lopez, K., Lauritsen, J. Seasonal Variation in Violent Victimization: Opportunity and the Annual Rhythm of the School Calendar. J Quant Criminol 29, 399–422 (2013). https://doi.org/10.1007/s10940-012-9184-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-012-9184-8

Keywords

Navigation