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Crimes by Visitors Versus Crimes by Residents: The Influence of Visitor Inflows

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Abstract

Objectives

To disaggregate the crime impact of visitor inflows. There is increasing evidence that visitors can make a major contribution to levels of crime in a given neighbourhood: crimes by visiting offenders may add to those committed by local offenders, while visitors (and their property) may provide local offenders with additional opportunities for crime.

Methods

Using police-recorded crime data for a large Eastern Canadian city we determined whether individuals charged or chargeable for property and violent crimes were visitors or residents of census tracts (CT) where crimes had been committed. This information was combined with data from a large transportation survey, allowing us to estimate daily population flows into each CT for four purposes (work, shopping, recreation, and education). Negative binomial regression models including spatial lags were used.

Results

An increase in visitor inflow not only increases the number of visitors charged with crimes but also the number of local residents charged. These effects vary significantly by visit purpose: more infractions are committed in tracts where visits are for recreation and, to a lesser extent, for shopping. Findings for work and education are mixed.

Conclusions

One important implication of our results is that, because most studies of aggregate crime counts or rates fail to account for whether crimes have been committed by visitors or residents, previous research may have provided hasty, partial, or even erroneous explanations for crime concentrations.

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Notes

  1. It could be argued that the assumption is instead that mobility patterns are equally distributed across space and thus have negligible effects on aggregate counts and rates of crime but the literature and the analysis presented in this paper do not support this argument.

  2. According to Canada’s Uniform Crime Reporting reference manual, for an incident to be cleared other than by being charged, at least one accused must have been identified and there must be sufficient evidence to lay a charge in connection with the incident, but the accused has been processed by other means for one of the following reasons (in no particular order): complainant declines to lay charges, departmental discretion, diversionary program, reason beyond control of department, incident cleared by a lesser statute, incident cleared by other municipal/provincial/federal agency, and other reasons (such as suicide/death of the accused; accused less than 12 years old (under the age of criminal responsibility); and accused in a foreign country, cannot be returned).

  3. Twenty tracts did not appear in the crime data, although they existed at the time of collection. The Police Department did not provide an explanation for their absence but we suspect changes in tract delimitation between different census periods as a plausible cause.

  4. Unfortunately, the 2011 Canadian Census shifted policies and reduced the number of questions asked. A new voluntary survey (the National Household Survey) replaced the previous mandatory long census questionnaire. A direct consequence of this change is that substantial risk of non-response bias is expected and the validity of the 2011 data is controversial. Thus, 2006 data on residential mobility was used.

  5. Contrary to many previous studies, counts are used instead of rates. Because residential stability/mobility is treated as a dichotomy—the population is either stable or it is not—inclusion of both rates in the same statistical model would be meaningless; both proportions would perfectly mirror each other, for a total of 100%. However, there is a high but imperfect positive correlation (r = 0.614; p < 0.01) between counts, meaning that CTs with a large number of unstable residents tend to have a large number of stable residents as well. The use of counts allows us to capture the “trivial” effect of population size on crime (there are more infractions in more populated areas) but also to attempt to disentangle several propositions of social disorganization –a stable residential population prevents crime, an unstable population increases crime, or both.

  6. Models with spatial lags of outcomes (crime) were also conducted. As expected, outcome lag coefficients were all positive and significant, indicating spatial autocorrelation in the dependent variables. These models also provided slightly better predictions: the increase was more pronounced for local than visitor crime (a 3% increase of deviance vs a 10%). However, it did not alter parameter estimates for other variables.

  7. An indication of this is that residuals from all four models were not significantly autocorrelated in space. Moran’s I values for residuals are under 0.02, suggesting very low to inexistent spatial autocorrelation after the inclusion of spatial lags.

  8. Likelihood ratios and Chi square tests were computed using the “nbreg” command in Stata. That command also computes another goodness-of-fit measure, McFadden’s pseudo R-squared, that is less frequently presented because it does not have the same meaning than R-squared measure in OLS regression. As an indication, the models are associated with pseudo R-squared values between 0.04 (for local violent crime) and 0.10 (for visitor property crime). Negative binomial regression can also be conducted in Stata using the “glm” command. This procedure gives very similar parameter estimates but has the advantage of computing Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. These values are not presented here because both measures are useful for model selection but do not provide tests of the null hypothesis.

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Acknowledgements

The authors would like to thank Camille Faubert and Véronique Chadillon-Farinacci for their help with statistical analysis.

Author information

Authors and Affiliations

  1. School of Criminology, Université de Montréal, C.P. 6128, succursale Centre-ville, Montreal, QC, H3C 3J7, Canada

    Rémi Boivin

  2. International Centre for Comparative Criminology, Montreal, QC, Canada

    Rémi Boivin

  3. School of Criminal Justice, Texas State University, San Marcos, TX, USA

    Marcus Felson

Authors
  1. Rémi Boivin
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  2. Marcus Felson
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Correspondence to Rémi Boivin.

Appendix

Appendix

See Table 6.

Table 6 Correlations between variables (coefficient: Spearman’s rho)
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Boivin, R., Felson, M. Crimes by Visitors Versus Crimes by Residents: The Influence of Visitor Inflows. J Quant Criminol 34, 465–480 (2018). https://doi.org/10.1007/s10940-017-9341-1

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