While previous studies have indicated a link between, for example, violent crime and temperature, it must be cautioned that, in general, annual periodicity of a variable does not necessarily imply that the variations are directly related to annual variations in other potential explanatory variables like climate there is a very real danger of concluding that a significant relationship exists between two independent periodic time series with the same period, simply because the two time series, even when somewhat shifted by some phase and with different sub-harmonics, will exhibit significant correlations (for a detailed discussion of this, see Reference ). For example, if factors such as climate variables, holidays, day-of-week, paydays, etc are significant leading indicators for temporal variations in crime predictive analytics methods for law enforcement may benefit from a model that incorporates these effects to more accurately and precisely predict temporal crime trends, such that policing resources can potentially be more optimally allocated.
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In such software packages, it is desirable to take into account what are known as “leading indicators” factors which might forecast significant upward trends in crime in the foreseeable future. An example of such software is the VALET package, developed by the authors and other collaborators, and used by several law enforcement agencies in the US.
In recent years, law enforcement agencies have come to increasingly rely on quantitative analytics software packages for predicting temporal and geospatial trends in crime. As described below, this analysis addresses each of these issues, and to the authors’ knowledege is the first analysis of this kind to address all of the issues simultaneously. Perhaps most important to the issue of robust predictive power of these models, model validation has been almost universally neglected in the literature, as indeed it has been in many other fields (see, for instance, References for a discussion of this topic). Many past analyses have also failed to properly take into account the significant auto-correlations in the data, or have failed to use statistical analysis and model selection methods appropriate for data with significant multicollinearities. There have been several past studies of the effect of various extrinsic factors on crime, such as the effects of temperature and other climate variables, air pollution, and holidays, but most have examined just one, or a few, potential extrinsic factors at a time, despite the fact that exogenous factors are likely multifactorial, and also potentially inter-correlated (e.g. Other theories posit that differences in routine activities and social interactions during different times of the year (or week) affect patterns in crime this can be due to seasonal variations in temperature or other weather variables, or due to holidays, or the work week and weekends. Largely because of this, some theories of aggressive crime posit that hot temperatures increase irritability, stress, and aggression. Necessary to the predictive performance of these tools are statistical models that take into account not only secular trends, but also various exogenous factors that might influence the incidence of different types crime for example, climate, daylight hours, day-of-week, and holidays and festivals.įor example, many types of crimes display marked annual seasonality, as do many climate variables. Note: Significant changes are calculated at a 90-percent confidence level.In recent years, predictive analytics and informatics software has become an essential tool for police forces nationwide to visualize and predict patterns of crime.
Hide table Employment change by industry with confidence intervals, May 2023, seasonally adjusted, in thousands Industry
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