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Research Papers

  • From the Special Section on Crime Forecasting, International Journal of Forecasting, Volume 19, No. 4 (October-November 2003):

Gorr, W.L. and R. Harries, “Introduction to crime forecasting,” pp. 551-555.

This short paper introduces the six papers comprising the Special Section on Crime Forecasting. A longer title for the section could have been ‘‘Forecasting crime for policy and planning decisions and in support of tactical deployment of police resources.’’ Crime forecasting for police is relatively new. It has been made relevant by recent criminological theories, made possible by recent information technologies including geographic information systems (GIS), and made desirable because of innovative crime management practices. While focused primarily on the police component of the criminal justice system, the six papers provide a wide range of forecasting settings and models including UK and US jurisdictions, long- and short-term horizons, univariate and multivariate methods, and fixed boundary versus ad hoc spatial cluster areal units for the space and time series data. Furthermore, the papers include several innovations for forecast models, with many driven by unique features of the problem area and data.

  1. Harries, R., “Modelling and predicting recorded property crime trends in England and Wales – a retrospective,” pp. 557-566.

In 1999 the Home Office published, for the first time ever, 3-year ahead projections of property crime in England and Wales. The projections covered the period 1999–2001 and indicated strong upward pressure after five full years of falling crime. This pressure was generated by three factors: the number of young men in the general population, the state of the economy and the fact that property crime appeared to be well below its underlying trend level. The projections received a mixed response, with some agreeing that crime was set to rise while questioning the scale of any increase, to others who doubted the value of this type of econometric modeling. In fact, property crime did increase in 1999, although not at the rate suggested by the models—and indeed levels of burglary continued to fall. This paper addresses some of the reasons for this disparity as well as considering various criticisms of the Home Office approach.

  1. Deadman, D., “Forecasting residential burglary,” pp. 567-578.

Following the work of Dhiri et al. [Modeling and predicting property crime trends. Home Office Research Study 198 (1999). London: HMSO] at the Home Office predicting recorded burglary and theft for England and Wales to the year 2001, econometric and time series models were constructed for predicting recorded residential burglary to the same date. A comparison between the Home Office econometric predictions and the less alarming econometric predictions made in this paper identified the differences as stemming from the particular set of variables used in the models. However, the Home Office and one of our econometric models adopted an error correction form which appeared to be the main reason why these models predicted increases in burglary. To identify the role of error correction in these models, time series models were built for the purpose of comparison, all of which predicted substantially lower numbers of residential burglaries. The years 1998–2001 appeared to offer an opportunity to test the utility of error correction models in the analysis of criminal behavior. Subsequent to the forecasting exercise carried out in 1999, recorded outcomes have materialized, which point to the superiority of time series models compared to error correction models for the short-run forecasting of property crime. This result calls into question the concept of a long-run equilibrium relationship for crime.

  1. Gorr, W.L., A. Olligschlaeger, and Y. Thompson, “Short-term forecasting of crime,” pp. 579-594. >

The major question investigated is whether it is possible to accurately forecast selected crimes 1 month ahead in small areas, such as ¨ police precincts. In a case study of Pittsburgh, PA, we contrast the forecast accuracy of univariate time series models with naıve methods commonly used by police. A major result, expected for the small-scale data of this problem, is that average crime count by precinct is the major determinant of forecast accuracy. A fixed-effects regression model of absolute percent forecast error shows that such counts need to be on the order of 30 or more to achieve accuracy of 20% absolute forecast error or less. A second major result is that practically any model-based forecasting approach is vastly more accurate than current police practices. Holt exponential smoothing with monthly seasonality estimated using city-wide data is the most accurate forecast model for precinct-level crime series.

  1. Felson, M. and E. Poulsen, “Simple indicators of crime by time of day,” pp. 595-601.

Crime varies greatly by hour of day—more than by any other variable. Yet numbers of cases declines greatly when fragmented into hourly counts. Summary indicators are needed to conserve degrees of freedom, while making hourly information available for description and analysis. This paper describes some new indicators that summarize hour-of-day variations. A basic decision is to pick the first hour of the day, after which summary indicators are easily defined. These include the median hour of crime, crime quartile minutes, crime’s daily timespan, and the 5-to-5 share of criminal activity; namely, that occurring between 5:00 AM and 4:59 PM. Each summary indicator conserves cases while offering something suitable to forecast.

  1. Liu, H. and D.E. Brown, “Criminal incident prediction using a point-patterb-based density model,” pp. 605-622.

Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or ‘‘hot spots’’ observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space–time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA.

  1. Corcoran, J.J., I.D. Wilson, and A. Ware, “Predicting the geo-temporal variations of crime and disorder,” pp. 624-634.

Traditional police boundaries—precincts, patrol districts, etc.—often fail to reflect the true distribution of criminal activity and thus do little to assist in the optimal allocation of police resources. This paper introduces methods for crime incident forecasting by focusing upon geographical areas of concern that transcend traditional policing boundaries. The computerized procedure utilizes a geographical crime incidence-scanning algorithm to identify clusters with relatively high levels of crime (hot spots). These clusters provide sufficient data for training artificial neural networks (ANNs) capable of modeling trends within them. The approach to ANN specification and estimation is enhanced by application of a novel and noteworthy approach, the Gamma test (GT).

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Working Papers and Technical Reports

Cohen, J., C. K. Durso, and W. L. Gorr, "Estimation of crime seasonality: a cross-sectional extension to time series classical decomposition," Heinz School Working Paper, 2003-18.

Reliable estimates of crime seasonality are valuable for law enforcement and crime prevention. Seasonality affects many police decisions from long-term reallocation of uniformed officers across precincts to short-term targeting of patrols for hot spots and serial criminals. This paper shows that crime seasonality is a small-scale, neighborhood-level phenomenon. In contrast, the vast literature on crime seasonality has almost exclusively examined crime data aggregations at the city or even larger scales. Spatial heterogeneity of crime seasonality, however, often gives rise to opposing seasonal patterns in different kinds of neighborhoods, canceling out seasonality at the city-wide level. Thus past estimates of crime seasonality have vastly underestimated the magnitude and impact of the phenomenon. We present a model for crime seasonality that extends classical decomposition of time series based on a multivariate, cross-sectional, fixed-effects model…

Gorr, W.L. and A.M. Olligschlaeger, Final project report crime hot spot forecasting: modeling and comparative evaluation, NIJ Grant 98-IJ-CX-K005, 2002.

This report is a detailed summary of early work on time-series-based crime forecasting, based on Pittsburgh, Pennsylvania crime data. It provides a comprehensive test of univariate and multivariate time series methods for one-month-ahead crime forecasts for use in COMPSTAT meetings or other organizational contexts for tactical deployment of police resources. Results indicate that seasonality and time-space-lagged leading indicators play important roles in accurately forecasting crime. The strongest determinant of high forecast accuracy is the average crime volume in individual time series for univariate methods, with at least 35 or more crime needed per month.


Olligschlager's Dissertation

One of the factors leading to increased attention to crime forecasting in the U.S. was the completion of Andreas M. Olligschlaeger's seminal dissertation in 1997, Spatial Analysis of Crime Using GIS-Based Data: Weighted Spatial Adaptive Filtering and Chaotic Cellular Forecasting with Applications to Street Level Drug Markets.

Presentations by Olligschlaeger at the second and third Crime Mapping Research Conferences held by the National Institute of Justice showed that short-term, leading indicator models could forecast crime in small areas with reasonable accuracy. About this time, police departments across the country were having big successes in mapping real-time crime data, and were thus primed for the next step of one-month-ahead crime forecasts.

Olligschlaeger used a good experimental design, a large crime space/time data sample, spatial data processing using a geographic information system based on uniform grid cells covering a police jurisdiction, and comparison of alternative methods including simple and advanced.