Linking Property Crime Using Offender Crime Scene Behaviour: A Comparison of Methods

This study compared the ability of seven statistical models to distinguish between linked and unlinked crimes. The seven models utilized geographical, temporal, and Modus Operandi information relating to residential burglaries (n = 180), commercial robberies, (n = 118), and car thefts (n = 376). Model performance was assessed using Receiver Operating Characteristic (ROC) analysis and by examining the success with which the seven models could successfully prioritize linked over unlinked crimes. The regression-based and probabilistic models achieved comparable accuracy and were generally more accurate than the tree-based models tested in this study. The Logistic algorithm achievied the highest Area Under the Curve (AUC) for residential burglary (AUC=0.903) and commercial robbery (AUC=0.830) and the SimpleLogistic algorithm achieving the highest for car theft (AUC=0.820). The findings also indicated that discrimination accuracy is maximized (in some situations) if behavioural domains are utilized rather than individual crime scene behaviours, and that the AUC should not be used as the sole measure of accuracy in behavioural crime linkage research

Forecasting occurrence and intensity of geomagnetic activity with pattern‐matching approaches

Variability in near-Earth solar wind conditions gives rise to space weather which can have adverse effects on space- and ground-based technologies. Enhanced and sustained solar wind coupling with the Earth’s magnetosphere can lead to a geomagnetic storm. The resulting effects can interfere with power transmission grids, potentially affecting today’s technology-centred society to great cost. It is therefore important to forecast the intensity and duration of geomagnetic storms to improve decision making capabilities of infrastructure operators. The 150-year aaH geomagnetic index gives a substantial history of observations from which empirical predictive schemes can be built. Here we investigate the forecasting of geomagnetic activity with two pattern-matching forecast techniques, using the long aaH record. The techniques we investigate are an Analogue Ensemble Forecast (AnEn), and a Support Vector Machine (SVM). AnEn produces a probabilistic forecast by explicitly identifying analogues for recent conditions in the historical data. The SVM produces a deterministic forecast through dependencies identified by an interpretable machine learning approach. As a third comparative forecast, we use the 27-day recurrence model, based on the synodic solar rotation period. The methods are analysed using several forecast metrics and compared. All forecasts outperform climatology on the considered metrics and AnEn and SVM outperform 27-day recurrence. A Cost/Loss analysis reveals the potential economic value is maximised using the AnEn, but the SVM is shown as superior by the true skill score. It is likely that the best method for a user will depend on their need for probabilistic information and tolerance of false alarms