This study investigates whether individual-and area-level factors explain variation in the residence-to-crime distances (RC distance) for 10 offense types. Methods Five years of police data from Dallas, Texas, are analyzed using multilevel models (HLM/MLM). Results RC distances for Dallas offenders varied notably across offense types. Although several area characteristics such as residential instability and concentrated immigration were associated with the overall variance in RC distance, neither these nor the individual-level characteristics used in our models explained the offense-type variance in the RC distance. Conclusions Although individual-and neighborhood-level factors did not explain substantial variation in RC distance across the various offenses, neighborhood-level factors explained a significant portion of neighborhood-level variance. Other finding included a curvilinear effect of age on RC distance. The salience of these findings and their implications for future research and offender travel theory are discussed.

No abstract

No abstract

Predators can play important roles in structuring their communities through topdown effects on the distribution and abundance of their prey. Sharks are top predators in many marine communities, yet few studies have quantified those factors influencing their distribution and hunting behaviour. Here, we use location data from 340 predatory interactions between white sharks Carcharodon carcharias (Linnaeus), and Cape fur seals Arctocephalus pusillus pusillus (Schreber), data on associated environmental factors, and spatial analysis, including a novel application of geographic profiling -a tool originally developed to analyse serial crime -to investigate spatial patterns of shark attack and search behaviour at Seal Island in False Bay, South Africa. We found that spatial patterns of shark predation at this site are nonrandom. Sharks appear to possess a well-defined search base or anchor point, located 100 m seaward of the seal's primary island entry-exit point. This location is not where chances of intercepting seals are greatest and we propose it may represent a balance among prey detection, capture rates, and competition. Smaller sharks exhibit more dispersed prey search patterns and have lower predatory success rates than larger conspecifics, suggesting possible refinement of hunting strategy with experience or competitive exclusion of smaller sharks from the most profitable hunting locations. As many of the features of this system will be common to other instances of foraging, our conclusions and approach employed may have implications and applications for understanding how large predators hunt and for studying other predator-prey systems.

No abstract

Snook, Taylor, and Bennell, in ‘Geographic profiling: The fast, frugal, and accurate way’ (Applied Cognitive Psychology, January 2004, volume 18, pp. 105–121), suggest that by invoking two simple rules untrained individuals can perform geographic profiling tasks as accurately as sophisticated computer software. While the results are interesting in terms of geographic heuristics, the authors' reach conclusions unsupported by their data and methods. Though they claim to address ‘the ongoing debate about whether individuals can perform as well as actuarial techniques when confronted with real world, consequential decisions,’ their laboratory experiment bears little resemblance to the reality of criminal investigation. Major flaws exist with both data selection (the cases used may not have met the assumptions underlying geographic profiling, and they only involved a series of three locations, too low for pattern detection), and methods of analysis (nonlinear error was measured linearly, and computerized geographic profiling search strategies were distorted). Copyright © 2005 John Wiley & Sons, Ltd.

Summary1. Geographic profiling (GP) was originally developed as an analytical tool in criminology, where it uses the spatial locations of linked crimes (for example murder, rape or arson) to identify areas that are most likely to include the offender's residence. The technique has been extremely successful in this field and is now widely used by police forces and investigative agencies around the world. More recently, the same method has been applied to biological data, notably in spatial epidemiology, where it uses the locations of disease cases to identify infection sources: the identification of these sources is critical to control efforts of diseases such as malaria, since targeted intervention is more efficient and cost-effective than untargeted intervention. 2. Here, we solve the problem of identifying multiple sources, even when the number of sources is unknown -a requirement for many biological studies. We present a new, rigorous mathematical and computational method and show why previous Bayesian methods were often outperformed by the empirically developed criminal geographic targeting (CGT) algorithm used in criminology. 3. We use simulations and real-world examples to compare our model to both the CGT algorithm and to an existing Bayesian model. We demonstrate that our method combines the advantages of both previous methods, particularly in cases featuring large data sets and multiple sources. 4. Our approach provides an increase in search efficiency over other methods and is likely to lead to improved targeting of interventions and more efficient use of resources. We suggest that the Dirichlet process mixture (DPM) model provides a useful and practical tool for conservation biologists and epidemiologists that can be used to inform management decisions and public health policy.

Geographic profiling (GP) was originally developed as a statistical tool in criminology, where it uses the spatial locations of linked crimes (for example murder, rape or arson) to identify areas that are most likely to include the offender's residence. The technique has been successful in this field, and is now widely used by police forces and investigative agencies around the world. Here, we show that this novel technique can also be used to identify source populations of invasive species, using their current locations as input, as a prelude to targeted control measures. Our study has two main parts. In the first, we use computer simulations to compare GP to other simple measures of spatial central tendency (centre of minimum distance, spatial mean, spatial median), as well as to a more sophisticated single parameter kernel density model. GP performs significantly better than any of these other approaches. In the second part of the study, we analyse historical data from the Biological Records Centre (BRC) for 53 invasive species in Great Britain, ranging from marine invertebrates to woody trees, and from a wide variety of habitats (including littoral habitats, woodland and man‐made habitats). For 52 of these 53 data sets, GP outperforms spatial mean, spatial median and centre of minimum distance as a search strategy, particularly as the number of sources (or potential sources) increases. We analyse one of these data sets, for Heracleum mantegazzianum, in more detail, and show that GP also outperforms the kernel density model. Finally, we compare fitted parameter values between different species, groups and habitat types, with a view to identifying general values that might be used for novel invasions where data are lacking. We suggest that geographic profiling could potentially form a useful component of integrated control strategies relating to a wide variety of invasive species.