Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach

Quick, Matthew

doi:10.1007/s10109-019-00305-2

Cited by 12 publications

(5 citation statements)

References 75 publications

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“…These results suggest that spatial influence on crime might have a scale-free organization, where no particular unit of organization emerges as particularly important. This interplay of scales, emerging from diverse mechanisms of social interaction is well known 31,33,34,59 ; this study underscores the importance of these multi-scale processes to determine the optimal influence neighborhoods that vary across the city. Also, the influence of crime seems to behave as a diffusive process only on average, hence is not very useful for prediction of individual events at specific locations (See Fig.…”

Section: Discussionmentioning

confidence: 80%

Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks

Li,

Huang,

Evans

et al. 2019

Preprint

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Large-scale trends in urban crime and global terrorism are well-predicted by socio-economic drivers 1-3 , but focused, event-level predictions have had limited success 4-8 . Standard machine learning approaches are promising 9 , but lack interpretability, are generally interpolative, and ineffective for precise future interventions with costly and wasteful false positives. Such attempts have neither adequately connected with social theory, nor analyzed disparities between urban crime and differentially motivated acts of societal violence such as terrorism. Thus, robust eventlevel predictability is still suspect, and policy optimization via simulated interventions remains unexplored. Here, we are introducing Granger Network inference as a new forecasting approach for individual infractions with demonstrated performance far surpassing past results, yet transparent enough to validate and extend social theory. Considering the problem of predicting crime in the City of Chicago, we achieve an average AUC of ≈ 90% for events predicted a week in advance within spatial tiles approximately 1000 ft across. Instead of pre-supposing that crimes unfold across contiguous spaces akin to diffusive systems 7,8 , we learn the local transport rules from data. As our key insights, we uncover indications of suburban bias 10-16 -how law-enforcement response is modulated by socio-economic contexts with disproportionately negative impacts in the inner city -and how the dynamics of violent and property crimes co-evolve and constrain each other -lending quantitative support to controversial pro-active policing policies 17-20 . To demonstrate broad applicability to spatio-temporal phenomena, we analyze terror attacks in the middle-east in the recent past, and achieve an AUC of ≈ 80% for predictions made a week in advance, and within spatial tiles measuring approximately 120 miles across. We conclude that while crime operates near an equilibrium quickly dissipating perturbations, terrorism does not. Indeed terrorism aims to destabilize social order, as shown by its dynamics being susceptible to run-away increases in event rates under small perturbations.

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Section: Discussionmentioning

confidence: 80%

Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks

Li,

Huang,

Evans

et al. 2019

Preprint

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“…Ref. [28] proposed a multi-level model to explore the spatial-temporal patterns of crime in different spatial scales of area. They provide guidance for the construction of cognitive models of ship behavior.…”

Section: Multi-scale Modeling Of Trajectorymentioning

confidence: 99%

Semantic Modeling of Ship Behavior in Cognitive Space

Song

Wen

Tao

et al. 2022

JMSE

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Ship behavior is the semantic expression of corresponding trajectory in spatial-temporal space. The intelligent identification of ship behavior is critical for safety supervision in the waterborne transport. In particular, the complicated behavior reflects the long-term intentions of a ship, but it is challenging to recognize it automatically for computers without a proper understanding. For this purpose, this study provides a method to model the behavior for computers from the perspective of knowledge modeling that is explainable. Based on our previous work, a semantic model for ship behavior representation is given considering the multi-scale features of ship behavior in cognitive space. Firstly, the multi-scale features of ship behavior are analyzed in spatial-temporal dimension and semantic dimension individually. Then, a method for multi-scale behaviors modeling from the perspective of semantics is determined, which divides the behavior scale into four sub-scales in cognitive space, considering spatial and temporal dimensions: action, activity, process, and event. Furthermore, an ontology model is introduced to construct the multi-scale semantic model for ship behavior, where behaviors with different semantic scales are expressed using the functions of ontology from a microscopic perspective to a macroscopic perspective consecutively. To validate the model, a case study is conducted in which ship behavior with different scales occurred in port water areas. Typical behaviors, which include leveraging the axioms expression and semantic web rule language (SWRL) of the ontology, are then deduced using a reasoner, such as Pellet. The results show that the model is reasonable and feasible to represent multi-scale ship behavior in various scenarios and provides the potential to construct a smart supervision network for maritime authorities.

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“…However, in some instances it may make sense to consider an additive measurement error structure, represented as, * = + , where ( ) ≠ 0 if the errors are systematic. This is the case when crime rates are log-transformed; a common strategy used to normalise their often right-skewed distribution (Sutherland et al, 2013;Whitworth, 2011), to interpret effects in relative terms (Goulas & Zervoyianni, 2013;Witt & Witte, 2000), or as a result of employing generalised linear models where logs are used as the link function, such as Poisson models (Quick, 2019;Sampson et al, 1997). Crucially, log-transforming crime rates also has the effect of transforming the observed multiplicative measurement error into an additive mechanism, since: log( * ) = log( ) = log( ) + log( ).…”

Section: Prevalence and Nature Of Measurement Error In Police Recordementioning

confidence: 99%

The impact of measurement error in models using police recorded crime rates

Pina-Sánchez¹,

Buil‐Gil²,

Brunton‐Smith³

et al. 2021

Preprint

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Objectives: Assess the extent to which measurement error in police recorded crime rates impact the estimates of regression models exploring the causes and consequences of crime.Methods: We focus on linear models where crime rates are included either as the response or as an explanatory variable, in their original scale, or log-transformed. Two measurement error mechanisms are considered, systematic errors in the form of under-recorded crime, and random errors in the form of recording inconsistencies across areas. The extent to which such measurement error mechanisms impact model parameters is demonstrated algebraically, using formal notation, and graphically, using simulations.Results: Most coefficients and measures of uncertainty from models where crime rates are included in their original scale are severely biased. However, in many cases, this problem could be minimised, or altogether eliminated by log-transforming crime rates. This transforms the multiplicative measurement error observed in police recorded crime rates into a less harmful additive mechanism.Conclusions: The validity of findings from regression models where police recorded crime rates are used in their original scale is put into question. In interpreting the large evidence base exploring the effects and consequences of crime using police statistics we urge researchers to consider the biasing effects shown here. Equally, we urge researchers to log-transform crime rates before they are introduced in statistical models.

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Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach

Cited by 12 publications

References 75 publications

Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks

Long-range Event-level Prediction and Response Simulation for Urban Crime and Global Terrorism with Granger Networks

Semantic Modeling of Ship Behavior in Cognitive Space

The impact of measurement error in models using police recorded crime rates

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