Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method

Bouhana, N; Gill, Paul

doi:10.1186/s40163-021-00149-6

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…In a meta-analysis of 25 studies selected through a systematic review process, Marchment and Gill (2021) found that risk terrain modeling has a proven effect in predicting areas that are prone to high-risk events for driving under the influence (DWI), violent crimes, drug-related crimes, terrorism, and child maltreatment. Robbery studies consistently had higher RTM predictive accuracy ratings than other acquisitive crime types.…”

Section: Rtm and Anroc Measure Of The Physical Environmentmentioning

confidence: 99%

Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City

Ejiogu

2023

SAGE Open

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The structure and functions of neighborhoods determine the impact of measures used to estimate the distribution of street robbery across space and time. The risk of street robbery could vary between different sections of the area. Prior research has typically relied on one-dimensional analysis, which sparsely accounts for the hierarchical configuration of a neighborhood’s influence on spatial crime distribution. Much less is known about how the predictor variables moderate each other at different neighborhood levels. Data was collected from 13,789 US Census blocks ( N = 13,788) aggregated to administrative neighborhoods ( N = 278) to examine how neighborhood structure affects street robbery distribution at two spatial levels. A Risk Terrain Model was adopted to develop a physical environment risk measure—Aggregate Neighborhood Risk of Crime (ANROC), which, alongside sociodemographic risk factors, predicted the outcomes of the Negative Binomial General Linear Regression models. Study findings suggest that individual-level (block) and group (neighborhood) level predictors influenced street robbery. Both group-level physical environment and total population moderated the impact of the individual-level physical environment on street robbery incidents.

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Section: Rtm and Anroc Measure Of The Physical Environmentmentioning

confidence: 99%

Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City

Ejiogu

2023

SAGE Open

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“…This approach can help to reduce the risk and occurrence of crime in the vicinity. RTM has been widely applied to a variety of crime types and has consistently achieved good results [23].…”

Section: Crime Mappingmentioning

confidence: 99%

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Ding

2023

IJGI

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Crime is always one of the most important social problems, and it poses a great threat to public security and people. Accurate crime prediction can help the government, police, and citizens to carry out effective crime prevention measures. In this paper, the research on crime prediction is systematically reviewed from a variety of temporal and spatial perspectives. We describe the current state of crime prediction research from four perspectives (prediction content, crime types, methods, and evaluation) and focus on the prediction methods. According to various temporal and spatial scales, temporal crime prediction is divided into short-term prediction, medium-term prediction, and long-term prediction, and spatial crime prediction is divided into micro-, meso-, and macro-level prediction. Spatio-temporal crime prediction classification can be a permutation of temporal and spatial crime prediction classifications. A variety of crime prediction methods and evaluation metrics are also summarized, and different prediction methods and models are compared and evaluated. After sorting out the literature, it was found that there are still many limitations in the current research: (i) data sparsity is difficult to deal with effectively; (ii) the practicality, interpretability, and transparency of predictive models are insufficient; (iii) the evaluation system is relatively simple; and (iv) the research on decision-making application is lacking. In this regard, the following suggestions are proposed to solve the above problems: (i) the use of transformer learning technology to deal with sparse data; (ii) the introduction of model interpretation methods, such as Shapley additive explanations (SHAPs), to improve the interpretability of the models; (iii) the establishment of a set of standard evaluation systems for crime prediction at different scales to standardize data use and evaluation metrics; and (iv) the integration of reinforcement learning to achieve more accurate prediction while promoting the transformation of the application results.

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“…It also does not take near repeats into account and thus does not consider short-term risks at specific locations. A meta-analysis [23] found that RTM is an effective forecasting method for a number of different crime types. However, research has also demonstrated that RTM can be less accurate than machine learning methods that better model the complexity of interactions between input variables, such as Random Forest [24].…”

Section: A Brief Review Of Crime Prediction Modelsmentioning

confidence: 99%

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

Joshi

Curtis‐Ham²,

D'Ath

et al. 2021

IJGI

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A literature review of the important trends in predictive crime modeling and the existing measures of accuracy was undertaken. It highlighted the need for a robust, comprehensive and independent evaluation and the need to include complementary measures for a more complete assessment. We develop a new measure called the penalized predictive accuracy index (PPAI), propose the use of the expected utility function to combine multiple measures and the use of the average logarithmic score, which measures accuracy differently than existing measures. The measures are illustrated using hypothetical examples. We illustrate how PPAI could identify the best model for a given problem, as well as how the expected utility measure can be used to combine different measures in a way that is the most appropriate for the problem at hand. It is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand and to combine multiple measures. The measures proposed here go some way towards providing this ability. Further development along these lines is needed.

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Systematic review and meta-analysis of risk terrain modelling (RTM) as a spatial forecasting method

Cited by 9 publications

References 29 publications

Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City

Risk Terrain and Multilevel Modeling of Street Robbery Distribution in Baltimore City

A Systematic Review of Multi-Scale Spatio-Temporal Crime Prediction Methods

Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy

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