Risk terrain modeling predicts child maltreatment

Abstract

As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.

Risk terrain modeling for monitoring illicit drugs markets across Bogota, Colombia

“…The validation metric proposed in this work is similar to the one presented by Daley et al (2016) and the predictive accuracy index (PAI) (Chainey et al 2008), which could be further calculated using our validation method. After the model is obtained using RTMDx, a raster layer of the model described by (2) is obtained using ArcGIS, this is the risk of existence of retail sale points.…”

“…Also, it is important to note the drawbacks of omitting the time dimension, since the distribution of events could change on a seasonal basis. Even though some these factors may affect the accuracy of RTM, it still outperforms other methodologies when mapping crime risk in complex environments (Drawve et al 2016;Daley et al 2016). This could be especially important for the case of Bogota where significant efforts in intelligence have been made during the past years (Garzón et al 2017).…”

“…Extensive work has been carried out in the past by applying RTM to a wide array of case studies. Daley et al (2016) applied RTM to evaluate the environmental factors thought to be more related to child maltreatment. Kennedy et al (2015) studied the events and environmental features that have spatial influence and thus generate vulnerability to violence and assaults.…”

Algorithmic Justice in Child Protection: Statistical Fairness, Social Justice and the Implications for Practice

“…There is not a single type of use, a single type of algorithm, uniform types of data, nor a single end user impacted by the use of algorithmic risk prediction tools in child protection. In terms of type of use, algorithmic tools can be used either to distribute preventive family support services, in child protection screening decision making, or in risk terrain profiling to predict spatially where child abuse reports might occur (Cuccaro-Alamin et al 2017;Daley et al 2016;van der Put et al 2017). The type of algorithm selected categorises data in algorithm-specific ways to generate graded recommendations or binary flags and can include decision trees or regression methods amongst others, with varying levels of transparency or opacity.…”

Cholera deaths in Soho, London, 1854: Risk Terrain Modeling for epidemiological investigations

“…Outside of crime applications, RTM has been applied in correctional settings dealing with a range of topics surrounding recidivism of parolees [16; 17], and to issues of terrorism and security [18; 19]. Other applications of RTM, perhaps most related to public health and our current research topic, involve matters of child abuse [20], domestic violence [21], residential evictions [22], homelessness [23], and traffic crashes [10; 24]. RTM is multifaceted and can be applied to a broad range of subject matters that have a spatial component.…”