CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201

Neighborhood Features and Physiological Risk: An Examination of Allostatic Load

Poor neighborhoods may represent a situation of chronic stress, and may therefore be associated with health-related correlates of stress. We examined whether lower neighborhood income would relate to higher allostatic load, or physiological well-being, through psychological, affective, and behavioral pathways. Using data from the Biomarker Project of the Midlife in the United States (MIDUS) study and the 2000 Census, we demonstrated that people living in lower income neighborhoods have higher allostatic load net of individual income. Moreover, findings indicate that this relation is partially accounted for by anxious arousal symptoms, fast food consumption, smoking, and exercise habits

Relationships between minimum alcohol pricing and crime during the partial privatization of a Canadian government alcohol monopoly

Objective: The purpose of this study was to estimate the independent effects of increases in minimum alcohol prices and densities of private liquor stores on crime outcomes in British Columbia, Canada, during a partial privatization of off-premise liquor sales. Method: A time-series cross-sectional panel study was conducted using mixed model regression analysis to explore associations between minimum alcohol prices, densities of liquor outlets, and crime outcomes across 89 local health areas of British Columbia between 2002 and 2010. Archival data on minimum alcohol prices, per capita alcohol outlet densities, and ecological demographic characteristics were related to measures of crimes against persons, alcohol-related traffic violations, and non–alcohol- related traffic violations. Analyses were adjusted for temporal and regional autocorrelation. Results: A 10% increase in provincial minimum alcohol prices was associated with an 18.81% (95% CI: ±17.99%, p < .05) reduction in alcohol-related traffic violations, a 9.17% (95% CI: ±5.95%, p < .01) reduction in crimes against persons, and a 9.39% (95% CI: ±3.80%, p < .001) reduction in total rates of crime outcomes examined. There was no significant association between minimum alcohol prices and non–alcohol-related traffic violations (p < .05). Densities of private liquor stores were not significantly associated with alcohol involved traffic violations or crimes against persons, though they were with non–alcohol-related traffic violations. Conclusions: Reductions in crime events associated with minimum-alcohol-price changes were more substantial and specific to alcohol-related events than the countervailing increases in densities of private liquor stores. The findings lend further support to the application of minimum alcohol prices for public health and safety objectives

Elevation dependency of mountain snow depth

Elevation strongly affects quantity and distribution patterns of precipitation and snow. Positive elevation gradients were identified by many studies, usually based on data from sparse precipitation stations or snow depth measurements. We present a systematic evaluation of the elevation snow depth relationship. We analyse areal snow depth data obtained by remote sensing for seven mountain sites near to the time of the maximum seasonal snow accumulation. Snow depths were averaged to 100 m elevation bands and then related to their respective elevation level. The assessment was performed at three scales: (i) the complete data sets (10 km scale), (ii) sub-catchments (km scale) and (iii) slope transects (100 m scale). We show that most elevation-snow depth curves at all scales are characterised through a single shape. Mean snow depths increase with elevation up to a certain level where they have a distinct peak followed by a decrease at the highest elevations. We explain this typical shape with a generally positive elevation gradient of snow fall that is modified by the interaction of snow cover and topography. These processes are preferential deposition of precipitation and redistribution of snow by wind, sloughing and avalanching. Furthermore, we show that the elevation level of the peak of mean snow depth correlates with the dominant elevation level of rocks (if present)

A Mixed-Method Analysis of Fatal Attacks on Police by Far-Right Extremists

Several recent high-profile homicides of police officers have brought increased attention to issues of far-right extremist violence in the United States. We still, however, know very little about why (and how) certain encounters between far-right extremists and police result in violence. To fill this research gap, we conduct a mixed-method analysis of far-right antipolice homicides based on quantitative and qualitative data from the U.S. Extremist Crime Database. We begin by categorizing cases based on key aspects of homicide storylines. We then comparatively analyze attributes of event precursor, transaction, and aftermath stages across four storyline categories. Finally, a case study is purposively selected to follow-up on each storyline category to better capture the nuances of fluid homicide processes. Our findings have important implications for identifying triggering events, escalation factors, and other situated sets of conditions and circumstances that contribute to deadly outcomes for police officers