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This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00-03:59) and highest in the evening (20:00-21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted. [ABSTRACT FROM AUTHOR]

Background: Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. Methods: This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. Results: For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. Conclusions: This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity. [ABSTRACT FROM AUTHOR]

Conventional methods used to identify crime hotspots at the small-area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small-area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area-specific violent crime trends for small-area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area-specific trends in the north and decreasing area-specific trends in the southeast. Posterior probabilities of area-specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective. [ABSTRACT FROM AUTHOR]

Plasma membrane neurotransmitter transporters are a family of integral membrane proteins, found on both neurons and glia, that have the capacity to influence neuronal signaling through a number of mechanisms including transmitter reuptake and ionic flux. Clinically, these proteins are of interest because their dysfunction is associated with several neurological and psychiatric disorders, and because they are the targets of many drugs of abuse and therapy. In this review, the authors focus on one of the more recent, fascinating discoveries about neurotransmitter transporters; namely, that transporter function is regulated by altering the number of transporters on the cell surface. These data suggest that transporter expression is in continual flux and that transporters respond to their environment in an effort to maintain baseline transmitter levels in the brain. The authors examine the mechanisms underlying changes in transporter number, discuss clinical disorders that are correlated with transporter expression, and suggest that controlling transporter redistribution may be a future therapeutic strategy for disorders related to abnormal transmitter levels. NEUROSCIENTIST 6(3):199-207, 2000 [ABSTRACT FROM AUTHOR]

Objective: This study examines the association between community-level marginalization and emergency room (ER) wait time in Ontario., Methods: Data sources included ER wait time data and Ontario Marginalization Index scores. Linear regression models were used to quantify the association., Results: A positive association between total marginalization and overall, high-acuity and low-acuity ER wait time was found. Considering specific marginalization dimensions, we found positive associations between residential instability and ER wait time and negative associations between dependency and ER wait time., Conclusions: Reductions in community-level marginalization may impact ER wait time. Future studies using individual-level data are necessary., (Copyright © 2020 Longwoods Publishing.)