Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

Bayesian Space-Time Analysis of Brain Cancer Incidence in Southern Ontario, Canada: 2010-2013

Canada has one of the highest incidence rates of brain cancer in the world. This study investigates the space–time variation of brain cancer risk across Southern Ontario, Canada. A Bayesian spatio-temporal regression model is used to estimate the relative risk of brain cancer in the 12 spatial health units of Southern Ontario over a four-year period (2010–2013). This work also explores the association between brain cancer and two potential risk factors: traumatic head injury (THI) and excess body fat (EBF). Across all areal units from 2010–2013, results show that the relative risk of brain cancer ranged from 0.83 (95% credible interval (CI) 0.74–0.91) to 1.26 (95% CI 1.13–1.41). Over the years, the eastern and western health units had persistently higher risk levels compared to those in the central areas. Results suggest that areas with elevated THI rates and EBF levels were also potentially associated with higher brain cancer relative risk. Findings revealed that the mean temporal trend for cancer risk progression in the region smoothly decreased over time. Overall, 50% of the health units displayed area-specific trends which were higher than the region’s average, thus indicating a slower decrease in cancer rates for these areas in comparison to the mean trend.

Measuring spatio-temporal accessibility to emergency medical services through big GPS data

Medical accessibility is an important indicator for evaluating the effectiveness of public health services. However, the previous medical accessibility studies mainly focus on spatial accessibility without considering temporal variation in population distribution which is significant for evaluating access to emergency medical service (EMS). This paper proposes a model of spatio-temporal accessibility to EMS called ST-E2SFCA based on adapting the enhanced two-step floating catchment area (E2SFCA) method. We apply our method to the greater Tokyo area for a large volume of GPS dataset with millions of users and compare the accessibility difference over space and time. To evaluate our model, we also analyze the distinction of our model over different weight sets and compare the performance of ST-E2SFCA with the traditional E2SFCA. The result shows that our method can illustrate the temporal difference and is suitable for measuring the spatio-temporal accessibility to EMS, thus can guide the hospital location selection and urban planning.

Spatio-Temporal Analysis of Suicide-Related Emergency Calls

Considerable effort has been devoted to incorporate temporal trends in disease mapping. In this line, this work describes the importance of including the effect of the seasonality in a particular setting related with suicides. In particular, the number of suicide-related emergency calls is modeled by means of an autoregressive approach to spatio-temporal disease mapping that allows for incorporating the possible interaction between both temporal and spatial effects. Results show the importance of including seasonality effect, as there are differences between the number of suicide-related emergency calls between the four seasons of each year.