Spatial point process via regularisation modelling of ambulance call risk
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This study investigates the spatial distribution of emergency alarm call events to identify spatial covariates associated with the events and discern hotspot regions for the events. The study is motivated by the problem of developing optimal dispatching strategies for prehospital resources such as ambulances. To achieve our goals, we model the spatially varying call occurrence risk as an intensity function of an inhomogeneous spatial Poisson process that we assume is a log-linear function of some underlying spatial covariates. The spatial covariates used in this study are related to road network coverage, population density, and the socio-economic status of the population in Skellefteå, Sweden. A new heuristic algorithm has been developed to select an optimal estimate of the kernel bandwidth in order to obtain the non-parametric intensity estimate of the events and to generate other covariates. Since we consider a large number of spatial covariates as well as their products, and since some of them may be strongly correlated, lasso-like elastic-net regularisation has been used in the log-likelihood intensity modeling to perform variable selection and reduce variance inflation from overfitting and bias from underfitting. As a result of the variable selection, the fitted model structure contains individual covariates of both road network and demographic types. We discovered that hotspot regions of calls have been observed along dense parts of the road network. Evaluation of the model also suggests that the estimated model is stable and can be used to generate a reliable intensity estimate over the region, which can be used as an input in the problem of designing prehospital resource dispatching strategies.
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