A Bayesian Approach to Modelling Subnational Spatial Dynamics of Worldwide Non-State Terrorism, 2010–2016

Python, André; Illian, Janine B.; Jones‐Todd, Charlotte M.; Blangiardo, Marta

doi:10.1111/rssa.12384

Cited by 29 publications

(27 citation statements)

References 132 publications

(262 reference statements)

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“…For instance, different types of response variables may be considered, such as spatiotemporal log-Gaussian Cox processes as considered in Yuan et al (2017). Similarly, models for data on a larger spatial scale such as global data may be fitted directly on the surface of the Earth without the need for a projection into two-dimensional space as applied in Python et al (2016) to model global terrorism in space and time. Using the joint modelling approach these models may be extended to a multispecies or to a multievent situation.…”

Section: Discussionmentioning

confidence: 99%

A Spatiotemporal Multispecies Model of a Semicontinuous Response

Jones‐Todd

Swallow

Illian

et al. 2017

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary As accessible and potentially vulnerable species high up in the food chain, birds are often used as indicator species to highlight changes in ecosystems. This study focuses on multiple spatially dependent relationships between a raptor (sparrowhawk), a potential prey species (house sparrow) and a sympatric species (collared doves) in space and time. We construct a complex spatiotemporal latent Gaussian model to incorporate both predator–prey and sympatric relationships, which is novel in two ways. First, different types of species interactions are represented by a shared spatiotemporal random effect, which extends existing approaches to multivariate spatial modelling through the use of a joint latent modelling approach. Second, we use a delta–gamma model to capture the semicontinuous nature of the data to model the binary and continuous sections of the response jointly. The results indicate that sparrowhawks have a localized effect on the presence of house sparrows, which could indicate that house sparrows avoid sites where sparrowhawks are present.

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Section: Discussionmentioning

confidence: 99%

A Spatiotemporal Multispecies Model of a Semicontinuous Response

Jones‐Todd

Swallow

Illian

et al. 2017

Journal of the Royal Statistical Society Series C: Applied Statistics

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“…In general, MCMC will take hours or days in order to successfully simulate from the posterior making the computational cost of fitting multiple process models extremely high. In Python et al (2016), terrorism data was fit using a grid over the entire planet using INLA, though without self-excitation in the model.…”

Section: Self-excitingmentioning

confidence: 99%

“…We will make the simplifying assumption that both of these are static over time. Previous statistical analysis on terrorism considered macro level covariates, such as democracy in Python et al (2016) that differ country to country but would not change within a single country as analyzed here. Other studies considered more micro level covariates such as road networks that were found to be statistically related to terrorism in Braithwaite and Johnson (2015).…”

Section: Spatio-temporal Diffusion Of Violence Inmentioning

confidence: 99%

“…In modeling terrorism or crime data one possibility is to use an extremely general spatio-temporal process model to capture variance not explained through the use of covariates. For example Python et al (2016) use a Matern class covariance function over space and an AR(1) process over time. They then use covariates to test the impact of infrastructure, population, and governance.…”

mentioning

confidence: 99%

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Modeling and estimation for self-exciting spatio-temporal models of terrorist activity

Clark¹,

Dixon²

2018

Ann. Appl. Stat.

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Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely. The spatio-temporal diffusion is placed, as a matter of convenience, in the process model allowing for straightforward estimation of the diffusion parameters through Bayesian techniques. However, this method of modeling does not allow for the existence of self-excitation, or a temporal data model dependency, that has been shown to exist in criminal and terrorism data. In this manuscript we will use existing theories on how violence spreads to create models that allow for both spatio-temporal diffusion in the process model as well as temporal diffusion, or self-excitation, in the data model. We will further demonstrate how Laplace approximations similar to their use in Integrated Nested Laplace Approximation can be used to quickly and accurately conduct inference of self-exciting spatiotemporal models allowing practitioners a new way of fitting and comparing multiple process models. We will illustrate this approach by fitting a self-exciting spatio-temporal model to terrorism data in Iraq and demonstrate how choice of process model leads to differing conclusions on the existence of self-excitation in the data and differing conclusions on how violence spread spatially-temporally in that country from 2003-2010. 1. Introduction. A typical spatio-temporal model consists of three levels, a data model, a process model, and a parameter model. A common way to model data then is to assume Y (·), is conditionally independent given the process model X(·). For example, if observations take place at aerial regions, s i , at discrete time periods, t, and Y (s i , t) are counts, a common model is Y (s i , t)|X(s i , t) ∼ Pois(exp(X(s i , t))). The spatio-temporal diffusion structure is commonly then placed on the process model which commonly is assumed to have a Gaussian joint distribution of X ∼ Gaus(0, Q −1 (θ)). The majority of analysis of these models is done using Bayesian techniques requiring a further parameter model for θ. The challenge in these models

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“…Early analyses explored these by using routine data and methods, novel at the time, that tweaked the existing multiple‐regression methods, by incorporating a measure of distance (Pocock et al ., ). Further work has evolved to be much more explicit about the spatial properties of the mapping and has been applied in diverse areas including forestry and terrorism (de Rivera et al ., ; Python et al ., ).…”

Section: Current Challengesmentioning

confidence: 99%

Pigeonholes and Mustard Seeds: Growing Capacity to use Data for Society

Ashby

2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary The Royal Statistical Society was founded to address social problems ‘through the collection and classification of facts’, leading to many developments in the collection of data, the development of methods for analysing them and the development of statistics as a profession. Nearly 200 years later an explosion in computational power has led, in turn, to an explosion in data. We outline the challenges and the actions needed to exploit those data for the public good, and to address the step change in statistical skills and capacity development necessary to enable our vision of a world where data are at the heart of understanding and decision making.

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A Bayesian Approach to Modelling Subnational Spatial Dynamics of Worldwide Non-State Terrorism, 2010–2016

Cited by 29 publications

References 132 publications

A Spatiotemporal Multispecies Model of a Semicontinuous Response

A Spatiotemporal Multispecies Model of a Semicontinuous Response

Modeling and estimation for self-exciting spatio-temporal models of terrorist activity

Pigeonholes and Mustard Seeds: Growing Capacity to use Data for Society

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