arXiv subjects

Raiha Browning

Publications and source records attributed to Raiha Browning.

3 recordsLinked to original sources

arXiv2026

Bayesian spatiotemporal modelling of political violence and conflict events using discrete-time Hawkes processes

The monitoring of conflict risk in the humanitarian sector is largely based on simple historic averages. The overarching goal of this work is to assess the potential for using a more statistically rigorous approach to monitor the risk of political violence and conflict events in practice, and thereby improve our understanding of their temporal and spatial patterns, to inform preventative measures. In particular, a Bayesian, spatiotemporal variant of the Hawkes process is fitted to data gathered by the Armed Conflict Location and Event Data (ACLED) project to obtain sub-national estimates of conflict risk in South Asia over time and space. Our model can effectively estimate the risk level of these events within a statistically sound framework, with a more precise understanding of uncertainty than was previously possible. The model also provides insights into differences in behaviours between countries and conflict types. We also show how our model can be used to monitor short and long term trends, and that it is more stable and robust to outliers compared to current practices that rely on historical averages.

stat.AP↗

arXiv2023

Data-driven recommendations for enhancing real-time natural hazard warnings, communication, and response

The effectiveness and adequacy of natural hazard warnings hinges on the availability of data and its transformation into actionable knowledge for the public. Real-time warning communication and emergency response therefore need to be evaluated from a data science perspective. However, there are currently gaps between established data science best practices and their application in supporting natural hazard warnings. This Perspective reviews existing data-driven approaches that underpin real-time warning communication and emergency response, highlighting limitations in hazard and impact forecasts. Four main themes for enhancing warnings are emphasised: (i) applying best-practice principles in visualising hazard forecasts, (ii) data opportunities for more effective impact forecasts, (iii) utilising data for more localised forecasts, and (iv) improving data-driven decision-making using uncertainty. Motivating examples are provided from the extensive flooding experienced in Australia in 2022. This Perspective shows the capacity for improving the efficacy of natural hazard warnings using data science, and the collaborative potential between the data science and natural hazards communities.

cs.CY↗

arXiv2022

A flexible, random histogram kernel for discrete-time Hawkes processes

Hawkes processes are a self-exciting stochastic process used to describe phenomena whereby past events increase the probability of the occurrence of future events. This work presents a flexible approach for modelling a variant of these, namely discrete-time Hawkes processes. Most standard models of Hawkes processes rely on a parametric form for the function describing the influence of past events, referred to as the triggering kernel. This is likely to be insufficient to capture the true excitation pattern, particularly for complex data. By utilising trans-dimensional Markov chain Monte Carlo inference techniques, our proposed model for the triggering kernel can take the form of any step function, affording significantly more flexibility than a parametric form. We first demonstrate the utility of the proposed model through a comprehensive simulation study. This includes univariate scenarios, and multivariate scenarios whereby there are multiple interacting Hawkes processes. We then apply the proposed model to several case studies: the interaction between two countries during the early to middle stages of the COVID-19 pandemic, taking Italy and France as an example, and the interaction of terrorist activity between two countries in close spatial proximity, Indonesia and the Philippines, and then within three regions of the Philippines.

stat.ME↗