{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T00:07:28Z","timestamp":1789258048894,"version":"build-2803163510"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":179,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The modelling of spatial point processes has advanced considerably, yet extending these models to non-Euclidean domains, such as road networks, remains a challenging problem. We propose a novel framework for log-Gaussian Cox processes on general compact metric graphs by leveraging Gaussian Whittle\u2013Mat\u00e9rn fields, which are solutions to fractional-order stochastic differential equations on metric graphs. To achieve computationally efficient likelihood-based inference, we introduce a numerical approximation to the likelihood that eliminates the need to approximate the Gaussian process. This method, coupled with the exact evaluation of finite-dimensional distributions for Whittle\u2013Mat\u00e9rn fields with integer smoothness, ensures scalability and theoretical rigour, with derived convergence rates for the posterior distributions. The framework is implemented in the open-source MetricGraph R package, which integrates seamlessly with R-INLA to support fully Bayesian inference. We demonstrate the applicability and scalability of this approach through an analysis of road accident data from Al-Ahsa, SaudiArabia, consisting of over 160\u2009000 road segments. By identifying high-risk road segments using exceedance probabilities and excursion sets, our framework provides localized insights into accident hotspots and offers a powerful tool for modelling spatial point processes directly on complex networks.<\/jats:p>","DOI":"10.1093\/biomet\/asag043","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T16:43:03Z","timestamp":1782751383000},"source":"Crossref","is-referenced-by-count":0,"title":["Log-Gaussian Cox processes on general metric graphs"],"prefix":"10.1093","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2361-5465","authenticated-orcid":false,"given":"David","family":"Bolin","sequence":"first","affiliation":[{"name":"Statistics Program, King Abdullah University of Science and Technology (KAUST) ,Thuwal 23955, \u00a0 david.bolin@kaust.edu.sa","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2276-9622","authenticated-orcid":false,"given":"Damilya","family":"Saduakhas","sequence":"additional","affiliation":[{"name":"Statistics Program, King Abdullah University of Science and Technology (KAUST) ,Thuwal 23955,","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2562-2829","authenticated-orcid":false,"given":"Alexandre B","family":"Simas","sequence":"additional","affiliation":[{"name":"Statistics Program, King Abdullah University of Science and Technology (KAUST) ,Thuwal 23955, \u00a0 alexandre.simas@kaust.edu.sa","place":["Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,6,29]]},"reference":[{"key":"2026091219264284400_asag043-B1","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1214\/19-AOS1896","article-title":"Isotropic covariance functions on graphs and their edges","volume":"48","author":"Anderes","year":"2020","journal-title":"Ann. 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