Jorge Mateu Explained

Jorge Mateu
Birth Date:11 October 1969
Birth Place:Spain
Nationality:Spanish
Occupation:Mathematician, author and academic
Family:Adrián Mateu (son)
Education:Undergraduate., Mathematics and Statistics
M.Sc. Mathematics
Ph.D. Mathematics
Workplaces:Jaume I University

Jorge Mateu is a Spanish mathematician, author, and academic. He is a professor of Statistics within the Department of Mathematics at University Jaume I of Castellon[1] and Director of the Unit Eurocop for Data Science in criminal activities in the same department.

Mateu's research is centered on data science, geostatistics, and stochastic processes, with a particular emphasis on spatio-temporal point processes.[2] He led the 'Mathematical-statistical modelling of space-time data and data mining' group at Universitat Jaume I to develop spatio-temporal statistical techniques used for modelling across fields of public safety, environmental management, and criminology.[3] He is co-editor of books, including Spatial Statistics Through Applications (2002), Case Studies in Spatial Point Process Modeling (2005), Spatio-temporal Design. Advances in Efficient Data Acquisition (2012), Spatial and Spatio-Temporal Geostatistical Modeling and Kriging (2015), or Geostatistical Functional Data Analysis (2021). He has also received the Social Council Award from UJI and has been noted as a World Class Professor by an Indonesian ministry.[4]

Mateu is a Fellow of the Royal Statistical Society and Wessex Institute in Great Britain and a member of The International Statistical Institute[5] and the Bernoulli Society for Mathematical Statistics and Probability. He served as a Guest Editor for special issues in the Journal of Geophysical Research, and Environmetrics, as the editor-in-chief for the Journal of Agricultural, Biological, and Environmental Statistics[6] as well as an associate editor for Stochastic Environmental Research and Risk Assessment,[7] Spatial Statistics,[8] Environmetrics,[9] and International Statistical Review.[10]

Education

Mateu earned his Undergraduate Degree in Mathematics and Statistics from the University of Valencia in 1987, followed by a master's degree in 1995. He graduated with a Ph.D. from the Department of Mathematics at University of Valencia (UV) in 1998.[11]

Career

Mateu began his academic career as an Assistant Professor of Statistics in the Department of Mathematics at Jaume I University in 1992[12] where he served as an associate professor from 2000 to 2007. In 2007, he assumed the position of Full Professor of Statistics at UJI.[13]

In 2011, he held the position of Secretary for the International Environmetrics Society's board of directors[14] and became a co-director of the Erasmus Mundus Master in Geospatial Technologies.[15] Additionally, he served as President of the Board of Editors for METMA Workshops[16] Since 2014, he has been serving as the director of the Unit Eurocop: Statistical Modeling of Crime Data at Jaume I University.[17]

Research

Mateu focuses his research on the intersection of geostatistics, spatial data, stochastic processes, computational sciences, and natural sciences, with a particular emphasis on data science. He has analysed crime data and public health projects by employing a combination of statistical and machine-learning methods.[18] He served as a joint principal investigator for GEO-C.[19] He was worked on the projects (a) Statistical analysis of complex dependencies in space-time stochastic processes. Networks, functional marks and SPDE-based intensities. Ministry of Science and bInnovation (PID2022-141555OB-I00), 2023-2026, and (b) Spatio-temporal stochastic processes over networks and trajectories. Parametric models and functional marks. Generalitat Valenciana (CIAICO/2022/191), 2023-2025.

Data science and stochastic processes

Mateu's research on data science has included a range of topics such as filament delineation, model selection, and stochastic processes. In his research on the automatic delineation of filaments obtained from redshift catalogs, he applied a marked point process, to gain insights into the cosmic filament structure.[20] Together with a number of coauthors, he extended Gneiting's work to develop new spatio-temporal covariance models, resulting in novel classes of stationary nonseparable functions.[21] In addition, his research of space-time covariance function estimation introduced two methods based on the concept of composite likelihood which were designed to strike a balance between computational complexity and statistical efficiency.[22] Furthermore, while addressing the challenge of model selection, he discussed the limitations of traditional models like Bayesian Information Criterion and proposed a practical extension aimed at handling model selection issues effectively.[23] In 2018, during his research on the use of administrative data, he identified challenges related to statistical analyses and discussed the need for a critical approach to ensure the validity and accuracy of results.[24]

Spatial data and environmental management

Mateu has conducted studies on the spatial and spatio-temporal point processes. He conducted research to analyse spatial point patterns across different experimental groups, summarising his findings using the K-function in a non-parametric approach to emphasise the strengths and limitations of spatial data.[25] His work on Functional Data Analysis demonstrated its connection with three traditional types of spatial data structures and provided examples to illustrate the integration of geostatistical data, and areal data.[26] He also introduced a methodological framework based on geostatistics that applied to agricultural planning and environmental restoration.[27] In collaboration with other colleagues, he analysed real-world soil penetration and presented an approach for predicting spatial patterns in functional data which enabled the estimation of values at unobserved locations.[28]

Crime data and public health analysis

Mateu's research on functional environmental data, particularly in modelling air pollutant concentrations, emphasised the importance of cross-validation for parameter selection and provided insights into adapting kriging techniques.[29] In 2003, he introduced a spatiotemporal Hawkes-type point process model for analysing violence by incorporating daily and weekly periodic patterns in crime occurrences to shed light on the interplay of temporal trends in crime.[30] Expanding on this research, he introduced a deep learning approach in temporal correlations of historical data resulting in the enhancement of police resources, surveillance, crime event predictions, and prevention strategies.[13]

Awards and honors

Bibliography

Books

Selected articles

Notes and References

  1. Web site: Jorge Mateu Mahiques - Universitat Jaume I.
  2. Web site: Jorge Mateu: Member Profile—Wolfram Community. community.wolfram.com.
  3. Web site: UJI research team offers modeling techniques that allow planning in areas such as pollution, epidemiology or safety.
  4. Web site: International Seminar on World Class Professor Program.
  5. Web site: Meet the ASA's 2022 Incoming Editors | Amstat News. February 1, 2022.
  6. Web site: Journal of Agricultural, Biological and Environmental Statistics. Springer.
  7. Web site: Stochastic Environmental Research and Risk Assessment. Springer.
  8. Web site: Jorge Mateu - Editorial Board - Spatial Statistics - Journal - Elsevier. www.journals.elsevier.com.
  9. Web site: Environmetrics.
  10. Web site: International Statistical Review.
  11. Web site: Journal of Agricultural, Biological and Environmental Statistics. Springer.
  12. Web site: ITS Adjunct Professors.
  13. Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks . 10.1109/ACCESS.2020.3036715 . 227232920 . 2020 . Esquivel . Nicolas . Nicolis . Orietta . Peralta . Billy . Mateu . Jorge . IEEE Access . 8 . 209101–209112 . 2020IEEEA...8t9101E . free . 10234/192286 . free .
  14. Web site: Newsletter Volume 17, No. 1, June 2011.
  15. Modelling count data based on weakly dependent spatial covariates using a copula approach: Application to rat sightings . 10.1007/s10651-017-0379-x . 2017 . Gräler . Benedikt . Ayyad . Carlos . Mateu . Jorge . Environmental and Ecological Statistics . 24 . 3 . 433–448 . 254471945 .
  16. Web site: 20th Edition of the International Workshop on Spatial Econometrics and Statistics - Sciencesconf.org. sew2022.sciencesconf.org.
  17. Web site: A Conversation with Peter Diggle.
  18. Web site: Meet the ASA's 2022 Incoming Editors. Default.
  19. Using GIS to map the spatial and temporal occurrence of cholera epidemic in Camaroon. Ayuk Sally. Agbor. February 28, 2014. Master's Thesis . 10362/11547.
  20. Detection of cosmic filaments using the Candy model . 10.1051/0004-6361:20042409 . 2005 . Stoica . R. S. . Martínez . V. J. . Mateu . J. . Saar . E. . Astronomy & Astrophysics . 434 . 2 . 423–432 . astro-ph/0405370 . 2005A&A...434..423S . 3078877 .
  21. Nonseparable stationary anisotropic space–time covariance functions. E.. Porcu. P.. Gregori. J.. Mateu. December 1, 2006. Stochastic Environmental Research and Risk Assessment. 21. 2. 113–122. Springer Link. 10.1007/s00477-006-0048-3. 121599229 .
  22. Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach. Moreno. Bevilacqua. Carlo. Gaetan. Jorge. Mateu. Emilio. Porcu. March 14, 2012. Journal of the American Statistical Association. 107. 497. 268–280. CrossRef. 10.1080/01621459.2011.646928. 10234/68502 . 121529248 . free.
  23. A Bayesian Information Criterion for Singular Models . 10.1111/rssb.12187 . 2017 . Drton . Mathias . Plummer . Martyn . Journal of the Royal Statistical Society Series B: Statistical Methodology . 79 . 2 . 323–380 . 15334628 . free . 10.1111/rssb.12187 . free .
  24. Statistical Challenges of Administrative and Transaction Data . 10.1111/rssa.12315 . 2018 . Hand . David J. . Journal of the Royal Statistical Society Series A: Statistics in Society . 181 . 3 . 555–605 . 126301517 . free . 10044/1/61527 . free .
  25. A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns. Peter J.. Diggle. Jorge. Mateu. Helen E.. Clough. June 14, 2000. Advances in Applied Probability. 32. 2. 331–343. Cambridge University Press. 10.1239/aap/1013540166. 120635354 .
  26. Statistics for spatial functional data: some recent contributions. P.. Delicado. R.. Giraldo. C.. Comas. J.. Mateu. May 14, 2010. Environmetrics. 21. 3–4. 224–239. CrossRef. 10.1002/env.1003. 2010Envir..21..224D . 120192912 .
  27. Spatial dynamics of soil salinity under arid and semi-arid conditions: geological and environmental implications. M. M.. Jordán. J.. Navarro-Pedreño. E.. García-Sánchez. J.. Mateu. P.. Juan. February 1, 2004. Environmental Geology. 45. 4. 448–456. Springer Link. 10.1007/s00254-003-0894-y. 53125885 .
  28. Ordinary kriging for function-valued spatial data. R.. Giraldo. P.. Delicado. J.. Mateu. September 1, 2011. Environmental and Ecological Statistics. 18. 3. 411–426. Springer Link. 10.1007/s10651-010-0143-y. 40403028 .
  29. Kriging with external drift for functional data for air quality monitoring. Rosaria. Ignaccolo. Jorge. Mateu. Ramon. Giraldo. July 1, 2014. Stochastic Environmental Research and Risk Assessment. 28. 5. 1171–1186. Springer Link. 10.1007/s00477-013-0806-y. 53375199 . 2318/137791. free.
  30. A Semiparametric Spatiotemporal Hawkes-Type Point Process Model with Periodic Background for Crime Data . 10.1111/rssa.12429 . 2019 . Zhuang . Jiancang . Mateu . Jorge . Journal of the Royal Statistical Society Series A: Statistics in Society . 182 . 3 . 919–942 . 125818982 .

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