Jorge Mateu
Born (1969-10-11) October 11, 1969
Spain
NationalitySpanish
Occupation(s)Mathematician, author and academic
Academic background
EducationUndergraduate., Mathematics and Statistics
M.Sc. Mathematics
Ph.D. Mathematics
Alma materUniversity of Valencia
Academic work
InstitutionsJaume 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.[2]

Mateu's research is centered on data science, geostatistics, and stochastic processes, with a particular emphasis on spatio-temporal point processes.[3] 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.[4] 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.[5]

Mateu is a Fellow of the Royal Statistical Society and Wessex Institute in Great Britain and a member of The International Statistical Institute[6] 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[7] as well as an associate editor for Stochastic Environmental Research and Risk Assessment,[8] Spatial Statistics,[9] Environmetrics,[10] and International Statistical Review.[11]

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.[12]

Career

Mateu began his academic career as an Assistant Professor of Statistics in the Department of Mathematics at Jaume I University in 1992[13] where he served as an associate professor from 2000 to 2007. In 2007, he assumed the position of Full Professor of Statistics at UJI.[14]

In 2011, he held the position of Secretary for the International Environmetrics Society's board of directors[15] and became a co-director of the Erasmus Mundus Master in Geospatial Technologies.[16] Additionally, he served as President of the Board of Editors for METMA Workshops[17] Since 2014, he has been serving as the director of the Unit Eurocop: Statistical Modeling of Crime Data at Jaume I University.[2]

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.[14]

Awards and honors

  • 2022 – Social Council Award, Jaume I University
  • 2022 – Recognition of World Class Professor, Ministry of Education, Culture, Research, and Technology, Republic of Indonesia[5]

Bibliography

Books

  • Spatial Statistics Through Applications (2002) ISBN 978-1853126499
  • Geoestadística y Modelos Matemáticos en Hidrogeología (2003) ISBN 978-8480214179
  • Spatial Point Process Modelling and its Applications. Proceedings of the International Conference on Spatial Point Process Modelling and its Applications (2004) ISBN 978-8480214759
  • Case Studies in Spatial Point Process Modeling (2005) ISBN 978-0387283111
  • New Advances in Space-Time Random Field Modelling (2008) ISBN 978-8480216500
  • Statistics for Spatio-Temporal Modelling (2008) ISBN 978-8860250988
  • Positive Definite Functions: from Schoenberg to Space-Time Challenges (2008) ISBN 978-8461282821
  • Stochastic Processes for Spatial Econometrics (2009) ISBN 978-8497454124
  • Spatio-temporal Design. Advances in Efficient Data Acquisition (2012) ISBN 978-0470974292
  • Spatial and Spatio-Temporal Geostatistical Modeling and Kriging (2015) ISBN 978-1118413180
  • Geostatistical Functional Data Analysis (2021) ISBN 978-1119387848

Selected articles

  • Waagepetersen, R., Guan, Y., Jalilian, A., & Mateu, J. (2016). Analysis of multi-species point patterns using multivariate log Gaussian Cox processes. Journal of the Royal Statistical Society C, 65 (1), 77–96.
  • Stoica, R. S., Philippe, A., Gregori, P., & Mateu, J. (2017). An ABC Shadow algorithm: a new tool for spatial patterns statistical analysis. Statistics and Computing, 27, 1225–1238.
  • Eckardt, M., & Mateu, J. (2018). Point patterns occurring on complex structures in space and spacetime: An alternative network approach. Journal of Computational and Graphical Statistics, 27 (2), 312–322.
  • Zhuang, J., & Mateu, J. (2019). A semi-parametric spatiotemporal Hawkes-type point process model with periodic background for crime data. Journal of the Royal Statistical Society A, 182 (3), 919–942.
  • González, J. A., Hahn, U., & Mateu, J. (2020). Analysis of tornado reports through replicated spatio-temporal point patterns. Journal of the Royal Statistical Society C, 69 (1), 3-23.
  • Müller, R., Schuhmacher, D., & Mateu, J. (2020). Metrics and barycenters for point pattern data. Statistics and Computing, 30 (4), 953–972.
  • Eckardt, M., & Mateu, J. (2021). Second-order and local characteristics of network intensity functions. Test, 30, 318–340.
  • Frías, M. P., Torres-Signes, A., Ruiz-Medina, M. D., & Mateu, J. (2022). Spatial Cox processes in an infinite-dimensional framework. Test, 31, 175–203.

References

  1. ↑ "Jorge Mateu Mahiques - Universitat Jaume I".
  2. 1 2 "A Conversation with Peter Diggle" (PDF).
  3. ↑ "Jorge Mateu: Member Profile—Wolfram Community". community.wolfram.com.
  4. ↑ "UJI research team offers modeling techniques that allow planning in areas such as pollution, epidemiology or safety".
  5. 1 2 "International Seminar on World Class Professor Program".
  6. ↑ "Meet the ASA's 2022 Incoming Editors | Amstat News". February 1, 2022.
  7. ↑ "Journal of Agricultural, Biological and Environmental Statistics". Springer.
  8. ↑ "Stochastic Environmental Research and Risk Assessment". Springer.
  9. ↑ "Jorge Mateu - Editorial Board - Spatial Statistics - Journal - Elsevier". www.journals.elsevier.com.
  10. ↑ "Environmetrics".
  11. ↑ "International Statistical Review".
  12. ↑ "Journal of Agricultural, Biological and Environmental Statistics". Springer.
  13. ↑ "ITS Adjunct Professors".
  14. 1 2 Esquivel, Nicolas; Nicolis, Orietta; Peralta, Billy; Mateu, Jorge (2020). "Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks". IEEE Access. 8: 209101–209112. Bibcode:2020IEEEA...8t9101E. doi:10.1109/ACCESS.2020.3036715. hdl:10234/192286. S2CID 227232920.
  15. ↑ "Newsletter Volume 17, No. 1, June 2011" (PDF).
  16. ↑ Gräler, Benedikt; Ayyad, Carlos; Mateu, Jorge (2017). "Modelling count data based on weakly dependent spatial covariates using a copula approach: Application to rat sightings" (PDF). Environmental and Ecological Statistics. 24 (3): 433–448. doi:10.1007/s10651-017-0379-x. S2CID 254471945.
  17. ↑ "20th Edition of the International Workshop on Spatial Econometrics and Statistics - Sciencesconf.org". sew2022.sciencesconf.org.
  18. ↑ "Meet the ASA's 2022 Incoming Editors". Default.
  19. ↑ Agbor, Ayuk Sally (February 28, 2014). Using GIS to map the spatial and temporal occurrence of cholera epidemic in Camaroon (Master's Thesis). hdl:10362/11547.
  20. ↑ Stoica, R. S.; Martínez, V. J.; Mateu, J.; Saar, E. (2005). "Detection of cosmic filaments using the Candy model". Astronomy & Astrophysics. 434 (2): 423–432. arXiv:astro-ph/0405370. Bibcode:2005A&A...434..423S. doi:10.1051/0004-6361:20042409. S2CID 3078877.
  21. ↑ Porcu, E.; Gregori, P.; Mateu, J. (December 1, 2006). "Nonseparable stationary anisotropic space–time covariance functions". Stochastic Environmental Research and Risk Assessment. 21 (2): 113–122. doi:10.1007/s00477-006-0048-3. S2CID 121599229 – via Springer Link.
  22. ↑ Bevilacqua, Moreno; Gaetan, Carlo; Mateu, Jorge; Porcu, Emilio (March 14, 2012). "Estimating Space and Space-Time Covariance Functions for Large Data Sets: A Weighted Composite Likelihood Approach". Journal of the American Statistical Association. 107 (497): 268–280. doi:10.1080/01621459.2011.646928. hdl:10234/68502. S2CID 121529248 – via CrossRef.
  23. ↑ Drton, Mathias; Plummer, Martyn (2017). "A Bayesian Information Criterion for Singular Models". Journal of the Royal Statistical Society Series B: Statistical Methodology. 79 (2): 323–380. doi:10.1111/rssb.12187. hdl:10.1111/rssb.12187. S2CID 15334628.
  24. ↑ Hand, David J. (2018). "Statistical Challenges of Administrative and Transaction Data". Journal of the Royal Statistical Society Series A: Statistics in Society. 181 (3): 555–605. doi:10.1111/rssa.12315. hdl:10044/1/61527. S2CID 126301517.
  25. ↑ Diggle, Peter J.; Mateu, Jorge; Clough, Helen E. (June 14, 2000). "A comparison between parametric and non-parametric approaches to the analysis of replicated spatial point patterns". Advances in Applied Probability. 32 (2): 331–343. doi:10.1239/aap/1013540166. S2CID 120635354 – via Cambridge University Press.
  26. ↑ Delicado, P.; Giraldo, R.; Comas, C.; Mateu, J. (May 14, 2010). "Statistics for spatial functional data: some recent contributions". Environmetrics. 21 (3–4): 224–239. Bibcode:2010Envir..21..224D. doi:10.1002/env.1003. S2CID 120192912 – via CrossRef.
  27. ↑ Jordán, M. M.; Navarro-Pedreño, J.; García-Sánchez, E.; Mateu, J.; Juan, P. (February 1, 2004). "Spatial dynamics of soil salinity under arid and semi-arid conditions: geological and environmental implications". Environmental Geology. 45 (4): 448–456. doi:10.1007/s00254-003-0894-y. S2CID 53125885 – via Springer Link.
  28. ↑ Giraldo, R.; Delicado, P.; Mateu, J. (September 1, 2011). "Ordinary kriging for function-valued spatial data". Environmental and Ecological Statistics. 18 (3): 411–426. doi:10.1007/s10651-010-0143-y. S2CID 40403028 – via Springer Link.
  29. ↑ Ignaccolo, Rosaria; Mateu, Jorge; Giraldo, Ramon (July 1, 2014). "Kriging with external drift for functional data for air quality monitoring". Stochastic Environmental Research and Risk Assessment. 28 (5): 1171–1186. doi:10.1007/s00477-013-0806-y. hdl:2318/137791. S2CID 53375199 – via Springer Link.
  30. ↑ Zhuang, Jiancang; Mateu, Jorge (2019). "A Semiparametric Spatiotemporal Hawkes-Type Point Process Model with Periodic Background for Crime Data". Journal of the Royal Statistical Society Series A: Statistics in Society. 182 (3): 919–942. doi:10.1111/rssa.12429. S2CID 125818982.
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