Jorge Mateu

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 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.

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.

Mateu earned his Undergraduate Degree in Mathematics and Statistics from the University of Valencia in 1987, followed by a master's degree in 1995.

Mateu began his academic career as an Assistant Professor of Statistics in the Department of Mathematics at Jaume I University in 1992 where he served as an associate professor from 2000 to 2007.

He graduated with a Ph.D. from the Department of Mathematics at University of Valencia (UV) in 1998.

He is co-editor of books, including Spatial Statistics Through Applications (2002), Case Studies in Spatial Point Process Modeling (2005), Spatio-temporal Design.

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.

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.

In 2007, he assumed the position of Full Professor of Statistics at UJI.

In 2011, he held the position of Secretary for the International Environmetrics Society's board of directors and became a co-director of the Erasmus Mundus Master in Geospatial Technologies.

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.

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

Additionally, he served as President of the Board of Editors for METMA Workshops Since 2014, he has been serving as the director of the Unit Eurocop: Statistical Modeling of Crime Data at Jaume I University.

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.

He served as a joint principal investigator for GEO-C.

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.

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.

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.

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.

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.

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.

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.

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.

He also introduced a methodological framework based on geostatistics that applied to agricultural planning and environmental restoration.

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.

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.