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-temporalpoint 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 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.
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