Yongze Song

Lecturer, School of Design and the Built Environment
Curtin University
Perth, 6102, WA, Australia

Email: yongze.song@curtin.edu.au

Join Us

We are seeking highly motivated PhD students, junior researchers, visiting researchers/students, and collaborators to join us.

Dr. Yongze Song, RGS Fellow, is a Lecturer and Doctoral Supervisor at Curtin University, Australia. He is a Fellow of the Royal Geographical Society, United Kingdom, and a Publications Committee Member of the International Association for Mathematical Geosciences (IAMG).

He has expertise in developing geospatial methods and implementing Earth data for sustainable infrastructure. He developed a series of new geospatial methods and R software packages for understanding geographical and spatial data. He is a recipient of more than ten awards at international and national levels, such as the Global Top 10 Young Scientist Award and the Australian National Location Data First Prize.

He is an Editorial Board Member of GIScience & Remote Sensing (IF 6.397), iScience (IF 6.107), and Managing Guest Editor for a few journals in Earth and spatial sciences. He serves as a Session Chair, Scientific Committee Member, Keynote Speaker, or Special Issue Coordinator for international conferences IAMG, AAG, AGILE, WBC, IPC, IWEG, ICCSTE, etc. He serves for over 30 peer-reviewed journals as a reviewer. He was invited to present more than 40 research seminars at universities (e.g., Harvard University, Australian National University, National University of Singapore, Peking University, etc) and industries globally.

Research Topics
Earth Observation for Sustainable Infrastructure

Innovative Geospatial Methods

Second-Dimension Spatial Association (SDA) for spatial prediction.

## use R package "SecDim"


Optimal Parameters-based Geographical Detectors (OPGD) model for spatial factors exploration.

## use R package "GD"


Interactive Detector for Spatial Associations (IDSA) for spatial factors exploration.

## use R package "IDSA"


Spatial Trade-Off Relation (STOR) for assessing quantity-quality trade-off relations of sustainability indicators.

Model-driven fuzzy spatial multi-criteria decision making (MFSD): Making decisions using statistical, machine learning, and spatial models.