#  24006 Geocomplexity Learning for Spatial Prediction 

 



 **Project Title:** Geocomplexity Learning for Spatial Prediction

 **Key Words:** Geocomplexity ,Spatial Prediction

 **Research Topics:** Geospatial Modelling

 **Mentor:** Yongze Song, Curtin University, [yongze.song@curtin.edu.au](mailto:alexander.hohl@geog.utah.edu)

 **Project Description:**

 This project aims at developing a geocomplexity learning approach for more accurate spatial prediction. The geocomplexity learning is an integration of local spatial complexity as explained in <https://doi.org/10.1080/13658816.2023.2203212> and machine learning. The study will be published in a top journal.

 **Tasks and Responsibilities:**

- Good English communication and writing skills
- Be familiar with spatial statistical approaches
- Will be evaluated through an assessment about theories and methods about spatial statistics and mathematical geosciences before joining the project.

 **Minimum qualifications:**

- R/Python programming
- GIS
- Literature search

 **Term of the Project:**

- May to Dec 2024

 **Deliverables:**

- A set of visualizations (maps, charts) showcasing key insights.
- A comprehensive report detailing findings, interpretations, and recommendations.
- Documentation of the workflow, methodology, and tools used for reproducibility.
- Presentation of key findings and insights to the research team and stakeholders.