Dr Chayn Sun is a GIS Analyst/Spatial Scientist with over 10 years of experience in applying GIS technologies in multi-disciplinary projects and research. Chayn has professional GIS working experience in a number of environmental assessment projects in New Zealand and Australia, her work included integrating, organising, processing and visualizing spatial data from multiple sources to conduct spatial and statistical analysis and modelling. Chayn also served as a GIS consultant for quality assessment and control for government funded projects on landscape assessment, flood hazard assessment and transport route selection. Between 2013 to 2016, Chayn worked on my PhD research in the Department of Spatial Sciences, Curtin University. Chayn developed a research framework to identify variables and research questions for an interdisciplinary study. Her research engaged a range of spatial technologies and mixing of quantitative and qualitative methods within a single investigation, a considerable volume of research data crossing movement tracking, geodemography were successfully collected, geocoded and analysed. Chayn has been publishing as the leading-author in the areas of transport, spatial science, eye movement research, etc. Her current activities at RMIT are to look at the impact of urban vegetation on urban heat islands (UHI) effect, and active traveling with temperature and tree shadeways information services. She is also developing algorithm to enhance the spatial-temporal resolution of satellite imagery derived surface temperature. Chayn has taught in an arrange of GIS course for both undergraduate and post-graduate levels, including GIS, Spatial analysis and modeling and cartographic statistics.

• 1. Practical Spatial Statistics: Fundamental Theories, Techniques and Applications. 2. Spatial statistics overview: History; Four Fundamental Concepts. 3. The Famous Geography Theories: Spatial autocorrelation; Spatial heterogeneity. 4. Spatial Relationships: The various spatial relationships. 5. Statistical measurements in GIS: P value and Z score; Hypothesis testing; Location, attributes, topology, spatial parameters. 6. Basic spatial data discovery: Data integration; Visualization. 7. Building spatial statistical analysis framework. 8. Point data pattern analysis: Attributes of point datasets; Frequency and density; Sampling and Interpolation. 9. Spatial relationship analysis: Clustering analysis; Spatial Autocorrelation analysis; Hot-spots analysis. 10. Spatial regression and modelling: Spatial regression; Geographically Weighted Regression. 11. Exercises.