Spatial statistics


Introduce theoretical foundations and practical aspects of spatial statistics Present the application of spatial statistics for characterizing patterns in and making inferences with geographical data Familiarize students with the use of open-source, specialized software for spatial statistics/analysis


Basic knowledge and skills in Statistics

Learning Outcomes

Ability to differentiate between classical and spatial statistics Working knowledge of main analytical methods used for the statistical analysis of geographical data Ability to select the appropriate spatial statistical method (or combination of methods) depending on the particular problem and data at hand Ability to succinctly and efficiently communicate analysis results, using specialized software, maps and graphs  


Class Overview / Statistics Review / R Basics Regression Models / Examples / Applications in R Spatial Point Pattern Analysis / Examples / Applications in R Spatial Interpolation & Geostatistics / Examples / Applications in R Spatial Analysis of Areal Data / Examples / Applications in R  

Content Delivery

Lectures: Theoretical foundations and practical aspects of spatial statistics are presented in class during lectures Computer-based lab practice:  Hands-on experience in the practice of spatial statistics using open-source software and real-world data

Coursework And Assignment Details

Computer-based lab assignments: involving applications of spatial statistics on real-world data, using R, an open-source computational and graphical environment for spatial analysis (50% of final grade) Final exam: including multiple-choice questions and questions requiring short answers (1-2 paragraphs) on topics covered during the class (50% of final grade)