In today's data-driven world, spatial information plays a crucial role, with an estimated 80% of data containing a spatial component. Spatial statistics, as a specialized field within statistics, provides individuals with the necessary tools for analyzing and interpreting spatial patterns and processes found within geographical or spatially referenced datasets. As a rapidly developing area of statistics, spatial statistics has seen enormous growing interest and applications in a broad range of disciplines, such as natural resources, environmental science, earth science and public health. This course aims at providing students an overview and introduction to spatial statistical methods and their applications. The course will include lectures and hands-on exercises. Lectures will focus on basic statistical concepts and techniques for the analysis of spatially referenced data, including point level (or geostatistical) data, regional (or lattice) data, and spatial point patterns. Examples from a variety of topical areas will be used to illustrate the spatial statistical methodologies. Computer laboratory exercises will allow students to gain hands-on experience in the statistical analysis of spatial data.