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This course has been designed to help geography students gain essential skills in analyzing and interpreting spatial data using statistical methods. As geographic studies increasingly rely on digital tools and data, the ability to apply statistics to spatial patterns and relationships is becoming more important than ever. Many students are familiar with mapping and data collection through GIS or remote sensing, but they often lack the statistical knowledge needed to draw meaningful conclusions from spatial data. This course aims to fill that gap. The course allows students to explore and understand patterns, relationships, and trends in space. It covers both foundational techniques like measures of central tendency and dispersion, and more advanced tools such as spatial autocorrelation, regression, and time series analysis. It introduces students to the principles and methods of statistical analysis applied to spatial data in geography. It combines core statistical concepts with geographic thinking, enabling students to explore spatial patterns, relationships, and trends using quantitative techniques. Topics include descriptive and inferential statistics, probability distributions, sampling strategies, correlation and regression analysis, multivariate methods, and time series analysis. Special emphasis is placed on spatial applications such as spatial autocorrelation, inequality measurement, and integration with GIS. Through lectures, practical exercises, and case studies, students will gain the skills needed to interpret complex spatial data, design robust analyses, and support decision-making in geographic research and applied fields.Description should contain 100-150 words.
