EARTHSYS EARTHSYS 144/EARTHSYS 164: Fundamentals of Geographic Information Science (GISci) is an always-evolving survey of the concepts, data, technologies, and methods that underpin modern spatial analysis, grounded in the idea that "everything is somewhere, and that somewhere matters." As spatial data technologies continue to reshape research and decision-making, from environmental science and public health to urban systems and personalized location-based services, the course introduces students to current and emerging approaches for creating, managing, analyzing, and communicating geographic information. Topics include geographic data modeling and "spatial thinking," field and sensor-based data collection, coordinate systems and metadata, basic spatial statistics and modeling, remote sensing and satellite imagery, large-scale and "big" spatial data, machine learning for spatial analysis, cartographic design, and web-based storytelling platforms. Through lectures, hands-on labs, guest speakers, and an individual project, students gain practical experience with contemporary GIS tools and platforms while developing the critical skills needed to evaluate and apply spatial data and methods in both academic research and real-world contexts.
3-4 units · Letter or Credit/No Credit · GER: WAY-AQR
EARTHSYS 144/164: Fundamentals of Geographic Information Science (GISci) is an always-evolving survey of the concepts, data, technologies, and methods that underpin modern spatial analysis, grounded in the idea that "everything is somewhere, and that somewhere matters." As spatial data technologies continue to reshape research and decision-making, from environmental science and public health to urban systems and personalized location-based services, the course introduces students to current and emerging approaches for creating, managing, analyzing, and communicating geographic information. Topics include geographic data modeling and "spatial thinking," field and sensor-based data collection, coordinate systems and metadata, basic spatial statistics and modeling, remote sensing and satellite imagery, large-scale and "big" spatial data, machine learning for spatial analysis, cartographic design, and web-based storytelling platforms. Through lectures, hands-on labs, guest speakers, and an individual project, students gain practical experience with contemporary GIS tools and platforms while developing the critical skills needed to evaluate and apply spatial data and methods in both academic research and real-world contexts.
Offered in Autumn 2026, Spring 2027 at Stanford University.