Course abstract:
Landscape Representation offers a landscape-centric approach to digital analysis and representation. While sharing histories and methods with architecture and planning, landscape representation, given its engagement with natural processes and ecologies, requires greater control over complex forms and materials. This course established students' foundational knowledge in computer science and geographic information systems (GIS) through reviewing innovations, vocabularies, and impacts of disruptive technologies on design and project delivery since the 1960s. While these histories help form a critical understanding of software as a medium of design, this course also questioned the inherent problems of landscape as a digital and narrative medium. Students manipulated geospatial data from remotely sensed and open-source datasets to build a generalist's understanding of geospatial digital media for the range of scales that landscape architects confront and in which they collaborate. Automation and iterative, procedural workflows were stressed as part of an efficient design process and problem-solving toolset for landscape research and design, from regional to site-scale works.
For their term projects, students applied GIS and parametric modeling tools to explore landscape and biodiversity modeling techniques for measuring connectivity, fragmentation, species richness, and environmental impact with a focus on describing issues of data quality, scientific bias, reductive methodologies, and disciplinary blindspots in landscape ecology.
