MS in Urban Analytics & Geodesign

College

  • East Falls Campus
  • College of Architecture & the Built Environment

Degree Earned

Master of Science

Program Type

  • On Campus

Contact Information

Program

Name: Peng Du, PhD, LEED AP, WELL AP
Position: Program Director & Assistant Professor
Contact Number(s):
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Scholarship Info
STEM FAQ

The College of Architecture and the Built Environment is not accepting applications to the MS in Urban Analytics & Geodesign program. Please contact Admissions with questions at enroll@jefferson.edu or call 215-951-2800.

The MS in Urban Analytics & Geodesign provides students with interdisciplinary urban research and design, incorporating spatial data science, urban informatics, geospatial technology, and sustainability. This 30-credit, STEM-designated MS program leverages data-driven technologies in identifying and addressing major issues our cities continue to face, such as urbanization, climate change, inequality, and resource depletion.

Collaboration with the Jefferson Institute for Smart and Healthy Cities offers students unparalleled opportunities for research and industry experience. Addressing climate change, public health, pandemics and other challenges by incorporating urban analytics, computational design, and smart technologies into urban environments is the next frontier within the profession.

MS in Urban Analytics & Geodesign is a STEM-designated program (CIP Code 11.0804), which allows international students to be eligible for a total of 3 years of employment within their discipline based on the Optional Practical Training (OPT) Program.

Mapping Density

Program Highlights

In this highly innovative program, next-generation designers and researchers of the built environment learn through a combination of advanced urban analytics, spatial data science and geospatial technologies. Students work on real projects in a highly-collaborative fast-paced, hands-on learning environment. Upon graduation, students will have the skill sets necessary to address complex design and planning problems with powerful data-driven analytical and visualization tools that foster interdisciplinary collaborations.

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