Introduction GIS Courses
- CEE 187: Geographic Information Systems – Fall 2023
- CLS 125/ HIST 179/ FAH 0092-04/ ARCH 175 Introduction to Geospatial Humanities – Spring 2024
- DHP P207: GIS for International Applications – Summer 2023 / Fall 2023 / Spring 2024
- GIS 101/ ENV 107/ INTR 81: Introduction to Geographic Information Systems – Fall 2023 / Spring 2024
- HIA 219: Spatial Epidemiology – Fall 2023 / Spring 2024 (online)
- MCM 591: GIS for Conservation Medicine – Fall 2023
- Nutr 231: Fundamentals of GIS for Food, Agriculture, and Environmental Applications – Fall 2023 / Spring 2024
- PH 0262: GIS for Public Health – Spring 2024 (1/2 Semester Course)
- UEP 232: Intro to GIS – Summer 2023 / Fall 2023 / Spring 2024
- UEP 294-28: GIS Boot Camp & Qualitative GIS – Spring 2024
Advanced Geospatial Courses
- DHP P289: Advanced Geospatial Modeling – Spring 2024
- GIS 102 / Env 197: Advanced GIS – Spring 2024
- UEP 231: Interactive Web Mapping – Fall 2023
- UEP 235: Advanced GIS – Fall 2023 / Spring 2024
- UEP 236: Spatial Statistics – Spring 2024
Remote Sensing and Drone Courses
- UEP 189 / CEE 189: Introduction to Remote Sensing – Fall 2023
- UEP-294/ CEE-0293: Advanced Remote Sensing – Spring 2024
- MCM-1011: Drones– Unmanned Aircraft Systems (UAS) for Field Data Collection, Mapping & Analysis – Spring 2024
Programming and Visualization Courses
These are select data science/data visualization courses that earn credit towards the Spatial Data Science certificate.
- UEP 239: Introduction to Programming with Python (1 SHU) – Fall 2023 / Spring 2024
- UEP 0237-01 Urban Analytics & Visualization – Fall 2023
- UEP 0238-01 Data Science for Urban Sustainability – Spring 2024
CEE 187: Geographic Information Systems
Instructor: Dr. Laurie Baise
Spatial analysis with Geographic Information Systems (GIS), including their use for engineering applications. GIS data structure and management, techniques for spatial analysis. Applications including seismic hazard, water resources, and environmental health. Laboratory exercises in GIS. Prerequisites: ES 56.
CLS 125/ HIST 179/ FAH 0092-04/ ARCH 175 Introduction to Geospatial Humanities
Broad foundations of Geographic Information Systems theory, capabilities, technology and applications as they relate to the humanities.
DHP P207: GIS for International Applications (Fletcher)
Instructor: Dr. Marcia Moreno-Baez
This course will introduce students to the use of geospatial technologies, data and analysis focusing on applications in the international context. The course gives primary emphasis to the use of geographic information systems (GIS) for data creation, mapping, and analysis. It will also cover the use of global positioning systems (GPS) for field data collection and mapping; cartography for high quality visualization of conditions, issues, and analysis results in a given locale; and the use of map mash-ups and crowd sourcing in the international arena
GIS 101/ ENV 107/ INTR 81: Introduction to Geographic Information Systems
Instructor: Dr. Rebecca Shakespeare, Dr. Alexandra Thorn, Dr. Marshall Pontrelli, Dr. Aggeliki Barberopoulou (Instructor is dependent on GIS section)
Broad foundation of Geographic Information Systems theory, capabilities, technology, and applications. Topics include GIS data discovery, data structure and management; principles of cartographic visualization; and basic spatial analysis and modeling. Assignments concentrate on applying concepts covered in lectures and class exercises to term projects in each student’s fields of interest.
MCM 591: GIS for Conservation Medicine
Instructor: Carolyn Talmadge
This course will introduce students to the fundamental concepts of the Geographic Information Systems (GIS) as it relates to the one health paradigm and veterinary health. This course is designed for novice GIS students with specific focus on mapping and spatial analysis for human, animal, and environmental health applications. Tutorials include vulnerability analyses of animal habitats, monitoring disease outbreaks for public health, calculating deforestation and land cover change, suitability analysis for Ebola treatment centers in Liberia, site analysis for alternative energy sources, and many more. Technical topics to be covered include GIS data discovery; GPS field data collection; data structure and management; principles of cartographic visualization and design; and basic spatial tools, analysis and modeling. Classes will consist of both a lecture segment and an in-class activity/demonstration. Students will complete weekly tutorials or project assignments and conclude the semester with a final mapping/analysis project of their choosing. This course is open to all students and faculty from the Veterinary School.
Instructor: Dr. Alexandra Thorn
This is a full credit course that introduces Geographic Information Systems (GIS) to address the many problems of agriculture, food systems, and the environment. This course will provide students with the fundamentals to begin using GIS in research and applied projects. The primary purpose of this course is to provide students with sufficient understanding of GIS science and software programs to be able to conduct a simple project independently.
PH 0262: GIS for Public Health
Instructor: Dr. Thomas Stopka
This half credit course will be taught during the second half of the Spring Semester of 2015. The course, which will include a mix of lectures and labs, will offer public health students the opportunity to learn introductory GIS and spatial analysis concepts and applications. Students with previous coursework in public health and epidemiology are encouraged to apply. Enrollment will be limited to 25 students.
UEP 232 Intro to GIS: GIS for Urban Analysis
Instructor: Dr. Sumeeta Srinivasan , Dr. Rebecca Shakespeare, Dr. Aggeliki Barberopoulou (Instructor is dependent on GIS section)
This course will focus on introducing students to the use of geographic information systems in the urban/suburban/metropolitan environment. Students will learn to work with urban spatial databases (including data sets pertaining to land use/land cover, parcel records, census demographics, environmental issues, water, transportation, local government, community development, and businesses). Technical topics to be covered include finding and understanding sources of information for metropolitan spatial databases, integration of data from a variety of sources, database structure and design issues, spatial analysis capabilities, data quality and data documentation. While learning GIS skills, participants will complete a mapping/analysis project of their choosing. Prerequisite: None. Course Syllabus
UEP 294-28: GIS Boot Camp & Qualitative GIS
Instructor: Dr. Rebecca Shakespeare
Accelerated introduction to the mechanics of spatial analysis, cartography, and applications in ArcGIS. Laboratory modules and hands-on practice applying GIS techniques, including basic introduction to spatial data management and geodesy. Students will be prepared to continue into Qualitative GIS, create maps, and complete basic GIS analyses independently. Mixed-method, critical, and qualitative approaches and applications of GIS will be considered through readings, seminar discussions, and applied laboratory activities. Topics include participatory GIS, critical GIS, sketch-mapping, and augmenting qualitative analysis with GIS approaches.
DHP P289: Advanced Geospatial Modeling
Instructor: Dr. Marcia Moreno-Baez
Check out this StoryMap on what you’ll learn in Advanced Geospatial Modeling.
GIS 102 / Env 197 Advanced GIS
Instructor: Dr. Sumeeta Srinivasan
Design and use of spatial information systems to support analytical modeling in research and geospatial processing for professional development, research, and practice. Topics include the structure and integration of large data sets, relational database management, development of spatial data, integration of data into models and geoprocessing techniques, and basic scripting to support geospatial modeling. Prerequisites: GIS 101, UEP 232 or equivalent.
UEP 231: Interactive Web Mapping
Instructor: Paul Platosh
UEP 235: Advanced GIS
Instructor: Dr. Sumeeta Srinivasan
This course is intended to be for students from any discipline with an interest in advanced geospatial modeling and spatial analysis. It explores topics in Database Management such as SQL and UML and work with a variety of spatial data formats. It will also build on previous knowledge of Geographic Information Systems (GIS) applications. Students will learn spatial analysis methods including raster analysis, suitability analysis, spatiotemporal statistics, Geostatistics and network analysis. The lab component will focus on the use of ArcGIS. Automation using ArcPy and/or Model Builder will be an essential component of many of the lab exercises. Students will work on a either group or individual projects based on their own interests. Prerequisites: A full semester introductory GIS course or its equivalent.
UEP 236: Spatial Statistics
Instructor: Dr. Sumeeta Srinivasan
This is a first course on spatial data analysis. Students will learn about global and local spatial autocorrelation statistics, cluster analysis, principal component analysis, point patterns, interpolation, hotspot analysis and space time analysis. They will also learn to use a variety of regression techniques for spatial data including spatial, autologistic and geographically weighted regressions. Several open source software will be introduced: Geoda, CrimeStat, SAM, CAST and R. The course will have weekly lab exercises and a final project based on the student interests. Prerequisite: Introduction to Statistics or equivalent.
UEP-189 / CEE 189: Introduction to Remote Sensing
Instructor: Dr. Aggeliki Barberopoulou
This course introduces students to the use of satellite imagery and other remotely sensed data for urban and environmental analysis. The course will emphasize practical applications of remote sensing for understanding human-environment dynamics. Students will get a thorough overview of remote sensing data sources and understand which sources are appropriate for which applications. Lectures and labs will cover the workflow of processing sensing data for environmental analysis, starting with data acquisition and moving on to image georeferencing, image enhancement and filtering, image classification and basic image analysis.
UEP-0294 /CEE-0293 Advanced Remote Sensing
Instructor: Dr. Magaly Koch (Magaly.Koch@tufts.edu)
This advanced course deals with Earth Observation (EO) satellites, EO data products, and applications. It is a project-oriented course with several hands-on activities. Students will learn advanced techniques to extract meaningful information from remote sensing imagery: monitoring and assessing land change, time-series analysis, target detection, spectral mixture analysis, decision support tools, change detection for disaster monitoring, advanced classification algorithms. A final project is required that involves a real-world application of advanced image processing or remote-sensing physics. Students will develop their own projects, tailored to their research interests. Prior coursework in Geographic Information Systems, remote sensing or statistics is recommended.
MCM-1011-1- Drones– Unmanned Aircraft Systems (UAS) for Field Data Collection, Mapping & Analysis
Instructor: Jon Caris (Jon.Caris@tufts.edu)
Drones for Field Data Collection, Mapping & Analysis is a course designed to teach students drone flight operations for the collection, mapping, and analysis of drone imagery. Data analysis will draw on techniques and methods that include photogrammetry, structure from motion, image processing, mapping, and aerial photography and videography. The course encourages teamwork, curiosity, critical thinking, perseverance, and creativity, as well as best practices, ethical conduct and etiquette regarding fieldwork and community-based research. We seek motivated students who want to learn practical techniques for acquiring and analyzing aerial data, as well as students who may be skeptical about drone technology. We encourage all students to think critically about their engagement with drones and to help us improve the academy’s approach to teaching and research with this emergent and disruptive technology.
UEP 239: Introduction to Programming with Python (1 SHU)
Dr. Aggeliki Barberopoulou
**This is a required prerequisite for UEP 0237-01 Urban Analytics & Visualization and UEP 0238-01 Data Science for Urban Sustainability. This course will be a 1 SHU short course on introduction to programming in python.
UEP 0237-01 Urban Analytics & Visualization
Instructor: Dr. Shan Jiang
With rapid urbanization, the development of data science, machine learning, and the emergence of ubiquitous sensing technologies, cities have become the foci of multidisciplinary investigations. This course is designed to equip future planners, data scientists, and policymakers with computational methods and tools to acquire new urban data from social media, crowdsourcing, and sensor networks, and use them to represent, understand, and visualize complex urban environments in comprehensive and scientific ways, to make informed decisions to design, plan and manage smart, sustainable and resilient cities. Prerequisites: (1) Intro to GIS; (2) Intro to Programming with Python
UEP 0238-01 Data Science for Urban Sustainability
Instructor: Dr. Shan Jiang
With rapid urbanization, the development of data science, machine learning, and the emergence of ubiquitous sensing technologies, cities have become the foci of multidisciplinary investigations. This course is designed to equip future planners, data scientists, and policymakers with computational methods and tools to acquire new urban data from social media, crowdsourcing, and sensor networks, and use them to represent, understand, and visualize complex urban environments in comprehensive and scientific ways, to make informed decisions to design, plan and manage smart, sustainable and resilient cities. This course is the second half of a one-year series of spatial data science for solving urban challenges. Building upon the toolkits and technical skills (such as Python NumPy, Pandas, and Matplotlib, and SQL etc.) that students learned in “Urban Analytics and Visualization” or other introductory level data science courses, this course is composed of two parts. • The first part will introduce students advanced topics, methods, and tools in spatial data science for urban sustainability with 3 Labs and 2 Assignments. • The second part will provide open-ended projects for students to work in pairs/teams. These real-world projects are developed with research collaborators (including New York City Department of Parks & Recreation, and Boston Region Metropolitan Planning Organization — Central Transportation Planning Staff) whose decision-making impacts the development of communities and cities. Students will apply methods learned in the course to collect, manage, and analyze new and traditional data to develop plans to measure the impacts of human activities and development on urban sustainability. Each team will present their project findings and solutions to the collaborators at the end of the class. This course can also be useful for students to work in conjunction with their theses or capstone projects.