GIS 101/ ENV 107/ INTR 81: Introduction to Geographic Information Systems – Fall 2021 / Spring 2022
UEP 0232: Intro to GIS – Summer 2021 / Fall 2021 / Spring 2022
MCM 591: GIS for Conservation Medicine – Fall 2021
CEE 187: Geographic Information Systems – Fall 2021
Fletcher DHP P207: GIS for International Applications – Fall 2021 / Spring 2022
Nutr 231: Fundamentals of GIS for Food, Agriculture, and Environmental Applications – Fall 2021 / Spring 2022
HIA 219: Spatial Epidemiology – Fall 2021/Spring 2022 (online)
PH 0262: GIS for Public Health – Spring 2022 (1/2 Semester Course)
Fletcher DHP P289: Advanced Geospatial Modeling – Spring 2022
UEP 29422: Advanced Geospatial Modeling– Fall 2021/Spring 2022
GIS 102 / Env 197: Advanced Geospatial Modeling – Spring 2022
UEP 239: Geospatial Programming with Python – Spring 2022
UEP 236: Spatial Statistics – Spring 2022
UEP 294-28: GIS Boot Camp & Qualitative GIS – Spring 2022
UEP 231: Interactive Web Mapping – Fall 2021
MCM-1011: Drones– Unmanned Aircraft Systems (UAS) for Field Data Collection, Mapping & Analysis – Spring 2022
UEP 189 / CEE 189: Introduction to Remote Sensing – Fall 2021
CEE-0293/UEP-0294 Advanced Remote Sensing – Spring 2021
STS-0150: Theory/Tech of Mapping – Not offered Spring 2021
GIS 101/ ENV 107/ INTR 81: Introduction to Geographic Information Systems
Instructor: Dr. Rebecca Shakespeare, Dr. Alexandra Thorn, 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.
UEP 0232 Intro to GIS: GIS for Urban Analysis
Instructor: 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
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.
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.
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
Nutr 231: Fundamentals of GIS for Food, Agriculture, and Environmental Applications
Instructor: Dr. Alex 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-0189 / CEE 189: Introduction to Remote Sensing
Instructor: 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 29422/ GIS 102 / Env 197 Advanced Geospatial Modeling
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.
UEP29415: 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 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.
|UEP 239: Geospatial Programming with Python|
Dr. Aggeliki Barberopoulou
This course will combine an introduction to Python with the fundamentals of spatial analysis. The course will introduce programming constructs using Python. Students will apply these programming skills using high-level toolkits to geoprocessing. Topics will include data types, functions, loops and control structures as well as the basics of object oriented programming in Python. Geospatial topics will include projections and coordinate systems, spatial data structures, overlay and data management as well as vector, raster, surface and network data algorithms. The class will include automation of spatial analysis tasks and the application of Python based opensource tools for individual projects.
CEE-0293/UEP–0294 Advanced Remote Sensing
Instructor: 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.