GIS Courses

Instructor: Dr. Laurie Baise (Fall 2025 taught by Shiying Nie while Dr. Baise is on sabbatical)
Email: Laurie.Baise@tufts.edu
Semester offered: Fall

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 or CEE 156 or graduate standing.

Instructor: Ian Spangler, Assistant Curator of Digital & Participatory Geography, Norman B. Leventhal Map & Education Center at the Boston Public Library
Email: Ian.Spangler@tufts.edu
LinkedIn: https://www.linkedin.com/in/ispangler
Semester offered: Spring

Geographic Information Systems (GIS) theory, methods, and technology for applications in the humanities in past and present settings. Topics include GIS data creation and data structures, geodesy, spatial analysis, and cartographic visualization. Includes extensive laboratory exercises to apply concepts presented in the lectures using a variety of GIS software and tools. Assignments concentrate on application of concepts covered in lectures and exercises and include a final project that applies GIS to each student’s field of interest.

For more info on the course, visit Ian’s course GitHub: https://itspangler.github.io/geospatial-humanities-s2024/syllabus/#course-summary

Instructor: Dr. Marcia Moreno-Báez
Email: marcia.morenobaez@tufts.edu
LinkedIn: https://www.linkedin.com/in/mmorenobaez/
Semester(s) offered: Summer, Fall, Spring

This course introduces students to geographic information systems (GIS) with a specific focus on their applications in various international settings and topics. The primary objective of this course is to equip students to explore geospatial technology and related tools, including ESRI ArcGIS Pro, for data creation, management, analysis, and visualization. Throughout the curriculum, students will delve into the dynamic field of GIS through a combination of assignments and interactive discussions, which are organized into modules. This approach enables students to develop an understanding of how geospatial technologies can be utilized across different fields and disciplines. The course also includes a practical component, where students will work on a final project with guided activities. By the end of this course, students will have acquired a relevant skill set that can be readily applied in a wide range of professional contexts. It is worth noting that this course is specifically designed to fulfill the quantitative analysis requirements for both the Master of Arts in Law and Diplomacy (MALD) and the Master of Global Affairs (MGA) programs. Enrollment is limited to 24 students. For more information, visit: https://bit.ly/49cNV1A

Instructor: Dr. Rebecca Shakespeare, Dr. Alexandra Thorn, Dr. Sumeeta Srinivasan (Instructor is dependent on GIS section)
Email(s): rebecca.shakespeare@tufts.edu, sumeeta.srinivasan@tufts.edu, alexandra.thorn@tufts.edu
Semester(s) offered: Fall, Spring

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.

Instructors: Dr. Shikhar Shrestha
Email: shikhar.shrestha@tufts.edu
Semester offered: Fall

In public health, place matters. Place is a close reflection of the social and economic deprivation and environmental exposures that can result in significant health disparities that are manifest in health outcomes, including morbidity and mortality. While uses of geographic information systems (GIS) and spatial epidemiology have increased steeply and steadily within the public health sciences during the past two decades, they are still in their infancy. In health disparities, nutrition, disease prevention, and health services research, this is particularly evident. More than an estimated 80% of health issues have a spatial component; however, only a small fraction of research studies include a focus on the geography of health and spatial associations of putative exposures, access to care, and health outcomes. This course will provide students with the basic skills needed to obtain, analyze, and decipher spatial data in GIS, using a variety of examples from public health, nutrition, urban development, and the US Census Bureau.

Instructor: Carolyn Talmadge, Data Lab Services Manager, Research Technology, Tufts Technology Services
Email: Carolyn.Talmadge@tufts.edu
Semester offered: Fall

This course will introduce students to the fundamental concepts of the Geographic Information Systems (GIS) as it relates to the one health paradigm. This course is designed for novice GIS students with specific focus on mapping and spatial analysis for human, animal, and environmental health applications. Examples include examining the distribution of zoonotic diseases, tracking animal migration patterns, conducting suitability analyses for species reintroduction, risk assessments for contracting Malaria, and many more. The course will cover Desktop ArcGIS software, specifically ArcGIS Pro, and introduce students to a few ESRI applications such as Survey123 and ArcGIS Online. Technical topics to be covered include GIS data discovery; field data collection; data searching and management; principles of cartographic visualization and design; and basic geoprocessing and spatial analysis tools, and poster design. This course is open to all students and faculty from the Veterinary School.

Instructor: Dr. Alexandra Thorn
Email: alexandra.thorn@tufts.edu
Semester offered: Fall

Many problems in agriculture, food and nutrition are inherently geographic in nature. This course introduces Geographic Information Systems (GIS) and its applications. GIS is a combination of software, data, methods and hardware with capabilities for manipulating, analyzing and displaying spatially referenced information. In its simplest applications GIS links spatial location to data. Applications of GIS in agriculture and food systems include analysis of spatial patterns of water and air pollution and foodborne illness resulting from livestock production concentration in large feeding operations, physical access to stores and restaurants for marginalized populations, and problems in nutrition and public health related to hunger hotspots, food deserts, and disease corridors. This course will equip students with the skills needed to capture, analyze and communicate spatial data in geographic information systems (GIS). Note: This course covers the same material as other introductory GIS courses at tufts (e.g. GIS 101 / ENV 107); students may not enroll in both GIS 101 / ENV 107 and NUTR 231 / ENV 231. Prerequisite: Graduate standing or instructor consent.

Instructor: Dr. Thomas Stopka Dr. Laura Corlin, Dr. Shikhar Shrestha
Email: Thomas.Stopka@tufts.edu, Laura.Corlin@Tufts.edu, Shikhar.Shrestha@tufts.edu
Semester offered: Spring

In public health, place matters. Place is a close reflection of the social and economic deprivation and environmental exposures that can result in significant health disparities that are manifest in health outcomes, including morbidity and mortality. While uses of geographic information systems (GIS) and spatial epidemiology have increased steeply and steadily within the public health sciences during the past two decades, they are still in their infancy. In health disparities, nutrition, disease prevention, addiction, environmental health, and health services research, this is particularly evident. More than an estimated 80% of health issues have a spatial component; however, only a small fraction of research studies include a focus on the geography of health and spatial associations of putative exposures, access to care, and health outcomes. This course will provide students with the basic skills needed to obtain, analyze, and decipher spatial data in GIS, using a variety of examples from public health, nutrition, environmental health, urban development, and the US Census Bureau.

This course includes in-person lectures, lab assignments, and guest speakers, as well as individual assessments on approaching specific problems through visualization of spatial data, spatial analytics, and spatial epidemiological perspectives.

Instructor: Dr. Sumeeta Srinivasan , Dr. Rebecca Shakespeare (Instructor is dependent on GIS section)
Email: Sumeeta.Srinivasan@tufts.edu, Rebecca.Shakespeare@tufts.edu
Semester(s) offered: Summer, Fall, Spring

Geographical Information Systems (GIS) theory, methods and their applications. Topics include GIS data structures, geodesy, cartography and spatial analysis using rasters and vectors. Includes extensive laboratory exercises using ArcGIS. Final project using GIS to apply student interests.  Prerequisite: None.

Instructor:Dr. Rebecca Shakespeare
Email:Rebecca.Shakespeare@tufts.edu
LinkedIn:https://www.linkedin.com/in/rshakesp/
Semester(s) offered: Summer, Fall, Spring

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.

Instructor: Dr. Marcia Moreno-Baez
Email: marcia.morenobaez@tufts.edu
LinkedIn: https://www.linkedin.com/in/mmorenobaez/
Semester offered: Spring

CGIS and Spatial analysis offer a unique and insightful perspective on our world, serving as a distinctive lens through which we explore events, patterns, and processes that unfold on or near the Earth’s surface. At its core, spatial analysis resides at the crossroads of human insight and computational power, combining the art of interpretation with the precision of spatial data analysis, modeling, machine learning, deep learning, and AI. This fusion of human expertise with advanced technologies is pivotal in shaping decision-making processes, enabling us to unravel complex spatial phenomena and make informed choices that drive progress and innovation. This course is designed to immerse professionals in training in a rich learning experience, blending lab sessions with hands-on activities and engaging lectures and discussions through a holistic approach. The course not only covers spatial data science but also delves into the intricate realms of machine learning, deep learning, and AI. Participants will gain the knowledge and skills needed to apply these cutting-edge technologies to spatial analyses and decision-making processes. Throughout the course, the emphasis is on practical, real-world applications, including topics such as modeling, suitability analysis, pattern detection, deep learning, neural networks, dynamic visualization, and the nuanced examination of complex spatial datasets. This ensures that students are well-equipped with various quantitative methods, from descriptive to geostatistical methodologies. Finally, decision-making methods and visualization techniques are embedded to ensure we equip students with the practical skills to effectively interpret and communicate complex spatial data. These skills help bridge the gap between data analysis and real-world applications, fostering a deeper understanding of spatial relationships and their impact on decision outcomes. GIS for International Applications is a prerequisite to enroll, and the class is limited to a maximum of 24 students. For more information, visit: https://bit.ly/3u2xki8

Instructor: Dr. Sumeeta Srinivasan
Email: Sumeeta.Srinivasan@tufts.edu
LinkedIn: https://www.linkedin.com/in/sumeeta-srinivasan-3b35134/
Semester offered: Spring

Design and use of spatial information systems to support analytical modeling in 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. Recommendations: GIS (CIS) 101 or equivalent.

Instructor: Dr. Sumeeta Srinivasan
Email: Sumeeta.Srinivasan@tufts.edu
LinkedIn: https://www.linkedin.com/in/sumeeta-srinivasan-3b35134/
Semester(s) offered: Fall, Spring

Spatial analysis methods including raster analysis, suitability analysis, spatiotemporal statistics, geostatistics and network analysis. Laboratory exercises include automation using ArcPy and Model Builder using ArcGIS. Topics in database management including SQL and UML. Group or individual projects based on students’ interests. For students from any discipline with an interest in advanced geospatial modeling and spatial analysis applications. Prerequisites: GIS 201 or equivalent

Instructor: Dr. Sumeeta Srinivasan
Email: Sumeeta.Srinivasan@tufts.edu
LinkedIn: https://www.linkedin.com/in/sumeeta-srinivasan-3b35134/
Semester offered: Spring

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.

Instructor: Dr. Pinde Fu, Adjunct Faculty, Team/Project Lead at ESRI
Email: Pinde.Fu@tufts.edu
LinkedIn: https://www.linkedin.com/in/pinde-fu-2617b86/
Semester offered: Summer

The internet, web, and mobile technologies have significantly changed the way geospatial information is acquired, transmitted, visualized, analyzed, published, and shared. This course aims to provide students with a deep understanding of Web GIS principles, along with the latest geospatial, cloud, and mobile technologies shaping modern Web GIS applications. This course will cover ArcGIS Online and ArcGIS Enterprise cloud GIS platforms, as well as out-of-the-box app builders including ArcGIS Instant Apps, StoryMaps, Experience Builder, Dashboards, Survey123, and Field Maps. Students will utilize HTML, CSS, ArcGIS Arcade, ArcGIS Maps SDK for JavaScript, and webhooks to enhance the user interface and functionality of the out-of-the-box web and mobile GIS apps. Additionally, this course will explore spatial-temporal data, 3D web scenes, spatial data science, and emerging technologies including virtual reality (VR) and augmented reality (AR) within the context of Web GIS.

Course Goals: Provide students with a comprehensive and up-to-date overview of Web GIS, including the basic concepts, principles, related fields, and frontiers. Inspire students with the broad and real-world applications of Web GIS, especially in e-Government and e-Business. Equip students with state-of-the-art technical skills needed to build Web GIS applications and the knowledge required to manage Web GIS projects.

Instructor: Jon Caris
Email: Jon.Caris@tufts.edu
LinkedIn: https://www.linkedin.com/in/joncaris/
Semester offered: Fall

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.

Instructor: Jon Caris
Email: Jon.Caris@tufts.edu
LinkedIn: https://www.linkedin.com/in/joncaris/
Semester offered: Spring (1/2 semester)

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.

Instructor: Dr. Magaly Koch
Email: Magaly.Koch@tufts.edu
Semester offered: Fall

Satellite remote sensing technology and its applications to a variety of fields including urban and land use planning, natural resources monitoring and management, and environmental sciences. Physical processes in remote sensing; optical, thermal and microwave based sensors; image analysis to derive desired information, and applications for geo-environmental studies. 

Instructor: Dr. Magaly Koch
Email: Magaly.Koch@tufts.edu
Semester offered: Spring

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.

Instructors: Dr. Andreia Martinho & Pramesh Singh
Semester(s) offered: Fall, Spring

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.

Instructor: Dr. Shan Jiang
Email: Shan.Jiang@tufts.edu
Semester offered: Fall

Computational methods and tools to both acquire new urban data from social media, crowdsourcing, and sensor networks, and using these tools to represent, understand, and visualize complex urban environments. Focus on learning to make informed decisions to design, plan and then manage smart, sustainable, and resilient cities. Prerequisites: GIS 201; GIS 110 or equivalent

Instructor: Dr. Shan Jiang
Email: Shan.Jiang@tufts.edu
Semester offered: Fall

Spatial data science for solving urban challenges. Builds upon toolkits and technical skills (such as Python NumPy, Pandas, and Matplotlib, and SQL). This course has two parts:

• Introduction to advanced topics, methods, and tools in spatial data science.

• Open-ended, real-world projects for students to work in teams, working with outside collaborators like planning agencies and data providers.

Application of methods to collect, manage, and analyze new and traditional data to develop plans to measure the impacts of human activities on urban sustainability. Each team will present their project findings and solutions to the collaborators. Opportunities for students to work on theses or capstone projects. Prerequisites: GIS 201; GIS 110 or equivalent.