Students will examine a variety of topics critical to successful navigation of the geospatial profession, with an emphasis on map communication and presentation, interpreting geospatial research, the ethical, legal, and social implications (ELSI) of using spatial data, metadata concepts, and linking results to policy actions. Students will engage in several writing, presentation, and interpretation exercises.

Typically offered in Fall and Spring

This course provides an advanced overview of how geographic information systems [GIS] facilitate data analysis and communication to address common geographic problems. Students improve spatial reasoning and problem definition expertise while emphasizing geographic data models and structures, data manipulation and storage, customization through programming, and the integration of geospatial analysis and modeling into project-based problem solving applicable to a variety of disciplines. Skilled application of both desktop and cloud-based GIS software supports these areas. Extensive independent learning and computer experiences include virtual laboratory sessions, alongside optional online or in-person weekly help sessions to facilitate student learning.

Prerequisite: Graduate Standing or PBS or Permission of Instructor

Typically offered in Fall and Spring

Exploration of the intersection between coding and Geographic Information Systems. This course provides a broad overview of coding platforms commonly used in geospatial applications, with an emphasis on basic Python programming skills for geospatial tasks related to spatial data science and visualization. Prior programming experience is not required or expected. Some GIS experience is preferred. Students in the GIS and Spatial Data Science minor may opt to take this course in the same semester as GIS 450 when available.

Prerequisite: GIS 280

Typically offered in Fall only

Principles and hands-on techniques for processing and analyzing remotely sensed data for natural resource applications. Topics include review of the electromagnetic spectrum, pre-processing (georectification, enhancements and transformations), processing (visual interpretation, indices, supervised and unsupervised classification) and post-processing (masking, change analysis and accuracy assessment) of digital image data. This course will provide students with fundamental concepts and skills needed to pursue further studies in digital processing of remotely sensed data.

Typically offered in Spring only

Principles of cartographic design and how to apply them to produce high-quality geographic information system (GIS) based maps. Successful students will acquire an understanding of map design and experience applying it with GIS software. Students produce project maps in both print and web media.

Prerequisite: GIS 510

Typically offered in Spring only

Introduction to the methods and applications of geographic spatial modeling technology in landscape architecture and environmental planning.

Typically offered in Fall only

Focus on spatial problem solving from a geographic information perspective. Students learn to solve spatial problems through advanced analysis using geospatial technologies, learn to integrate and analyze spatial data in various formats, and explore methods for displaying geographic data analysis results to guide decision making. All course materials are delivered through the Internet, with optional weekly on-campus and synchronous online help sessions.

Prerequisite: GIS 510 or PA 541 or SSC 440

Typically offered in Fall and Spring

The application of geographic information systems (GIS) to surface water modeling including stream and watershed delineations, regulatory wetlands jurisdiction determinations, and flood mapping. In addition students will develop spatial computation methods to support hydrological analysis in land use planning, landscape management, and engineering assessments.

Prerequisite: GIS 510 or PA 541 or SSC 440

Typically offered in Fall and Spring

This course focuses on geospatial information systems from a mathematical and information science perspective. We discuss theoretical frameworks for conceptualizing geographic data, including levels of measurement, data control, and the vector data and raster data paradigms. Then we discuss the geometric underpinnings of geospatial systems: representing data with geographic elements, spatial referencing systems, and projection. Next, we explore map-related topology and computational geometry concepts. Finally, we survey the algorithms for core spatial manipulations, such as interpolation and polygon operations.

Prerequisite: GIS 510 or PA 541 or SSC 440

Typically offered in Fall and Spring

This course provides the background and foundation necessary for geospatial analysis, with emphasis on spatial statistics. Introduction to data handling techniques, conceptual and practical geospatial data analysis and GIS in research will be provided. Problems raised by the use of geospatial data will be introduced to provide an awareness of issues, their consequences, and potential solutions. The focus of this course is application and interpretation of analytical methods, rather than derivation of techniques. Students will also explore the interoperability between open source analytical platforms (such as R) and GIS platforms, in addition to other open source software. Students should expect weekly assignments, lectures, and hands-on training using GIS and statistical software. Prior knowledge in basics of GIS is recommended. Topics include descriptive and inferential statistical methods for geospatial data.

Prerequisite: GIS 510

Typically offered in Spring only

This course examines the design, development and deployment of web and mobile geospatial applications using internet and web-based protocols. Throughout the course, students will develop and deploy web and mobile GIS maps and applications relevant to their career using on-premises hosted infrastructure. Course participants will be required to complete assignments with data relevant to their interests. Additionally, students will search for and examine scientific and popular literature to understand how the course concepts are being employed and to foster ideas and discussion.

Prerequisite: GIS 510

Typically offered in Fall only

This course equips students with essential skills for geospatial programming. Topics include computer programming to call geospatial processing tools, batch process, performing file reading/writing, and generating displays. To support these tasks, students learn basic programming concepts, such as pseudocode, flow-control, code reuse, and debugging. In the final project, students streamline GIS workflows and customize GIS user interfaces. Familiarity with GIS software is required, but no prior programming experience is expected.

Prerequisite: GIS 510

Typically offered in Fall and Spring

This course examines the spatial database models and structures used in geospatial information science and technology as well as the design and implementation of web and related mobile computing geospatial tools and systems. Students develop, evaluate, and deploy multiple spatial data models and web services that include connections to external data sources and systems.

Prerequisite: GIS 540

Typically offered in Fall and Spring

The course provides foundations in methods for GIS-based surface analysis and modeling. The topics include proximity analysis with cost surfaces and least cost paths, multivariate spatial interpolation and 3D surface visualization. Special focus is on terrain modeling, geomorphometry, solar irradiation, visibility, and watershed analysis. Students are also introduced to the basic concepts of landscape process modeling with GIS and to the principles of open source GIS. Introductory level knowledge of GIS or surveying/ geomatics principles is required.

Typically offered in Fall and Spring

The course provides an overview of UAS mapping technology and its rules and regulations. The principles of UAS data collection are explained along with optional hands-on practice with in flight planning and execution. The main focus is on processing imagery collected from UAS using structure from motion techniques and deriving orthophoto mosaics and ultra-high resolution digital elevation models of land surface, vegetation and structures. More advanced topics include multi-temporal 3D data analysis, fusion with lidar data and 3D visualization.

Prerequisite: GIS 510 or GIS/MEA 582 or Permission of Instructor

Typically offered in Fall only

This is the culmination course for The Master of Geospatial Information Science and Technology degree. This course provides students with the opportunity to demonstrate their accumulated degree skills and expertise by developing and communicating the solution to a complex geospatial problem through a Master's Capstone project. The project will include interoperable spatial and non-spatial data, web services, customized user interfaces and workflows completed in collaboration with a community partner. The student will design and manage a major project and professionally communicate their analysis and results to a public audience.

Prerequisite: GIS 550

Typically offered in Fall and Spring

Special Topics in Geospatial Information Science

Typically offered in Fall and Spring

Seminar in Geospatial Information Science

Typically offered in Fall and Spring

The Geospatial Forum brings together researchers, educators, practitioners, and students of the geospatial sciences in an exciting, weekly series of lively presentations and facilitated discussions centered upon frontiers in geospatial analytics and geospatial solutions to complex challenges. Live discussions are recorded and made available online for students.

Typically offered in Fall and Spring

Special Topics in Geospatial Information Science

Typically offered in Fall and Spring

Advanced topics not otherwise included in curriculum for advanced graduate students on a tutorial basis. Determination of credits and content by participating faculty in consultation with Director of Graduate Programs. Departmental consent required

Typically offered in Fall, Spring, and Summer

This course will focus on creating an effective digital portfolio, including content selection, description and reflection, and web site organization and design. The digital portfolio will present personal MGIST program accomplishments to demonstrate individual competences through knowledge, skills, and abilities of a geospatial science professional. Intended for students in their last semester in the MGIST Program.

Restriction: Graduate Student in the MGIST Program; Corequisite: GIS 590

Typically offered in Fall and Spring

Examination of sustainable solutions to grand societal challenges using geospatial analytics. Emphasis is placed on the roles that location, spatial interaction, and multi-scale processes play in scientific discovery and communication. Discussion of seminal and leading-edge approaches to problem-solving is motivated by grand challenges such as controlling the spread of emerging infectious disease, providing access to clean water, and creating smart and connected cities. Students also engage in several written and oral presentation activities focused on data science communication skills and professionalization.

Typically offered in Fall only

Data management principles and technologies for efficient implementation of geospatial applications. This course introduces students to: spatial and temporal data types, data models, geometry models, spatial predicates, spatial access methods, and spatial query processing. In addition, students will be exposed to modern data management systems for geospatial application development and data integration principles. Prior GIS programming knowledge and knowledge of database management systems and SQL is preferred.

Typically offered in Spring only

Focus is on passive electro-optical (microwaves, infrared and visible) remote sensing and will cover the physics of remote sensing, light interactions with Earth surface materials, limitations, advantages and disadvantages of passive remote sensing techniques, estimation of bio/geo-physical parameters from remote sensing data, and sensor performance and mission design for applications including hydrology, cryosphere, atmosphere-ocean dynamics, ecosystems and carbon cycle, and land use land cover change. Students should have introductory knowledge of GIS and remote sensing.

Typically offered in Fall only

This course equips students with the theoretical background and practical computational skills required to use data mining methodologies, including clustering, PCA, spatial autocorrelation, neural networks, classification and regression trees, and high performance, open source geocomputation. The course is designed around, and pays particular attention to, approaches for data with spatial components. Students are expected to have a working knowledge of basic geographic principles, statistical principles, GIS, and remote sensing. Some experience with R programming would also be beneficial.

Typically offered in Fall only

This course focuses on theoretical concepts and computational methods that describe, represent and simulate the functioning of real-world geospatial processes. We define the general properties of geospatial computing and explain the role of simulations in analysis and understanding of observed spatial phenomena, testing of hypotheses and theories, and prediction of spatio-temporal systems behavior. We discuss the current methods and techniques for simulations using deterministic, stochastic and rule-based models as well as agent-based simulation of complex systems. Hands-on component of the course will cover implementation of simulations in GIS and advanced applications driven by the student's research. Some prior programming experience is expected along with exposure to geospatial modeling, such as in GIS/MEA 582 or equivalent.

Restriction: 15GAPHD or Permission of Instructor

Typically offered in Spring only

Geovisualizations are a powerful way to reveal patterns in geospatial data, attract attention, and convey a message to an audience quickly and clearly. This course equips students to make informed design decisions (based on visual feature hierarchy, color theory, and design principles) and automate map-making techniques for multi-layered data, multivariate data, spatial-temporal data, and data with uncertainty. Students are expected to have a working knowledge of Geographic Information Systems. Some experience with Python programming would also be beneficial.

Restriction: Graduate standing in Geospatial Analytics or Permission of Instructor

Typically offered in Spring only

Special Topics in Geospatial Analytics

Typically offered in Fall, Spring, and Summer

Special topics in Geospatial Analytics

Typically offered in Fall, Spring, and Summer

Teaching experience under the mentorship of faculty who assist the student in planning for the teaching assignment, observe and provide feedback to the student during the teaching assignment, and evaluate the student upon completion of the assignment.

Typically offered in Fall and Spring

Dissertation Research

Typically offered in Fall, Spring, and Summer