University of Waterloo

Cartography for Mapping
This course introduces the principles and techniques of cartography, focusing on the design and production of effective maps. Students will learn how to collect, analyze, and visualize spatial data using modern mapping tools and GIS software. Topics include map projections, scale, symbology, color theory, typography, and map interpretation. Emphasis is placed on creating clear, accurate, and aesthetically pleasing maps for various applications in geography, environmental science, and urban planning.

Earth from Space Using Remote Sensing
The course covers the principles, physics, sensor technology, processing, and applications of remote sensing in the electromagnetic spectrum.

Introduction to Geographic Information Systems (GIS)
Introduction to the fundamental concepts and use of Geographic Information Systems (GIS). Students learn about the nature of geographic information and how to store, manipulate and analyze spatial data in a range of application areas. Students will learn underlying theory in lectures and gain a working knowledge of GIS software in lab sessions.

Geodesy and Surveying 
Concepts of geodesy and surveying, Earth's gravity field and the geoid, and measurement techniques applied to geomatics are examined. Field studies include the use of the level, the total station, and GPS for doing distance and angle measurements, leveling, traversing, and topographic surveying.

Multivariate Statistics
The theory and application of multivariate statistics, with particular emphasis upon the use of the computer.

Spatial Analysis
Advanced quantitative analysis in a spatial context. A selection of techniques from sampling, geostatistics, point pattern analysis and cluster detection, spatial classification, and spatial data mining.

Urban Form and Internal Spatial Structure, Geography of Transportation
An examination of the major factors giving rise to distinctive styles of urban spatial organization. Focus moves from city-wide scale to subareas/sectors - inner city, housing, retailing, etc., with emphasis on understanding and planning for the dynamics of complex environments. Applied issues or problems are dealt with throughout the course.

Advanced Remote Sensing Techniques
Advanced image processing techniques of digital remote sensing measurements (e.g. radar systems, optical and infrared systems) from ground, aircraft and satellite instrument systems. Techniques are applied to the study of physical and human environments.

Advanced Geographic Information Systems
Students learn theoretical and operational approaches to advanced spatial analysis using geographical information systems. Emphasis is placed on the use of automation procedures using models and programming to address a variety of topics that may include but are not limited to digital terrain modeling, suitability analysis, network analysis, and cell-based models. The domain of spatial problems explored may vary by instructor.

Spatial Databases
This course focuses on design and development of a GIS database. It addresses theoretical issues regarding data models used in GIS and data modeling techniques used in designing spatial databases. It considers the processing required to input data from a variety of sources and clean and edit a multi-theme database and introduces students to creation and use of internet map services.

Field Research
Field research course in which a specific area will be analyzed from a geographic point of view. Individual or group analysis of specific field problems.

Spatial Demography
This course develops the capacity of students to apply methods of spatial demography. Spatial demography refers to the statistical study of human population using spatial methods for analyzing demographic data. It can provide insights into the understanding of geographic variations of population's characteristics, which in turn can help to make better plans in building the environment. Through this course, students will learn the basic concepts, data sources, data issues, methodologies, and applications of spatial demography.

Changing Form and Structure of Metropolitan Canada
Selected analysis of processes, problems and planning issues associated with the internal growth and spatial reorganization of Canadian metropolitan areas. Three or four topics are chosen for detailed investigation; these will vary from year to year.

Remote Sensing Project
Digital image analysis for resource mapping and evaluation using remote sensing data. Topics range from initial data selection to final map production and assessment. Using commercial image analysis software, students will analyse data for a selected area and produce a portfolio of results. In addition, they will undertake a literature review on a selected topic and present highlights of the review at an end-of-term mini-conference.

Geographic Information Systems Project
The development, implementation, and presentation of a response to a set of GIS related project requirements is the focus of this course. Students work in small teams to enhance and develop their abilities to work with GIS and related spatial technologies and analytical methods in an advanced project setting. The nature of the project requirements and themes varies with faculty and student strengths and interests. Projects may emphasize development of software applications, use of programming, or advanced GIS analysis methods, and draw from theme areas such as environment studies and management, human and physical geography, or planning.

Civic Technology and Digital Infrastructures
A critical approach to the development, implementation, and evaluation of civic technology and smart cities, with a focus on practical implementation considerations. Topics covered include open data, urban data collection and analysis platforms, digital inequalities, locational privacy, and digital infrastructures.

Machine Learning in Geospatial Science
An in-depth study of current machine learning algorithms and their applications in geospatial science, with a focus on earth observation data processing and analysis. Topics include k-nearest neighbour, decision trees, support vector machines, ensemble learning, and some deep neural networks (e.g., CNN, U-Net). Machine learning algorithms implemented using Python will be applied for semantic segmentation, land use and land cover classification, and building and road detection using aerial and satellite images.

Management Issues in Geographic Information Systems
Built around a set of key issues in the management of Geographic Information Systems (GIS). Focuses on middle management concerns and covers topics including GIS needs assessment, benchmarking, the law and spatial data, spatial data warehousing, multi-user GIS modelling and GIS application development. Uses of GIS in both public and private sector organizations are covered.

Environmental Applications of Data Management and Statistics
This course introduces techniques for collecting, evaluating, and using data-based evidence in environmental research, including descriptive statistics (measures of centre, variation and shape, and measures of association between two variables), statistical research designs, sampling theory, and fundamental probability theory for inferential statistics. The course also develops skills in using statistical software for data display and analysis.

Applied Statistics for Environmental Research
This course examines further techniques for collecting, evaluating, and using data-based evidence in environmental research. It builds upon ENVS 178, with a focus on inferential statistics, including sampling distributions, confidence intervals, parametric and nonparametric hypothesis tests, and linear regression models. It further develops skills in using statistical software for data analysis and modeling of environmental data.

Introduction to Computers and Computer Systems
An introduction to hardware and software concepts used in computer systems. Specific topics include machine-level programming, memory organization, and basic I/O mechanisms.

Data Types and Structures
This course introduces widely used and effective methods of data organization, focusing on data structures, their algorithms, and the performance of these algorithms

Computer Applications in Business: Databases
The main objective of this course is to introduce students to fundamentals and use of database technology by studying databases from the viewpoint of a database user. It teaches the use of a database management system (DBMS) by treating it as a black box, focusing only on its functionality and its interfaces.

Linear Algebra
Vectors in 2- and 3-space and their geometry. Linear equations, matrices, and determinants. Introduction to vector spaces. Eigenvalues and diagonalization. Applications. Complex numbers.

Calculus 1 for Sciences
Functions of a real variable: powers, rational functions, trigonometric, exponential and logarithmic functions, their properties and inverses. Intuitive discussion of limits and continuity. Definition and interpretation of the derivative, derivatives of elementary functions, derivative rules and applications. Riemann sums and other approximations to the definite integral. Fundamental theorems and antiderivatives; change of variable. Applications to area, rates, average value.

Calculus 2 for Sciences
Transforming and evaluating integrals; application to volumes and arc length; improper integrals. Separable and linear first order differential equations and applications. Introduction to sequences. Convergence of series; Taylor polynomials, Taylor's Remainder theorem, Taylor series and applications. Parametric/vector representation of curves; particle motion and arc length. Polar coordinates in the plane.