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Modelling of Complex Social Systems

Graduate Certificate

The Modelling of Complex Social Systems (MoCSSY) certificate program is an interdisciplinary graduate program crosscutting the study of social issues in criminology, health sciences, urban dynamics, computing science, and mathematical modelling under the unifying theme of modelling the complex dynamics in urban neighbourhoods.

The program is offered at the Burnaby campus.

For further information, visit

Program Requirements

Students are required to satisfy all graduate requirements of their home department’s graduate studies program.

Students are expected to participate in the MoCSSy graduate seminar series and workshops for at least one term of each year while they are in the program.  Typically, this would mean that master of science (MSc) students will participate at least twice, while doctoral (PhD) students will participate at least four times. However, some flexibility will be granted to students who join the program near their graduation.

Course Lists

Students complete five courses chosen from the courses below, at least four of which must be graduate courses. A maximum of three courses may be from the student’s home department (consult with the MoCSSy program director regarding prior course credit eligibility). For students within the Faculty of Applied Sciences or the Faculty of Science, a minimum of two courses will be in departments outside of these two faculties. For students outside the Faculty of Applied Sciences and the Faculty of Science, a minimum of two courses will be in courses offered in departments inside of these two faculties.

Previously completed courses that were used to meet the requirements of earlier degrees will not count toward these requirements.

Computing Science

CMPT 305 - Computer Simulation and Modelling (3)

This course is an introduction to the modelling, analysis, and computer simulation of complex systems. Topics include analytic modelling, discrete event simulation, experimental design, random number generation, and statistical analysis. Prerequisite: CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and STAT 270.

CMPT 310 - Artificial Intelligence Survey (3)

Provides a unified discussion of the fundamental approaches to the problems in artificial intelligence. The topics considered are: representational typology and search methods; game playing, heuristic programming; pattern recognition and classification; theorem-proving; question-answering systems; natural language understanding; computer vision. Prerequisite: CMPT 225 and (MACM 101 or ENSC 251 and ENSC 252)). Students with credit for CMPT 410 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Toby Donaldson
Mo, We, Fr 10:30 AM – 11:20 AM
AQ 3181, Burnaby
CMPT 454 - Database Systems II (3)

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354.

Section Instructor Day/Time Location
D100 Tianzheng Wang
Tu 1:30 PM – 2:20 PM
Th 12:30 PM – 2:20 PM
BLU 9660, Burnaby
BLU 9660, Burnaby
CMPT 456 - Information Retrieval and Web Search (3)

Introduction to the essentials of information retrieval and the applications of information retrieval in web search and web information systems. Topics include the major models of information retrieval, similarity search, text content search, link structures and web graphics, web mining and applications, crawling, search engines, and some advanced topics such as spam detection, online advertisement, and fraud detection in online auctions. Prerequisite: CMPT 354.

CMPT 471 - Networking II (3)

This course covers the fundamentals of higher level network functionality such as remote procedure/object calls, name/address resolution, network file systems, network security and high speed connectivity/bridging/switching. Prerequisite: CMPT 300 and 371.

Section Instructor Day/Time Location
D100 Janice Regan
Mo, We, Fr 2:30 PM – 3:20 PM
BLU 9660, Burnaby
CMPT 705 - Design and Analysis of Algorithms (3)

The objective of this course is to expose students to basic techniques in algorithm design and analysis. Topics will include greedy algorithms, dynamic programming, advanced data structures, network flows, randomized algorithms. Students with credit for CMPT 706 may not take this course for further credit.

CMPT 721 - Knowledge Representation and Reasoning (3)

Knowledge representation is the area of Artificial Intelligence concerned with how knowledge can be represented symbolically and manipulated by reasoning programs. This course addresses problems dealing with the design of languages for representing knowledge, the formal interpretation of these languages and the design of computational mechanisms for making inferences. Since much of Artificial Intelligence requires the specification of a large body of domain-specific knowledge, this area lies at the core of AI. Prerequisite: CMPT 310/710 recommended. Cross-listed course with CMPT 411.

CMPT 740 - Database Systems (3)

Introduction to advanced database system concepts, including query processing, transaction processing, distributed and heterogeneous databases, object-oriented and object-relational databases, data mining and data warehousing, spatial and multimedia systems and Internet information systems.

CMPT 741 - Data Mining (3)

The student will learn basic concepts and techniques of data mining. Unlike data management required in traditional database applications, data analysis aims to extract useful patterns, trends and knowledge from raw data for decision support. Such information are implicit in the data and must be mined to be useful.

CMPT 745 - Software Engineering (3)

This course examines fundamental principles of software engineering and state-of-the-art techniques for improving the quality of software designs. With an emphasis on methodological aspects and mathematical foundations, the specification, design and test of concurrent and reactive systems is addressed in depth. Students learn how to use formal techniques as a practical tool for the analysis and validation of key system properties in early design stages. Applications focus on high level design of distributed and embedded systems.

CMPT 764 - Geometric Modelling in Computer Graphics (3)

Advanced topics in geometric modelling and processing for computer graphics, such as Bezier and B-spline techniques, subdivision curves and surfaces, solid modelling, implicit representation, surface reconstruction, multi-resolution modelling, digital geometry processing (e.g., mesh smoothing, compression, and parameterization), point-based representation, and procedural modelling. Prerequisite: CMPT 361, MACM 316. Students with credit for CMPT 464 or equivalent may not take this course for further credit.

CMPT 767 - Visualization (3)

Advanced topics in the field of scientific and information visualization are presented. Topics may include: an introduction to visualization (importance, basic approaches and existing tools), abstract visualization concepts, human perception, visualization methodology, 2D and 3D display and interaction and their use in medical, scientific, and business applications. Prerequisite: CMPT 316, 461 or equivalent (by permission of instructor). Students with credit for CMPT 878 or 775 may not take this course for further credit.

CMPT 771 - Internet Architecture and Protocols (3)

Investigates the design and operation of the global network of networks: the Internet. This course studies the structure of the Internet and the TCP/IP protocol suit that enables it to scale to millions of hosts. The focus is on design principles, performance modelling, and services offered by the Internet.

CMPT 813 - Computational Geometry (3)

This course covers recent developments in discrete, combinatorial, and algorithmic geometry. Emphasis is placed on both developing general geometric techniques and solving specific problems. Open problems and applications will be discussed.

CMPT 814 - Algorithmic Graph Theory (3)

Algorithm design often stresses universal approaches for general problem instances. If the instances possess a special structure, more efficient algorithms are possible. This course will examine graphs and networks with special structure, such as chordal, interval, and permutation graphs, which allows the development of efficient algorithms for hard computational problems.

CMPT 815 - Algorithms of Optimization (3)

This course will cover a variety of optimization models, that naturally arise in the area of management science and operations research, which can be formulated as mathematical programming problems. Equivalent Courses: CMPT860.

CMPT 816 - Theory of Communication Networks (3)

This course investigates the design, classification, modelling, analysis, and efficient use of communication networks such as telephone networks, interconnection networks in parallel processing systems, and special-purpose networks. Equivalent Courses: CMPT881.

Graduate students from computing science will complete 700/800 division courses.


CRIM 410 - Decision-making in Criminal Justice (3)

Examination of the factors which influence decision making in the criminal justice system. The exercise of discretion by criminal justice personnel; the role of organizational policies and priorities in decision making; the involvement of victims and the public. Consideration of decision making at specific stages of the criminal justice process. Prerequisite: CRIM 131.

CRIM 413 - Terrorism (3)

Considers the nature, extent, and basis of terrorism as an official crime throughout the world and its impact upon criminal justice systems. Theoretical explanations in a comparative perspective will be employed to examine the impact of terrorism on various countries and the response of governments to it. Prerequisite: CRIM 101.

CRIM 454 - Criminal Profiling (3)

Provides an overview of the advanced issues relating to the scientific study, development and evaluation of criminal profiling. Outlines the criminological and psychological principles upon which criminal profiling is based, including classification of violent behaviour, behavioural change and consistency. Prerequisite: CRIM 101.

CRIM 457 - Crime and Criminal Intelligence Analysis (4)

Examines data handling, data quality and analysis of various criminal justice system information sources common to police services, government agencies and academic researchers. Develops skills in tactical, strategic and administrative crime analysis functionality. Prerequisite: CRIM 352 is recommended. Students who have taken CRIM 418 under this topic may not take this course for further credit.

CRIM 800 - Criminological Theory I (3)

Intensive exposure to the major streams of criminological theory, the role of theory, and how theory develops, evolves, and changes. Consideration will be given to the relationship between criminological theories, their testable hypotheses and empirical support, as well as the interplay of theory and practice. Students enrolling in this course are expected to have a solid background in undergraduate criminological theory. Equivalent to CRIM 300W.

CRIM 810 - Spatial-temporal criminology (3)

An examination of research on crime that considers spatial and temporal dimensions. Subject matter will vary according to instructor interests and specialization. Specific areas of concentration may include: environmental criminology, communities and crime, neighborhoods and crime, ecology of crimes, and crime prevention.

CRIM 811 - Young Offenders (3)

An examination of contemporary issues in the context of young offenders and justice. Subject matter will vary according to instructor interest and specialization. Specific areas of concentration may include: social control theory and developmental life course theories and juvenile justice models; young offender legislation in Canada; neuropsychology and young offenders; serious and violent young offenders; and the assessment of theories of rehabilitation.

CRIM 820 - Criminal Justice Policy Analysis (3)

An introduction to policy analysis in the field of criminal justice, beginning with frames of reference for policy-making such as the market, welfare economics, equity, efficiency, and liberty. Through applied examples, students will define policy problems, identify goals and objectives, devise alternative solutions, predict the effects of these alternatives, and communicate advice to decision-makers.

CRIM 862 - Research Methods III: Qualitative Methods (3)

A range of research techniques generally subsumed under the rubric of qualitative research including field research, interview techniques, historical and legal research, and documentary analysis. Emphasis will be on the logic underlying such inquiry, the advantages and limitations associated with different sources of information and procedures, and the processes by which analytical rigor is achieved. Students enrolling in this course are expected to have a solid background in undergraduate qualitative research methods, equivalent to CRIM 321.

Graduate students in criminology will complete 800 division courses.


GEOG 352 - Spatial Analysis (4)

Advanced quantitative techniques for spatial analysis of geographic data and patterns. Topics include geostatistics, spatial interpolation, autocorrelation, kriging, and their use in geographic problem solving with spatial analysis software. Prerequisite: GEOG 251 or one of STAT 201, 203 (formerly 103), 205, or 270. Quantitative.

GEOG 451 - Spatial Modeling (4)

Spatial models for the representation and simulation of physical, human and environmental processes. GIS and spatial analysis software are used in the laboratory for model development, from problem definition and solution to visualization. Prerequisite: GEOG 251 or one of STAT 201, 203 (formerly 103), 205, or 270; one of GEOG 351, 352, 353, 355 or 356. Quantitative.

GEOG 453 - Theoretical and Applied Remote Sensing (4)

Examination of advanced topics in remote sensing, including calibration /validation, spatial scale, data fusion, and the role of remote sensing in a spatial world. Students will work on independent projects applying remote sensing in their area of interest. Prerequisite: GEOG 353. Recommended: One of GEOG 351, 352, 355 or 356. Students with credit for GEOG 453W may not repeat this course for further credit. Quantitative.

GEOG 455 - Theoretical and Applied GIS (4)

A critical examination of advanced topics in GIS, such as: boundary definition, expert systems and artificial intelligence, error and uncertainty, and scale in a digital context. Examines social applications and the roles of GIS in society. Students will design original projects, including data acquisition, analysis, and web site development. Prerequisite: GEOG 355. Students with credit for GEOG 452 or GEOG 455W may not take this course for further credit. Quantitative.

GEOG 457 - Geovisualization Interfaces (4)

The concepts, theories, and technology behind interactive and immersive interface technologies used for geospatial visualization. Applications and implications for GIScience and spatial knowledge acquisition. Combines GIScience, spatial cognition, and virtual environments/interface research perspectives. Prerequisite: GEOG 356. Students with credit for GEOG 457 (STT) Geospatial Virtual Environments in fall 2005 or fall 2006 may not take this course for further credit.

GEOG 606 - Research Design and Analytical Techniques in Physical Geography (5)

Research design, data collection and quantitative methods in physical geography. Equivalent Courses: GEOG706.

GEOG 651 - Advanced Spatial Analysis and Modeling (4)

Perspectives on the description, analysis and prediction of geographical processes using spatial modeling and decision-making in a GIS environment. Equivalent Courses: GEOG714.

GEOG 653 - Remote Sensing of Environment (4)

Selected principles and applications of remote sensing for the study of natural and human environments.

GEOG 655 - Advanced Principles of Geographic Information Science (4)

Examines data, data structures and computational methods that underlie GIS description and analysis. Illustrates the social science and science links between computers and geography. Equivalent Courses: GEOG715.

Graduate students in geography will complete 600 division courses.

Health Sciences

HSCI 802 - Principles of Epidemiology for Public Health (4)

The underlying concepts and methods of epidemiology in the context of population and public health. Students will acquire skills in the critical interpretation of the epidemiologic literature, methods for measuring disease frequency and effect, evaluation of study design, and assessment of bias and confounding.

HSCI 803 - Qualitative and Survey Research Methods (4)

Methodologies and strategic research design for advances in knowledge and understanding in the health sciences. Problem definition, sampling, data collection, analysis, proposal writing, and ethical issues are addressed. Provides experiential and intellectual grounding in surveys, interviews, focus groups, and ethnography. Prerequisite: Admission to the graduate program or permission of the Instructor.

HSCI 805 - Intermediate Epidemiologic Methods (3)

Follow-up course to HSCI 802. Designing, conducting, analyzing, and interpreting epidemiologic research. Theoretical frameworks, concepts of inference, measures of disease occurrence and effect, study designs, issues in measurement, bias, confounding, and interaction. Critical assessment of the epidemiologic and public health literature. Prerequisite: HSCI 801 and 802.

HSCI 826 - Program Planning and Evaluation (3)

Practical approaches to health needs assessment, needs prioritization, health program planning, and health program evaluation in low-to-middle income countries and/or resource-poor settings. Gender-based analyses are emphasized throughout. A case study approach. Prerequisite: Admission to the graduate program or permission of the instructor.

Section Instructor Day/Time Location
G100 Tatiana Popovitskaia
Mo, We 4:30 PM – 7:20 PM
HCC 1525, Vancouver
HSCI 827 - Analysis of Health Care Systems (3)

Analyze factors that determine the organization and performance of health services for individuals and populations.

HSCI 835 - Social Determinants of Health (3)

Examination of the major social determinants of health: income and social status, social support networks, education, employment/working conditions, social environments, physical environments, healthy child development, disabilities, gender, racialization, colonization, and culture. Evaluation of empirical research linking social determinants of health to health outcomes. Theoretical frameworks are introduced to operationalize social factors and their intersections for the purposes of application in public health research.

HSCI 845 - Environmental and Occupational Health (3)

Globalization and industrialization impacts on the health of the environment, populations, and workers. Environmental hazards in consumables (food, air, and water) and waste (liquid, solid, and gaseous) with special reference to hazardous waste. Risk assessment in community, workplace, and residential settings. A case studies approach. Prerequisite: Admission to the graduate program or permission of the instructor.


MACM 316 - Numerical Analysis I (3)

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Section Instructor Day/Time Location
D100 Benjamin Adcock
Mo, We, Fr 10:30 AM – 11:20 AM
D101 Mo 2:30 PM – 3:20 PM
WMC 2830, Burnaby
D102 Mo 3:30 PM – 4:20 PM
WMC 2830, Burnaby
D103 Tu 10:30 AM – 11:20 AM
WMC 2830, Burnaby
D104 Tu 11:30 AM – 12:20 PM
WMC 2830, Burnaby
D105 Tu 9:30 AM – 10:20 AM
WMC 2830, Burnaby
D106 Tu 1:30 PM – 2:20 PM
WMC 2830, Burnaby
D107 Mo 4:30 PM – 5:20 PM
WMC 2830, Burnaby
D108 Mo 5:30 PM – 6:20 PM
WMC 2830, Burnaby
D109 Mo 11:30 AM – 12:20 PM
WMC 2830, Burnaby
MACM 409 - Numerical Linear Algebra: Algorithms, Implementation and Applications (3)

Development of numerical methods for solving linear algebra problems at the heart of many scientific computing problems. Mathematical foundations for the use, implementation and analysis of the algorithms used for solving many optimization problems and differential equations. Prerequisite: MATH 251, MACM 316, programming experience. Quantitative.

MACM 416 - Numerical Analysis II (3)

The numerical solution of ordinary differential equations and elliptic, hyperbolic and parabolic partial differential equations will be considered. Prerequisite: MATH 310 and MACM 316. Quantitative.

MATH 308 - Linear Optimization (3)

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232. Quantitative.

MATH 309 - Continuous Optimization (3)

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251. Quantitative.

MATH 348 - Probabilistic Models in Operations Research (3)

Inventory theory, Markov decision process and applications, queuing theory, forecasting models, decision Analysis and games, probabilistic dynamic programming, simulation modeling, project planning using PERT/CPM, sequencing and scheduling. Prerequisite: STAT 270. Pre-/Co-requisite: MATH 308. Quantitative.

MATH 408 - Discrete Optimization (3)

Model building using integer variables, computer solution, relaxations and lower bounds, heuristics and upper bounds, branch and bound algorithms, cutting plane algorithms, valid inequalities and facets, branch and cut algorithms, Lagrangian duality, column generation of algorithms, heuristics algorithms and analysis. Prerequisite: MATH 308. Quantitative.

MATH 445 - Graph Theory (3)

Graph coloring, Hamiltonian graphs, planar graphs, random graphs, Ramsey theory, extremal problems, additional topics. Prerequisite: MATH 345. Quantitative.

MATH 448 - Network Flows (3)

Applications of network flow models; flow decomposition; polynomial algorithms for shortest paths, maximum flows and minimum costs flows; convex cost flows; generalized flows, multi-commodity flows. Prerequisite: MATH 308. Recommended: MATH 345. Quantitative.

MATH 708 - Discrete Optimization (3)

Held jointly with MATH 408-3. See description for MATH 408-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

MATH 709 - Numerical Linear Algebra and Optimization (3)

Held jointly with MACM 409-3. See description for MACM 409-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

MATH 716 - Numerical Analysis II (3)

The numerical solution of ordinary differential equations and elliptic, hyperbolic and parabolic partial differential equations will be considered. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

MATH 745 - Graph Theory (3)

Graph coloring, Hamiltonian graphs, planar graphs, random graphs, Ramsey theory, extremal problems, additional topics. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

MATH 748 - Network Flows (3)

Held jointly with MATH 448-3. See description for MATH 448-3. Students may not take a 700 division course if it is being offered in conjunction with a 400 division course which they have taken previously.

Graduate students in mathematics will complete 700 division courses.


STAT 302 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: Any STAT course (except STAT 100) or BUEC 232. Quantitative.

Section Instructor Day/Time Location
C100 Distance Education
D100 Michael Davis
Tu 2:30 PM – 4:20 PM
Th 2:30 PM – 3:20 PM
STAT 350 - Linear Models in Applied Statistics (3)

Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240. Quantitative.

STAT 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or BUEC 333. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. Quantitative.

STAT 410 - Statistical Analysis of Sample Surveys (3)

An introduction to the major sample survey designs and their mathematical justification. Associated statistical analyses. Prerequisite: STAT 350. Quantitative.

STAT 430 - Statistical Design and Analysis of Experiments (3)

An extension of the designs discussed in STAT 350 to include more than one blocking variable, incomplete block designs, fractional factorial designs, and response surface methods. Prerequisite: STAT 350 (or MATH 372). Quantitative.

STAT 650 - Quantitative Analysis in Resource Management and Field Biology (5)

The use of statistical techniques and mathematical models in resource management with special emphasis on experimentation, survey techniques, and statistical model construction. This course may not be used for the satisfaction of degree requirements in the Department of Statistics and Actuarial Science. Prerequisite: A course in parametric and non-parametric statistics.

STAT 832 - Applied Probability Models (4)

Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.

STAT 855 - Lifetime Data Analysis (4)

Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Prerequisite: STAT 450. Students with credit for STAT 806 may not take this course for further credit.

Graduate students in statistics will complete 800 division courses.