Courses

Pre-Requisite Course(s)

DATA 118000
Introduction to Data Science I
Data science provides tools for gaining insight into specific problems using data, through computation, statistics, and visualization. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation, and communication of results. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Although this course is designed to be at the level of general education in the mathematical sciences courses, with little background required, we expect the students to develop computational skills that will allow them to analyze data. Computation will be done using Python and Jupyter Notebook.

STAT 22000
Statistical Methods and Applications
This course introduces statistical techniques and methods of data analysis including the use of statistical software. Examples are drawn from the biological, physical, and social sciences. Students are required to apply the techniques discussed to data drawn from actual research. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, basic probability, random variables and expected values, confidence intervals, and significance tests for one- and two-sample problems for means and proportions, chi-square tests, linear regression, and, if time permits, analysis of variance.

Core Methods

GISC 28100 (GISC 38100; ARCH 28202)
Introduction to Geocomputation
Yue Lin / M W 1:30–2:50
This course investigates the theory and practice of computational approaches in Geographic Information Science. Geocomputation is introduced as a multidisciplinary systems paradigm necessary for solving complex spatial problems and facilitating new understandings. Students will learn about the elements of geographic data models, geospatial topologies, spatial operations, visualizations, and their implementation in Python using libraries such as GeoPandas and Shapely.

GISC 28200 (GISC 38200; ARCH 28402)
Spatial Analysis Methods in Geographic Information Systems
Crystal Bae / MWF 9:30–10:20
This course provides an overview of methods of spatial analysis and their implementation in geographic information systems. These methods deal with the retrieval, storage, manipulation and transformation of spatial data to create new knowledge. Examples are spatial join operations, spatial overlay, buffering, measuring accessibility, network analysis and raster operations. The fundamental principles behind the methods are covered as well as their application to real-life problems using open source software such as QGIS.

Electives

GISC 25900 (GISC 35900; CEGU 25900)
Introduction to Location Analysis
Yue Lin / T TH 9:30–10:50
Optimizing the location of facilities and services – agricultural, industrial, retail, and knowledge-based – has long been a focus for geographers, regional scientists, and urban planners. This course covers several foundational location problems in economic geography and urban planning, such as: covering problems, center problems, median problems, and fix charge facility location problems. This course incorporates several GIS exercises to teach students the basic principles of spatial optimization and to help illuminate the foundational theoretical principles of location modeling.

SOCI 20519 (GISC 20519; GISC 30519; ENST 20519; MACS 30519; SOCI 30519)
Spatial Cluster Analysis
Luc Anselin / W 12–2:50
This course provides an overview of methods to identify interesting patterns in geographic data, so-called spatial clusters. Cluster concepts come in many different forms and can generally be differentiated between the search for interesting locations and the grouping of similar locations. The first category consists of the identification of extreme concentrations of locations (events), such as hot spots of crime events, and the location of geographical concentrations of observations with similar values for one or more variables, such as areas with elevated disease incidence. The second group consists of the combination of spatial observations into larger (aggregate) areas such that internal similarity is maximized (regionalization).

The methods covered come from the fields of spatial statistics as well as machine learning (unsupervised learning) and operations research. Topics include point pattern analysis, spatial scan statistics, local spatial autocorrelation, dimension reduction, as well as spatially explicit hierarchical, agglomerative and density-based clustering. Applications range from criminology and public health to politics and marketing. An important aspect of the course is the analysis of actual data sets by means of open source software, such as GeoDa, R or Python.

SOCI 20595 (SOCI 30595)
Topics In Spatial Regression Analysis
Luc Anselin / M 12–2:50
This course covers methodological issues that affect the specification and estimation of spatial regression models. The course is organized as a seminar, with a combination of brief lectures, discussion of recent article and lab exercises. Topics will vary. Examples are spatial specification search, spatial effects in models for discrete dependent variables, spatial effects in count models, semi-parametric spatial models, spatial panel data models, spatial treatment effect analysis, spatial interaction models, endogenous regimes, regularization in spatial models, spatial feature engineering, and endogenous spatial weights. An important aspect of the course is the application of open source software tools, specifically those contained in the Python package PySAL. Prerequisites: An intermediate course in multivariate regression or econometrics. Familiarity with metrix algebra; SOCi 20559/30559 or equivalent is desired, but not required.

Independent Study Electives

Independent study courses count as electives in the GIS minor and require instructor consent for registration.

GISC 28700/38700
Readings in Spatial Analysis
This independent reading option is an opportunity to explore special topics in the exploration, visualization and statistical modeling of geospatial data. This course is consent-only. Students are required to submit the College Reading and Research Course Form. Available for either quality grades or for P/F grading.

GISC 29000 / 49000
Reading & Research: GISC
Independent study for graduate students interested in Geographic Information Sciences (GIS). Students and instructors can arrange a Reading/Research course when the material being studied goes beyond the scope of a particular course, when students are working on material not covered in an existing course, or when students would like to receive academic credit for independent research. Subject, course of study, and requirements must be arranged with the instructor.

Pre-Requisite Courses

GISC 28702/38702 (ARCH 28702; CEGU 28702; ENST 28702; SOCI 20283)
Intro to GIS & Spatial Analysis
Crystal Bae / T TH 12:30–1:50 
This course provides an introduction and overview of how spatial thinking is translated into specific methods to handle geographic information and the statistical analysis of such information. This is not a course to learn a specific GIS software program, but the goal is to learn how to think about spatial aspects of research questions, as they pertain to how the data are collected, organized and transformed, and how these spatial aspects affect statistical methods. The focus is on research questions relevant in the social sciences, which inspires the selection of the particular methods that are covered. Examples include spatial data integration (spatial join), transformations between different spatial scales (overlay), the computation of “spatial” variables (distance, buffer, shortest path), geovisualization, visual analytics, and the assessment of spatial autocorrelation (the lack of independence among spatial variables). The methods will be illustrated by means of open source software such as QGIS and R.

DATA 118000
Introduction to Data Science I
Data science provides tools for gaining insight into specific problems using data, through computation, statistics, and visualization. This course introduces students to all aspects of a data analysis process, from posing questions, designing data collection strategies, management+storing and processing of data, exploratory tools and visualization, statistical inference, prediction, interpretation, and communication of results. Simple techniques for data analysis are used to illustrate both effective and fallacious uses of data science tools. Although this course is designed to be at the level of general education in the mathematical sciences courses, with little background required, we expect the students to develop computational skills that will allow them to analyze data. Computation will be done using Python and Jupyter Notebook.

STAT 22000
Statistical Methods and Applications
This course introduces statistical techniques and methods of data analysis including the use of statistical software. Examples are drawn from the biological, physical, and social sciences. Students are required to apply the techniques discussed to data drawn from actual research. Topics include data description, graphical techniques, exploratory data analyses, random variation and sampling, basic probability, random variables and expected values, confidence intervals, and significance tests for one- and two-sample problems for means and proportions, chi-square tests, linear regression, and, if time permits, analysis of variance.

Core Methods

GISC 28400 (GISC 38400; CHST 28400)
GISCience Practicum
Yue Lin / M W 1:30–2:50
This applied course in geographic information science builds upon and refines knowledge and geocomputational expertise gained in the GIScience sequence. Students will develop a multifaceted GIS project incorporating spatial thinking in design, infrastructure, and implementation.

The 2024 Practicum will emphasize Urban renewal in the mid-20th century, specifically, Chicago during the 1960s. Students will conduct guided projects investigating the implications and legacies of urban renewal, utilizing spatial analysis methods such as network analysis, accessibility analysis, machine learning, and/or regression modeling. This course will feature guest lectures from organizations such as the Chicago History Museum and Preservation Chicago to introduce stories and backgrounds of urban renewal in the United States. Students will also have the opportunity to present their work at an event in collaboration with the Chicago History Museum.

Electives

GISC 27102 (GISC 37102; CEGU 27102; CHST 27102; ENST 28722)
Spatial Cognition
Crystal Bae / T Th 9:30–10:50
This course serves as an overview of spatial cognition and environmental perception, which relates to all aspects of spatial thinking, spatial behavior, and human-environment interaction in spatial and social contexts. Topics of study include cognitive maps and wayfinding behavior, spatial and environmental learning, spatial choice and decision-making, migration and travel, time geography, place and regional identity, and the role of gender and culture in spatial cognition.

GISC 27107 (GISC 37107)
Spatial Reasoning & Pitfalls
Robert Shephard / T Th 3:30–4:50

Independent Study Electives

GISC 28700/38700
Readings in Spatial Analysis
This independent reading option is an opportunity to explore special topics in the exploration, visualization and statistical modeling of geospatial data. This course is consent-only. Students are required to submit the College Reading and Research Course Form. Available for either quality grades or for P/F grading.

GISC 29000 / 49000
Reading & Research: GISC
Independent study for graduate students interested in Geographic Information Sciences (GIS). Students and instructors can arrange a Reading/Research course when the material being studied goes beyond the scope of a particular course, when students are working on material not covered in an existing course, or when students would like to receive academic credit for independent research. Subject, course of study, and requirements must be arranged with the instructor.