GIScience Minor

GEOG 28702/GEOG 38702 (ARCH 28702; ENST 28702; SOCI 20283; SOCI 30283)
Introduction to GIS & Spatial Analysis
Marynia Kolak
This course provides an overview of how spatial thinking is translated into specific methods to handle geographic information and statistical analysis, with a focus on research questions relevant in the social sciences. Basics of cartography, spatial data wrangling, and the essential elements of spatial analysis are introduced within this context. 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; this course does not teach a specific GIS software program.


GEOG Majors

ARTH 24190 (GEOG 24190; AMER 24190; ARCH 24190; ARTV 20210; ENST 24190)
Imagining Chicago's Common Buildings
Luc Joyner
This course is an architectural studio based in the common residential buildings of Chicago and the city's built environment. While design projects and architectural skills will be the focus of the course, it will also incorporate readings, a small amount of writing, some social and geographical history, and several explorations around Chicago. The studio will: (1) give students interested in pursuing architecture or the study of cities experience with a studio course and some skills related to architectural thinking, (2) acquaint students intimately with Chicago's common residential buildings and built fabric, and (3) situate all this within a context of social thought about residential architecture, common buildings, housing, and the city. This course is part of the College Course Cluster program: Urban Design.

 

GEOG 28702/GEOG 38702 (ARCH 28702; ENST 28702; SOCI 20283; SOCI 30283)
Introduction to GIS & Spatial Analysis
Marynia Kolak
This course provides an overview of how spatial thinking is translated into specific methods to handle geographic information and statistical analysis, with a focus on research questions relevant in the social sciences. Basics of cartography, spatial data wrangling, and the essential elements of spatial analysis are introduced within this context. 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; this course does not teach a specific GIS software program.