Blended Course Map: ECON 690 — GIS Analysis and Big Data
Blended Learning
This blended course map was created by a participant of the Blend@UW Course Design Series. It represents an example of how activities could be designed for one unit of a course to achieve the course and unit outcomes.
Example of a blended course map from ECON 690 — GIS Analysis and Big Data
Name: Matthew Friedman
SCID: College of Letters & Science
Department: Economics
Course Name: ECON 690 — GIS Analysis and Big Data
Supported Program Outcomes:
 SPO1: Construct and evaluate economic models, their assumptions, and their conclusions.
 SPO2:Acquire a diverse set of skills and strategies in mathematical reasoning/statistical and computational techniques/deductive logic/problemsolving.
 SPO3: Use mathematics/computational/statistical techniques to analyze realworld situations and policies.
Course Objectives:
 CO1:Acquire knowledge and interest in approaches to economic analysis that leverage large collections of [geospatially detailed] data
 CO2: Draw connections between the most innovative products and services on the market today and the big data behind them
 CO3:Understand how GIS technology can aid critical evaluation of economic outcomes and policies
 CO4: Develop competence in compiling, combining, manipulating, and evaluating large [geospatial] datasets
 CO5:Obtain project management experience by working collaboratively with peers to complete a diverse set of highly specialized programming and analysis tasks
 CO6:Value economic models, their assumptions, and conclusions. Students will also relate theoretical models to realworld data sources in order to support or invalidate their conclusions through empirical testing
 CO7: Engage with communities of professionals and other resources that can help further their big data wrangling skills long after successfully completing ECON690
Course Units:
 CU1: Introduction to Geospatial Information Systems (Practice)
 CU2: Using GIS to conduct economic analysis (Place)
 CU3: Basic programming tools for unlocking Big Data’s insights (Programming)
 CU4: Big Data Rent: How profit maximizers leverage information (Prices)
 CU5: Leveraging Big Data to measure preferences and predict trends (Prediction)
 CU6: Evaluating, Visualizing, and Presenting Big Data Effectively (Presentations & Pratfalls)
Unit Being Redesigned:
PLACE
Unit Objectives:
 UO1: Understand how economists are utilizing GIS data to more accurately describe human interactions
 UO2: Appreciate how spatial detail can enhance traditional economics analysis
 UO3: Classify geospatial tools, identify their effective uses, and outline how each tool can be used to generate new data
 UO4: Deconstruct the price of a composite good into the value attributable to each of its constituent parts
 UO5: Summarize how GIS applications enhance economic efficiency by matching trading partners in space
 UO6: Conduct basic spatial operations to generate new variables
PreClass Activities  InClass Activities  PostClass Activities 



