UP 494

Fall 2021 All Classes

All Classes

Special Topics in Planning

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SHOW FINAL EXAM SCHEDULE

Credit: 1 TO 6 hours.

Seminar on topics of current interest, as announced in the Schedule.

1 to 6 undergraduate hours. 1 to 6 graduate hours. May be repeated to a maximum of 16 hours.

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UP 494 class schedule data for fall 2021
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	59771	Lecture	AG	9:30AM -10:50AM	TR	Campus Instructional Facility	Dastrup, E Greenlee, A	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Neighborhood Analysis Section Info: Survey of methods for analyzing demographic, economic, physical, and social conditions at the neighborhood and local government scale. Analytical techniques cover descriptive approaches, projection of future trends, and basic mapping and spatial analysis techniques. Treatment of how analysis guides local deliberative processes and decision-making, and training in methods for engaging public, government, and private stakeholders in conversations regarding scenarios derived from analysis. Restriction(s): Restricted to students in the Urban & Regional Planning department. Restricted to Graduate - Urbana-Champaign.
	45681	Laboratory Online	FF FF	3:30PM -4:50PM 3:30PM -4:50PM	R T	Temple Hoyne Buell Hall n.a.	Fang, F Sohoni, S Fang, F Sohoni, S	Part of Term: 1 Date Range: 08/23/21-12/08/21 Credit: 4 hours Section Title: Adv Data Science for Planners Section Info: For professional planners, knowledge of philosophy, problem resolution, and policymaking are required. In fact, planners need to deal with different datasets relate to cultural, social, and economic characteristics of human settlement. Big data analysis is now playing a major role for us planners to discover urban knowledge and further support our planning strategy. This R-based advanced data science course is designed for upper-level undergrads, who are already equipped with necessary planning theories and knowledge. We will introduce both basic data processing techniques in R and project-oriented topics toward social science e.g. spatial analysis, statistical modeling, and text analytics. It will also contribute to students' work especially capstone/thesis/dissertation.