https://github.com/musa-zhanchao/ppa_assignment
Assignment Repo for PPA
https://github.com/musa-zhanchao/ppa_assignment
public-policy-analysts rmd
Last synced: about 2 months ago
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Assignment Repo for PPA
- Host: GitHub
- URL: https://github.com/musa-zhanchao/ppa_assignment
- Owner: MUSA-Zhanchao
- License: bsd-2-clause
- Created: 2025-02-01T17:24:49.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-08-11T17:39:21.000Z (about 2 months ago)
- Last Synced: 2025-08-11T19:36:42.905Z (about 2 months ago)
- Topics: public-policy-analysts, rmd
- Language: R
- Homepage: https://musa-zhanchao.github.io/MUSA5080showcase/
- Size: 77.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MUSA508 PPA Assignment - Spring 2025
Weitzman School of Design, University of Pennsylvania
Department of City Planning
Master of City Planning Program
Master of Urban Spatial Analytics Program
Instuctor: Dr. Elizabeth Delmelle
Creator/Student: Zhanchao Yang
## Course Description:
This course teaches advanced spatial analysis and an introduction to data
science/machine learning in the urban planning and public policy realm. The class focuses on real-world
spatial analysis applications and, in combination with introductory machine learning, provide students with a
modern framework for efficiently allocating limited resources across space. Unlike its private sector
counterpart, data science in the public or non-profit sector isn't strictly about optimization - it requires the
understanding of public goods, governance, and issues of equity. We explore use cases in transportation,
housing, public health, land use, criminal justice, and other domains. We will learn novel approaches for
understanding and avoiding risks of "algorithmic bias" against communities/people of color as well as
communities of different income levels.
## Course Structure
This course will generally follow a lecture-lab format to break up the 3-hour block. We will
begin with some conceptual and theoretical background on the week’s topic and then transition to hands-on
coding practice. Please bring a (charged) laptop to class to participate in the lab section. I would very much
appreciate it if you dedicate the brief 3-hour timeslot we have together each week to matters concerning this
class alone. Working on assignments for your other courses is a distraction to everyone.
## Learning Outcomes
By the end of the semester, students should:
- Understand how to build a predictive model for public policy decision-making applications.
- Effectively evaluate the effectiveness, generalizability, and biases of models.
- Be proficient in the data science workflow – data wrangling, exploration, modeling, and communication.
- Understand how to incorporate spatial variables from various sources into predictive models.
## Course Materials:
- Steif, Ken “Public Policy Analytics”: https://urbanspatial.github.io/PublicPolicyAnalytics/
- Wickham et al. “R for Data Science”: https://r4ds.hadley.nz/
- Lovelace et al., “Geocomputation with R”: https://r.geocompx.org/
- Walker, Kyle “Analyzing US Census Data: https://walker-data.com/census-r/
## Assessment:
- **Homework Assignments** – There will be 5 homework assignments throughout the semester. Most will be due 2 weeks after they are assigned (except the first one, which is shorter). The work for this course builds on skills taught previously so once you fall behind, it is increasingly difficult to catch up. These will be turned in individually and written up individually.
- **Occasional Low-Stakes Quiz** – These are to ensure that the core concepts I want you to know, you know. They also help me determine what needs to be taught better. They are very low-stakes, and you receive 1 point for doing it and 0 for not doing it (they must be submitted on time). Because you are not graded on how many you got correct, please don’t cheat (a.d.k. ask AI the answer)!! This is your opportunity to let me know you don’t understand something and for me to then try to explain it better.
- **Midterm & Final Projects** – There will be an applied, larger midterm and final project. Both will be group-based.