# gboeing/ppde642

USC urban data science course series in Python

Repository: https://github.com/gboeing/ppde642
Canonical: https://ross.abutalabs.com/products/ppde642
Homepage: https://geoffboeing.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: usc, urban-data-science, course-materials, data-science, urban-planning, urban-analytics, urban-informatics, city-government, syllabus, jupyter, python, statistics, network-analysis, spatial-analysis, urbanism, cities, course, coding, transport, transportation
Last push: 2026-07-21T20:20:54+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 93, release rhythm 35, longevity 100
- inputs: {"age_days": 4019, "days_push": 43, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1320, forks 485 (observed 2026-08-28T04:04:21.684526+00:00)

## What it is
PPDE642 is the second course in a two-course USC urban data science series, providing Jupyter notebook course materials on spatial analysis, network analysis, spatial models, and applied machine learning in Python. It is freely available on GitHub and runnable interactively in the cloud via Binder.

## Use cases
- learn urban data science with python
- self-study spatial analysis course materials
- teach a course on urban analytics
- learn network analysis of street networks
- introduction to applied machine learning for urban data
- run course notebooks in the cloud with binder

## When to choose
- you want free, hands-on Jupyter-based course materials for urban data science
- you are self-studying spatial analysis, network analysis, or urban machine learning
- you teach a course and want a ready-made syllabus and notebooks

## When to avoid
- you need production software rather than educational materials
- you want a general-purpose data science course with no spatial/urban focus
- you need a maintained library with an API

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, geospatial, math
- domain: education, data-science, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, course-materials, urban-planning, spatial-analysis, network-analysis, statistics, transportation, binder, syllabus, maps, web-server

## Member repositories
- gboeing/ppde642 (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:21.684526+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:47:27.435998+00:00, confidence not recorded.
  - readme: https://github.com/gboeing/ppde642 (fetched 2026-08-28T04:04:21.684526+00:00, sha b0e0f53086da)
  - homepage: https://geoffboeing.com (fetched 2026-08-29T12:06:22.467321+00:00, sha 601c0eae5358)
- Data as of 2026-08-30T08:39:29.467469+00:00.